instruction
stringclasses 45
values | integer
sequencelengths 168
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| output
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|---|---|---|
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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] | {
"1. Frame Length": "The total frame length is: 525.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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] | {
"1. Frame Length": null,
"2. Local Maxima": {
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[
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[
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]
},
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": null,
"3. Local Minima": {
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}
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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"1. Frame Length": "The total frame length is: 396.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": {
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"3. Local Minima": null
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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[
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}
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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"1. Frame Length": "The total frame length is: 296.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": {
"frames": [
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"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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"1. Frame Length": "The total frame length is: 400.",
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"3. Local Minima": null
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"3. Local Minima": null
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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"2. Local Maxima": null,
"3. Local Minima": {
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}
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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"1. Frame Length": "The total frame length is: 513.",
"2. Local Maxima": null,
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": {
"frames": [
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},
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": null,
"3. Local Minima": {
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}
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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"1. Frame Length": "The total frame length is: 342.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": {
"frames": [
[
0,
341
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]
},
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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"1. Frame Length": "The total frame length is: 432.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"2. Local Maxima": {
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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] | {
"1. Frame Length": null,
"2. Local Maxima": null,
"3. Local Minima": {
"frames": [
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]
]
}
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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] | {
"1. Frame Length": "The total frame length is: 1033.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": {
"frames": [
[
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[
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[
843,
1032
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]
},
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": null,
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[
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[
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[
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[
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]
}
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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"1. Frame Length": "The total frame length is: 475.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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] | {
"1. Frame Length": null,
"2. Local Maxima": {
"frames": [
[
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]
},
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": null,
"3. Local Minima": {
"frames": [
[
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]
]
}
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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"1. Frame Length": "The total frame length is: 364.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": {
"frames": [
[
0,
136
],
[
193,
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]
]
},
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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"2. Local Maxima": null,
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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"1. Frame Length": "The total frame length is: 442.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": {
"frames": [
[
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},
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": null,
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[
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]
}
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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] | {
"1. Frame Length": "The total frame length is: 503.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": {
"frames": [
[
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[
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]
]
},
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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] | {
"1. Frame Length": null,
"2. Local Maxima": null,
"3. Local Minima": {
"frames": [
[
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]
}
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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] | {
"1. Frame Length": "The total frame length is: 884.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": {
"frames": [
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[
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[
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[
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]
},
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": null,
"3. Local Minima": {
"frames": [
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]
}
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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] | {
"1. Frame Length": "The total frame length is: 423.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": {
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[
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},
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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] | {
"1. Frame Length": null,
"2. Local Maxima": null,
"3. Local Minima": {
"frames": [
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}
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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"1. Frame Length": "The total frame length is: 469.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": {
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"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": null,
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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"1. Frame Length": "The total frame length is: 586.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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},
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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"3. Local Minima": {
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}
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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"1. Frame Length": "The total frame length is: 399.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": {
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},
"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": null,
"3. Local Minima": {
"frames": [
[
240,
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],
[
371,
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]
]
}
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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"1. Frame Length": "The total frame length is: 401.",
"2. Local Maxima": null,
"3. Local Minima": null
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"3. Local Minima": null
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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"1. Frame Length": null,
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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"1. Frame Length": "The total frame length is: 338.",
"2. Local Maxima": null,
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CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"3. Local Minima": null
} |
CoM is the Center of Mass. The closer to the max value, the more stable the posture, and the closer to the min value, the more unstable the posture.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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"1. Frame Length": "The total frame length is: 525.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"1. Frame Length": null,
"2. Local Maxima": {
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[
189,
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[
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[
480,
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]
},
"3. Local Minima": null
} |
Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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] | {
"1. Frame Length": "The total frame length is: 513.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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] | {
"1. Frame Length": null,
"2. Local Maxima": {
"frames": [
[
133,
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]
]
},
"3. Local Minima": null
} |
Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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] | {
"1. Frame Length": null,
"2. Local Maxima": null,
"3. Local Minima": {
"frames": [
[
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[
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]
]
}
} |
Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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] | {
"1. Frame Length": "The total frame length is: 342.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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"1. Frame Length": "The total frame length is: 432.",
"2. Local Maxima": null,
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Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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[
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]
},
"3. Local Minima": null
} |
Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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[
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}
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Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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] | {
"1. Frame Length": "The total frame length is: 1033.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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] | {
"1. Frame Length": null,
"2. Local Maxima": {
"frames": [
[
670,
703
],
[
764,
824
],
[
904,
934
]
]
},
"3. Local Minima": null
} |
Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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}
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Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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"1. Frame Length": "The total frame length is: 475.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local maxima, we identify the ranges where the values reach a peak within the dataset. A local maximum is a point where the value is higher than its neighboring values. This indicates periods of high activity or dynamic movement. The detection is based on a threshold percentage (e.g., 80% of the maximum value) to focus on the most significant peaks. The output lists the frame ranges where these peaks occur and their respective values. | [
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"2. Local Maxima": {
"frames": [
[
242,
409
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},
"3. Local Minima": null
} |
Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To find the local minima, we identify the ranges where the values reach a low point within the dataset. A local minimum is a point where the value is lower than its neighboring values. This indicates periods of less activity or reduced movement. The detection is based on a threshold percentage (e.g., 20% above the minimum value) to focus on the most significant dips. The output lists the frame ranges where these dips occur and their respective values. | [
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"2. Local Maxima": null,
"3. Local Minima": {
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]
}
} |
Symmetry represents the body is divided into left and right sides, and the similarity between the symmetrical joints is calculated. Near the maximum value, the less symmetry there is between the left and right sides of the body with moving only one side of arm or leg, and near the minimum value, the more symmetry there is between the left and right sides of the body.
To calculate the frame length, count the total number of frames in the dataset. Each frame represents a data point collected over time. The total frame length gives the duration of the motion or activity being analyzed. In this dataset, the total frame length is determined by the number of entries in the data array. | [
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29
] | {
"1. Frame Length": "The total frame length is: 364.",
"2. Local Maxima": null,
"3. Local Minima": null
} |
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