Sensor Data Augmentation from Skeleton Pose Sequences for Improving Human Activity Recognition
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911930852048896 |
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| author | Zolfaghari, Parham Rey, Vitor Fortes Ray, Lala Kim, Hyun Suh, Sungho Lukowicz, Paul |
| author_facet | Zolfaghari, Parham Rey, Vitor Fortes Ray, Lala Kim, Hyun Suh, Sungho Lukowicz, Paul |
| contents | The proliferation of deep learning has significantly advanced various fields, yet Human Activity Recognition (HAR) has not fully capitalized on these developments, primarily due to the scarcity of labeled datasets. Despite the integration of advanced Inertial Measurement Units (IMUs) in ubiquitous wearable devices like smartwatches and fitness trackers, which offer self-labeled activity data from users, the volume of labeled data remains insufficient compared to domains where deep learning has achieved remarkable success. Addressing this gap, in this paper, we propose a novel approach to improve wearable sensor-based HAR by introducing a pose-to-sensor network model that generates sensor data directly from 3D skeleton pose sequences. our method simultaneously trains the pose-to-sensor network and a human activity classifier, optimizing both data reconstruction and activity recognition. Our contributions include the integration of simultaneous training, direct pose-to-sensor generation, and a comprehensive evaluation on the MM-Fit dataset. Experimental results demonstrate the superiority of our framework with significant performance improvements over baseline methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_16886 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sensor Data Augmentation from Skeleton Pose Sequences for Improving Human Activity Recognition Zolfaghari, Parham Rey, Vitor Fortes Ray, Lala Kim, Hyun Suh, Sungho Lukowicz, Paul Signal Processing Computer Vision and Pattern Recognition Machine Learning The proliferation of deep learning has significantly advanced various fields, yet Human Activity Recognition (HAR) has not fully capitalized on these developments, primarily due to the scarcity of labeled datasets. Despite the integration of advanced Inertial Measurement Units (IMUs) in ubiquitous wearable devices like smartwatches and fitness trackers, which offer self-labeled activity data from users, the volume of labeled data remains insufficient compared to domains where deep learning has achieved remarkable success. Addressing this gap, in this paper, we propose a novel approach to improve wearable sensor-based HAR by introducing a pose-to-sensor network model that generates sensor data directly from 3D skeleton pose sequences. our method simultaneously trains the pose-to-sensor network and a human activity classifier, optimizing both data reconstruction and activity recognition. Our contributions include the integration of simultaneous training, direct pose-to-sensor generation, and a comprehensive evaluation on the MM-Fit dataset. Experimental results demonstrate the superiority of our framework with significant performance improvements over baseline methods. |
| title | Sensor Data Augmentation from Skeleton Pose Sequences for Improving Human Activity Recognition |
| topic | Signal Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2406.16886 |