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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.12921 |
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| _version_ | 1866916694543302656 |
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| author | Homm, Daniel Carqueville, Patrick Eichhorn, Christian Weikert, Thomas Menard, Thomas Plecher, David A. Easthope, Chris Awai |
| author_facet | Homm, Daniel Carqueville, Patrick Eichhorn, Christian Weikert, Thomas Menard, Thomas Plecher, David A. Easthope, Chris Awai |
| contents | This study explores the potential of using wrist-worn inertial sensors to automate the labeling of ARAT (Action Research Arm Test) items. While the ARAT is commonly used to assess upper limb motor function, its limitations include subjectivity and time consumption of clinical staff. By using IMU (Inertial Measurement Unit) sensors and MiniROCKET as a time series classification technique, this investigation aims to classify ARAT items based on sensor recordings. We test common preprocessing strategies to efficiently leverage included information in the data. Afterward, we use the best preprocessing to improve the classification. The dataset includes recordings of 45 participants performing various ARAT items. Results show that MiniROCKET offers a fast and reliable approach for classifying ARAT domains, although challenges remain in distinguishing between individual resembling items. Future work may involve improving classification through more advanced machine-learning models and data enhancements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12921 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IdentiARAT: Toward Automated Identification of Individual ARAT Items from Wearable Sensors Homm, Daniel Carqueville, Patrick Eichhorn, Christian Weikert, Thomas Menard, Thomas Plecher, David A. Easthope, Chris Awai Machine Learning This study explores the potential of using wrist-worn inertial sensors to automate the labeling of ARAT (Action Research Arm Test) items. While the ARAT is commonly used to assess upper limb motor function, its limitations include subjectivity and time consumption of clinical staff. By using IMU (Inertial Measurement Unit) sensors and MiniROCKET as a time series classification technique, this investigation aims to classify ARAT items based on sensor recordings. We test common preprocessing strategies to efficiently leverage included information in the data. Afterward, we use the best preprocessing to improve the classification. The dataset includes recordings of 45 participants performing various ARAT items. Results show that MiniROCKET offers a fast and reliable approach for classifying ARAT domains, although challenges remain in distinguishing between individual resembling items. Future work may involve improving classification through more advanced machine-learning models and data enhancements. |
| title | IdentiARAT: Toward Automated Identification of Individual ARAT Items from Wearable Sensors |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2504.12921 |