用可穿戴传感器自动识别手部功能测试动作，提升评估效率
IdentiARAT: Toward Automated Identification of Individual ARAT Items from Wearable Sensors
- 用惯性传感器采集动作数据，结合快速分类模型识别测试项目
- 在45人数据上实现高准确率的测试项分类，相似动作仍存混淆
- 适合康复医学、智能诊断系统研发人员参考
本研究探索使用腕部惯性传感器自动化标记ARAT（动作研究上肢测试）项目。尽管ARAT常用于评估上肢运动功能，但其存在临床人员主观判断和耗时的问题。通过使用IMU（惯性测量单元）传感器与MiniROCKET时间序列分类技术，本研究旨在基于传感器记录对ARAT项目进行分类。我们测试了多种预处理策略以高效利用数据信息，并采用最优预处理方案提升分类性能。数据集包含45名参与者完成各类ARAT项目的记录。结果表明，MiniROCKET提供了一种快速且可靠的分类方法，但在区分相似动作项目方面仍面临挑战。未来工作可借助更先进的机器学习模型和数据增强手段进一步优化分类效果。
原文摘要 · Abstract (English)
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.
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