Home-based physical therapies are effective if the prescribed exercises are correctly executed and patients adhere to these routines. This is specially important for older adults who can easily forget the guidelines from therapists. Inertial Measurement Units (IMUs) are commonly used for tracking exercise execution giving information of patients' motion data. In this work, we propose the use of Machine Learning techniques to recognize which exercise is being carried out and to assess if the recognized exercise is properly executed by using data from four IMUs placed on the person limbs. To the best of our knowledge, both tasks have never been addressed together as a unique complex task before. However, their combination is needed for the complete characterization of the performance of physical therapies. We evaluate the performance of six machine learning classifiers in three contexts: recognition and evaluation in a single classifier, recognition of correct exercises, excluding the wrongly performed exercises, and a two-stage approach that first recognizes the exercise and then evaluates it. We apply our proposal to a set of 8 exercises of the upper-and lower-limbs designed for maintaining elderly people health status. To do so, the motion of volunteers were monitored with 4 IMUs. We obtain accuracies of 88.4 \% and the 91.4 \% in the two initial scenarios. In the third one, the recognition provides an accuracy of 96.2 \%, whereas the exercise evaluation varies between 93.6 \% and 100.0 \%. This work proves the feasibility of IMUs for a complete monitoring of physical therapies in which we can get information of which exercise is being performed and its quality, as a basis for designing virtual coaches.

翻译：居家物理治疗的有效性取决于患者能否正确执行预设动作并坚持训练计划，这对易遗忘治疗师指导的老年群体尤为重要。惯性测量单元（IMUs）常用于追踪动作执行情况，可提供患者运动数据。本研究提出利用机器学习技术，通过固定在人体四肢的四个IMU传感器数据，识别当前执行动作并评估其规范性。据我们所知，这两项任务此前从未被作为统一复杂任务协同处理，然而其整合对于完整表征物理治疗效果至关重要。我们在三种场景下评估了六种机器学习分类器的性能：单分类器联合执行识别与评估、仅对正确动作进行识别（排除错误动作）、以及先识别后评估的两阶段方法。我们将方案应用于8组针对老年健康维护设计的上/下肢训练动作，通过4个IMU监测志愿者运动数据。前两种场景分别获得88.4%和91.4%的准确率。第三种场景中，动作识别准确率达96.2%，而动作评估准确率在93.6%至100.0%之间浮动。本研究证实了利用IMUs实现物理治疗全过程监测的可行性，可同时获取动作类型与执行质量信息，为设计虚拟教练系统奠定基础。