Department of Electrical Engineering, National United University, Miaoli 36003, Taiwan.
Department of Information Management, National United University, Miaoli 36003, Taiwan.
Sensors (Basel). 2024 Mar 18;24(6):1935. doi: 10.3390/s24061935.
Lower extremity exercises are considered a standard and necessary treatment for rehabilitation and a well-rounded fitness routine, which builds strength, flexibility, and balance. The efficacy of rehabilitation programs hinges on meticulous monitoring of both adherence to home exercise routines and the quality of performance. However, in a home environment, patients often tend to inaccurately report the number of exercises performed and overlook the correctness of their rehabilitation motions, lacking quantifiable and systematic standards, thus impeding the recovery process. To address these challenges, there is a crucial need for a lightweight, unbiased, cost-effective, and objective wearable motion capture (Mocap) system designed for monitoring and evaluating home-based rehabilitation/fitness programs. This paper focuses on the development of such a system to gather exercise data into usable metrics. Five radio frequency (RF) inertial measurement unit (IMU) devices (RF-IMUs) were developed and strategically placed on calves, thighs, and abdomens. A two-layer long short-term memory (LSTM) model was used for fitness activity recognition (FAR) with an average accuracy of 97.4%. An intelligent smartphone algorithm was developed to track motion, recognize activity, and calculate key exercise variables in real time for squat, high knees, and lunge exercises. Additionally, a 3D avatar on the smartphone App allows users to observe and track their progress in real time or by replaying their exercise motions. A dynamic time warping (DTW) algorithm was also integrated into the system for scoring the similarity in two motions. The system's adaptability shows promise for applications in medical rehabilitation and sports.
下肢运动被认为是康复和全面健身的标准和必要治疗方法，可增强力量、灵活性和平衡能力。康复计划的疗效取决于对家庭运动常规的坚持程度和执行质量的细致监测。然而，在家庭环境中，患者通常倾向于不准确地报告所执行的运动次数，并忽略康复动作的正确性，缺乏可量化和系统的标准，从而阻碍康复过程。为了解决这些挑战，迫切需要一种轻便、无偏、经济高效且客观的可穿戴运动捕捉（Mocap）系统，用于监测和评估基于家庭的康复/健身计划。本文重点介绍了这种系统的开发，以将运动数据收集到可用的指标中。开发了五个射频（RF）惯性测量单元（IMU）设备（RF-IMU），并战略性地放置在小腿、大腿和腹部。使用两层长短时记忆（LSTM）模型进行健身活动识别（FAR），平均准确率为 97.4%。开发了一种智能智能手机算法，用于实时跟踪运动、识别活动和计算深蹲、高膝和弓步运动的关键运动变量。此外，智能手机应用程序上的 3D 头像允许用户实时观察和跟踪他们的进度，或重播他们的运动动作。该系统还集成了动态时间规整（DTW）算法，用于对两个动作的相似度进行评分。该系统的适应性有望在医疗康复和运动领域得到应用。
