Haghighi Osgouei Reza, Soulsby David, Bello Fernando
Imperial College Centre for Engagement and Simulation Science (ICCESS), Faculty of Medicine, Department of Surgery and Cancer, Imperial College London, London, United Kingdom.
Children's Services and Dietetics, Chelsea and Westminster Hospital, London, United Kingdom.
JMIR Rehabil Assist Technol. 2020 Aug 18;7(2):e17289. doi: 10.2196/17289.
Performing physiotherapy exercises in front of a physiotherapist yields qualitative assessment notes and immediate feedback. However, practicing the exercises at home lacks feedback on how well patients are performing the prescribed tasks. The absence of proper feedback might result in patients performing the exercises incorrectly, which could worsen their condition. We present an approach to generate performance scores to enable tracking the progress by both the patient at home and the physiotherapist in the clinic.
This study aims to propose the use of 2 machine learning algorithms, dynamic time warping (DTW) and hidden Markov model (HMM), to quantitatively assess the patient's performance with respect to a reference.
Movement data were recorded using a motion sensor (Kinect V2), capable of detecting 25 joints in the human skeleton model, and were compared with those of a reference. A total of 16 participants were recruited to perform 4 different exercises: shoulder abduction, hip abduction, lunge, and sit-to-stand exercises. Their performance was compared with that of a physiotherapist as a reference.
Both algorithms showed a similar trend in assessing participant performance. However, their sensitivity levels were different. Although DTW was more sensitive to small changes, HMM captured a general view of the performance, being less sensitive to the details.
The chosen algorithms demonstrated their capacity to objectively assess the performance of physical therapy. HMM may be more suitable in the early stages of a physiotherapy program to capture and report general performance, whereas DTW could be used later to focus on the details. The scores enable the patient to monitor their daily performance. They can also be reported back to the physiotherapist to track and assess patient progress, provide feedback, and adjust the exercise program if needed.
在物理治疗师面前进行物理治疗练习可产生定性评估记录并获得即时反馈。然而，在家中进行练习则缺乏关于患者执行规定任务情况的反馈。缺乏适当的反馈可能导致患者错误地进行练习，从而使病情恶化。我们提出一种生成表现分数的方法，以便患者在家中以及诊所的物理治疗师都能跟踪进展情况。
本研究旨在提出使用两种机器学习算法，即动态时间规整（DTW）和隐马尔可夫模型（HMM），来定量评估患者相对于参考标准的表现。
使用能够检测人体骨骼模型中25个关节的运动传感器（Kinect V2）记录运动数据，并与参考数据进行比较。总共招募了16名参与者来执行4种不同的练习：肩部外展、髋部外展、弓步和从坐姿到站立的练习。将他们的表现与作为参考的物理治疗师的表现进行比较。
两种算法在评估参与者表现方面呈现出相似的趋势。然而，它们的敏感度水平不同。虽然DTW对小变化更敏感，但HMM捕捉到了表现的总体情况，对细节不太敏感。
所选算法展示了客观评估物理治疗表现的能力。HMM可能在物理治疗计划的早期阶段更适合捕捉和报告总体表现，而DTW可在后期用于关注细节。这些分数使患者能够监测自己的日常表现。它们还可以反馈给物理治疗师，以跟踪和评估患者的进展情况、提供反馈，并在需要时调整练习计划。
