Identifying Movement Health Problems by Using Depth Camera, Pose Landmark Detection and Machine Learning

To identify human movement health problems effi-ciently and comfortably, we set up a 6-minute Timed Up and Go (TUG) test field in a hospital and used a depth camera to record the movement behavior of 60 patients during their tests. With the use of a pose landmark detection tool named MediaPipe on these recorded videos, we obtained the screen coordinates of 33 key body locations of a human body during each 6-minute TUG test. During each test, a physical therapist used her expertise to assess whether the patient had some specific symptoms. In this work, we propose a novel data processing method to more effectively use this 60-patient small data set to train machine learning models, which are later used to predict whether an individual has a specific symptom. Experimental results show that, compared to the traditional data processing method, our data processing method combined with the majority-voting prediction method significantly increases the prediction accuracy of four studied machine learning models.