A Video-based Physiotherapy Exercise Dataset

A patient recovering from a stroke, injury, or physical pain needs continuous physiotherapy and rehabilitation to achieve a quick and complete recovery. It is often difficult for elderly people to visit clinics to undertake exercises. Finding physiotherapists and relevant treatments becomes more difficult, particularly in an epidemic condition like covid-19. AI-driven at-home physiotherapy exercise monitoring and assessment systems can be the straightforward feasible solution in this regard. Accurate recognition of particular exercises, exercise assessments, providing feedback, etc. are parts of the whole system, which a machine typically learns through a data-heavy training process. A key issue in this regard is the lack of specific training data for physiotherapy exercises. There exist only a few datasets in the literature that are designed for physiotherapy exercises; most of them however are based on multiple body sensors or Kinect device. Sensor devices are quite costly, and their availability is not guaranteed everywhere. In contrast, video data can be a better alternative, where video can be acquired easily from an available smartphone camera or desktop/laptop webcam. Addressing this issue, we present a new video-based physiotherapy exercise database containing 1237 video clips of 14 physiotherapy exercises that were carefully elicited from an extensively conducted survey from multiple physiotherapists. Exercises were recorded with 28 male and female subjects within various lighting conditions, camera angles, and camera jitters to simulate the real-world setting. Several machine learning algorithms were utilized to carry out an experimental study on the dataset, and the results are provided for future reference.