Machine learning project in computer vision technique for the viability of quantifying the treatment for stroke patients a deep neural network approach

Training in limb recuperation speeds up the retrieval process and enhances the quality of life for stroke patients with hemiparalysis. Both doctors and patients must be aware of the patient's progress in recuperation.

The computer vision method, which can identify a patient's training action, movement trajectory, and activity status, can be used to monitor recuperation more precisely and effectively than wearable sensors or deep cameras. In the clinic, it is difficult to quantify the dynamic change of different training sessions to assess the progress of the recuperation, with the exception of static measures of real-time behaviour.

In this case study, we suggested a computational method to compare the upper limb's motion change. The upper limb joint points were first identified using Open Pose to pre-process the video data, and the positions of each joint point were then specified using Cartesian coordinates. Second, in order to determine the recuperation progress, we computed the similarity of the limb's lift angle and time in various training periods using the dynamic time warping algorithm.

The outcomes demonstrate that our approach can measure data and analyse the effectiveness of rehabilitative actions using a basic camera, which has a great potential for future diagnosis.