A stroke is a medical condition caused by a disruption in blood flow to the brain. This can lead to difficulties with everyday activities and movement. Music therapy is a promising new alternative to traditional rehabilitation methods. This therapy uses sound’s natural properties to enhance stroke recovery, improve motor skills and stimulate neural plasticity. This approach motivates people on both a physical and emotional level. Software tools developed to date to aid in motor recovery after stroke rely mainly on external mechanisms and specific hardware components. This limitation restricts the potential scope of these tools. This study aims to examine the effectiveness and mechanisms of using a mobile application with machine learning algorithms and music therapy principles as a complementary intervention for post-stroke motor recovery. This research project has resulted in the development of a mobile app, based on the widely used Fugl Meyer Assessment. The application uses Vision Framework from Apple and a custom Activity Classification CoreML machine learning model to detect an individual's position in a seated posture. It has also been integrated with XCode. The application generates an audio cue when a user successfully completes one of the Fugl-Meyer Assessment activities. To train the model, 340 clips of a variety of exercises have been created. The research sheds light on how this technology can be used to transform neurorehabilitation while also helping to develop accessible and convenient tools that promote stroke motor recovery
