Exploratory study to use K-means clustering for gesture selection of force myography upper limb data in participants with cerebral palsy

Many with Cerebral Palsy (CP) use assistive devices to perform daily activities. A gesture recognition based wearable device can be implemented using force myography (FMG). However, little research has been done regarding gestures to use with populations that exhibit physical disturbances associated with CP. The research presented in this Thesis lays the groundwork for implementing k-means clustering to conduct gesture selection for a FMG wearable device in a clinical setting. The concept was tested with ten healthy participants and then validated in a pilot study with a CP participant. The results from both population studies showed that the k-means clustering is able to determine the ideal gesture subset in a shorter computation time than testing machine learning models with all the possible combinations of gestures. A finally study explored online testing with three healthy participants controlling a line-following robot with the FMG band. Though this work provides the foundation for using the FMG technology to interact with individuals with cerebral palsy, additional studies are required to determine its full potential.