Mobile-Enabled Prosthetic System with Machine Learning Support

Machine learning (ML) and its applications have expanded over the past few years. ML inevitably makes its way to the world of prosthetics and amputees. Smart prosthetics have been studied and growing recently. Through the collection of data from these devices, results can help the user in numerous ways. On the other hand, mobile devices and applications are widely used. However, how to combine mobile applications and ML to enhance the prosthetic device was less addressed. In this research, we studied ML with an loT -based prosthetic device paired with a mobile application. The hypothesis was that the ML methods could help evaluate the user's status. The study results showed that some evaluated ML methods were able to see through the average temperature, humidity and contraction percentage of the people who wear a designed prosthetic device. The results also indicated that the users could tell if the contractions reached a concerning level.