FMG based continuous finger movement prediction toward partial hand prosthesis control

Partial hand amputation forms more than 90% of all the upper limb amputations. To improve the quality of life for partial hand amputees different prosthesis options, including externally-powered prosthesis, have been investigated. This work is exploring Force Myography (FMG) as a technique for regressing grasping movement accompanied by wrist position variations. This study can lay the groundwork for a future investigation of FMG as a technique for controlling externally-powered prostheses continuously. Ten able-bodied participants performed three hand movements while their wrist was fixed in one of the six predefined positions. Two approaches were examined for estimating grasping: (i) one regression model, trained on data from all wrist positions and hand movements; (ii) a classifier that identified the wrist position followed by a separate regression model for each wrist position. Both approaches presented similar performance while the first approach was more than two times faster. The results indicate the potential of FMG to regress grasping movement, accompanied by wrist position variations.