For trans-humeral amputation, daily living tasks requiring bimanual coordination, such as lifting up a box, are most difficult, hence most urgent for a trans-humeral prosthesis to fulfill. However, in studies reported on trans-humeral prosthetic control, the states of the target objects, such as their size, relative pose and position, which are important for any real reaching and manipulation tasks, have not been taken into account. In our previous study, for a box lifting-up task, we investigated the possibility of using around-shoulder EMG (electromyogram), for identifying target-reaching-positions for the boxes with different configurations (relative pose and position). However, with only the around-shoulder EMG, it is impossible for the system to guide the prosthesis to hold or grasp target objects precisely and fast sufficiently. The purpose of this study is to explore the possibility of using both the image information from an action camera and around-shoulder EMG, to identify targeted-reaching-positions for various box configurations more accurately and more rapidly. Multinomial logistic regression was employed to realize both information integration of, and the target-reaching-position identification. A set of experiments were conducted. As a result, an average classification rate of 75.1% could be achieved for various box configurations.
Part of the book: Prosthesis