A multi-Kalman filter-based approach for decoding arm kinematics from EMG recordings.

BACKGROUND: Remarkable work has been recently introduced to enhance the usage of Electromyography (EMG) signals in operating prosthetic arms. Despite the rapid advancements in this field, providing a reliable, naturalistic myoelectric prosthesis remains a significant challenge. Other challenges include the limited number of allowed movements, lack of simultaneous, continuous control and the high computational power that could be needed for accurate decoding. In this study, we propose an EMG-based multi-Kalman filter approach to decode arm kinematics; specifically, the elbow angle (θ), wrist joint horizontal (X) and vertical (Y) positions in a continuous and simultaneous manner. RESULTS: Ten subjects were examined from which we recorded arm kinematics and EMG signals of the biceps, triceps, lateral and anterior deltoid muscles corresponding to a randomized set of movements. The performance of the proposed decoder is assessed using the correlation coefficient (CC) and the normalized root-mean-square error (NRMSE) computed between the actual and the decoded kinematic. Results demonstrate that when training and testing the decoder using same-subject data, an average CC of 0.68 ± 0.1, 0.67 ± 0.12 and 0.64 ± 0.11, and average NRMSE of 0.21 ± 0.06, 0.18 ± 0.03 and 0.24 ± 0.07 were achieved for θ, X, and Y, respectively. When training the decoder using the data of one subject and decoding the data of other subjects, an average CC of 0.61 ± 0.19, 0.61 ± 0.16 and 0.48 ± 0.17, and an average NRMSE of 0.23 ± 0.07, 0.2 ± 0.05 and 0.38 ± 0.15 were achieved for θ, X, and Y, respectively. CONCLUSIONS: These results suggest the efficacy of the proposed approach and indicates the possibility of obtaining a subject-independent decoder.

Long-term decoding of movement force and direction with a wireless myoelectric implant.

OBJECTIVE: The ease of use and number of degrees of freedom of current myoelectric hand prostheses is limited by the information content and reliability of the surface electromyography (sEMG) signals used to control them. For example, cross-talk limits the capacity to pick up signals from small or deep muscles, such as the forearm muscles for distal arm amputations, or sites of targeted muscle reinnervation (TMR) for proximal amputations. Here we test if signals recorded from the fully implanted, induction-powered wireless Myoplant system allow long-term decoding of continuous as well as discrete movement parameters with better reliability than equivalent sEMG recordings. The Myoplant system uses a centralized implant to transmit broadband EMG activity from four distributed bipolar epimysial electrodes. APPROACH: Two Rhesus macaques received implants in their backs, while electrodes were placed in their upper arm. One of the monkeys was trained to do a cursor task via a haptic robot, allowing us to control the forces exerted by the animal during arm movements. The second animal was trained to perform a center-out reaching task on a touchscreen. We compared the implanted system with concurrent sEMG recordings by evaluating our ability to decode time-varying force in one animal and discrete reach directions in the other from multiple features extracted from the raw EMG signals. MAIN RESULTS: In both cases, data from the implant allowed a decoder trained with data from a single day to maintain an accurate decoding performance during the following months, which was not the case for concurrent surface EMG recordings conducted simultaneously over the same muscles. SIGNIFICANCE: These results show that a fully implantable, centralized wireless EMG system is particularly suited for long-term stable decoding of dynamic movements in demanding applications such as advanced forelimb prosthetics in a wide range of configurations (distal amputations, TMR).