Research on pattern recognition for myoelectric control has usually focused on a small number of electromyography (EMG) channels because of better clinical acceptability and low computational load with respect to multi-channel EMG. However, recently, high density (HD) EMG technology has substantially improved, also in practical usability, and can thus be applied in myocontrol. HD EMG provides several closely spaced recordings in multiple locations over the skin surface. This study considered the use of HD EMG for controlling upper limb prostheses, based on pattern recognition. In general, robustness and reliability of classical pattern recognition systems are influenced by electrode shift in dons and doff, and by the presence of malfunctioning channels. The aim of this study is to propose a new approach to attenuate these issues. The HD EMG grid of electrodes is an ensemble of sensors that records data spatially correlated. The experimental variogram, which is a measure of the degree of spatial correlation, was used as feature for classification, contrary to previous approaches that are based on temporal or frequency features. The classification based on the variogram was tested on seven able-bodied subjects and one subject with amputation, for the classification of nine and seven classes, respectively. The performance of the proposed approach was comparable with the classic methods based on time-domain and autoregressive features (average classification accuracy over all methods ∼ 95% for nine classes). However, the new spatial features demonstrated lower sensitivity to electrode shift ( ± 1 cm) with respect to the classic features . When even just one channel was noisy, the classification accuracy dropped by ∼ 10% for all methods. However, the new method could be applied without any retraining to a subset of high-quality channels whereas the classic methods require retraining when some channels are omitted. In conclusion, the new spatial feature space proposed in this study improved the robustness to electrode number and shift in myocontrol with respect to previous approaches.

Wireless implanted devices can be used to interface patients with disabilities with the aim of restoring impaired motor functions. Implanted devices that record and transmit electromyographic (EMG) signals have been applied for the control of active prostheses. This simulation study investigates the propagation losses and the absorption rate of a wireless radio frequency link for in-to-on body communication in the medical implant communication service (MICS) frequency band to control myoelectric upper limb prostheses. The implanted antenna is selected and a suitable external antenna is designed. The characterization of both antennas is done by numerical simulations. A heterogeneous 3D body model and a 3D electromagnetic solver have been used to model the path loss and to characterize the specific absorption rate (SAR). The path loss parameters were extracted and the SAR was characterized, verifying the compliance with the guideline limits. The path loss model has been also used for a preliminary link budget analysis to determine the feasibility of such system compliant with the IEEE 802.15.6 standard. The resulting link margin of 11 dB confirms the feasibility of the system proposed.

In the last few years the use of implanted devices has been considered also in the field of myoelectric hand prostheses. Wireless implanted EMG (Electromyogram) sensors can improve the functioning of the prosthesis, providing information without the disadvantage of the wires, and the usability by amputees. The solutions proposed in the literature are based on proprietary communication protocols between the implanted devices and the prosthesis controller, using frequency bands that are already assigned to other purposes. This study proposes the use of a standard communication protocol (IEEE 802.15.6), specific for wireless body area networks (WBANs), which assign a specific bandwidth to implanted devices. The propagation losses from in-to-on body were investigated by numerical simulation with a 3D human model and an electromagnetic solver. The channel model resulting from the study represents the first step towards the development of myoelectric prosthetic hands which are driven by signals acquired by implanted sensors. However these results can provide important information to researchers for further developments, and manufacturers, which can decrease the production costs for hand prostheses having a common standard of communication with assigned frequencies of operation.

This thesis is based on my work done at the Institute for Neurorehabilitation Systems at the University Medical Center Goettingen. My work has been partially founded by German Ministry for Education and Research (BMBF) via the Bernstein Focus Neurotechnology (BFNT) Göttingen under grant number 1GQ0810 The local ethics committee approved all studies involving human subjects, and all subjects signed informed consents prior to their participation in the studies. The entire thesis has been originally written by me. Part of the materials used in this thesis have also been published in journals or conferences, where I am the first or corresponding author. All rights for re-use of previously published material were obtained. Reused figures and tables of IEEE publications are marked with © [Year] IEEE.Hereby I declare that I have written this thesis independently and with no other aids and sources than quoted.