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A large number of amputee patients doesn't use their myoelectric prosthesis, mainly due to the limited functionality of the prosthesis. The aim of this study was to investigate if it is possible to distinguish 8 different contractions by using multi-electrode sEMG. We analysed sEMG signals of a grid of 40 and 30 electrodes from 10 healthy subjects and 1 amputee patient respectively during 8 different isometric movements of the wrist and fingers and classified them with a nearest neighbour classifier. The results were very promising: over 99.5% of the movements were correctly classified and reducing the number of electrodes to 20 and 10 electrods only decreases the performance with 0.05 and 0.18%.

Augmented feedback plays an essential role in stroke rehabilitation therapy. When a force is applied to the arm, an augmented sensory (proprioceptive) cue is provided. The question was to find out if stroke patients can learn reach-and retrieval movements with error-enhanced augmented sensory feedback. The movements were performed over a predefined path, and when deviating of the path a force is provided, as colliding to a wall of a tunnel. Two chronic stroke survivors (FM of 53 and 49) performed reach and retrieval movements in a virtual tunnel. When two consecutive series of 15 repetitions of the same movements were performed, there was a consistent decrease of collisions to the wall in the second series of movements. This indicates that these patients were able to learn the predefined trajectory by means of augmented proprioceptive feedback. Despite the small number of patients tested, this finding is promising for the usage of error-enhanced augmented proprioceptive feedback in rehabilitation therapy.

Large scale implementation of telemedicine services such as telemonitoring and teletreatment will generate huge amounts of clinical data. Even small amounts of data from continuous patient monitoring cannot be scrutinised in real time and round the clock by health professionals. In future huge volumes of such data will have to be routinely screened by intelligent software systems. We investigate how to make m-health systems for ambulatory care more intelligent by applying a Decision Support approach in the analysis and interpretation of biosignal data and to support adherence to evidence-based best practice such as is expressed in treatment protocols and clinical practice guidelines. The resulting Clinical Decision Support Systems must be able to accept and interpret real time streaming biosignals and context data as well as the patient’s (relatively less dynamic) clinical and administrative data. In this position paper we describe the telemonitoring/teletreatment system developed at the University of Twente, based on Body Area Network (BAN) technology, and present our vision of how BAN-based telemedicine services can be enhanced by incorporating mobile real time Clinical Decision Support. We believe that the main innovative aspects of the vision relate to the implementation of decision support on a mobile platform; incorporation of real time input and analysis of streaming biosignals into the inferencing process; implementation of decision support in a distributed system; and the consequent challenges such as maintenance of consistency of knowledge, state and beliefs across a distributed environment.

The problem of estimating parameters of distributions by an incomplete data set is theoretically considered, but in practice the implementation of the developed methods in commercial statistical software packages varies from program to program. None of the examined software offers all possible methods. In some programs the user has no choice concerning the use of a method, or no methods at all are available. However, the most popular programs have not always the largest variety of methods. Hence some work is still waiting for the producer of statistical software.