The study of hand and finger movement is an important topic with applications in prosthetics, rehabilitation, and ergonomics. Surface electromyography (sEMG) is the gold standard for the analysis of muscle activation. Previous studies investigated the optimal electrode number and positioning on the forearm to obtain information representative of muscle activation and robust to movements. However, the sEMG spatial distribution on the forearm during hand and finger movements and its changes due to different hand positions has never been quantified. The aim of this work is to quantify 1) the spatial localization of surface EMG activity of distinct forearm muscles during dynamic free movements of wrist and single fingers and 2) the effect of hand position on sEMG activity distribution. The subjects performed cyclic dynamic tasks involving the wrist and the fingers. The wrist tasks and the hand opening/closing task were performed with the hand in prone and neutral positions. A sensorized glove was used for kinematics recording. sEMG signals were acquired from the forearm muscles using a grid of 112 electrodes integrated into a stretchable textile sleeve. The areas of sEMG activity have been identified by a segmentation technique after a data dimensionality reduction step based on Non Negative Matrix Factorization applied to the EMG envelopes. The results show that 1) it is possible to identify distinct areas of sEMG activity on the forearm for different fingers; 2) hand position influences sEMG activity level and spatial distribution. This work gives new quantitative information about sEMG activity distribution on the forearm in healthy subjects and provides a basis for future works on the identification of optimal electrode configuration for sEMG based control of prostheses, exoskeletons, or orthoses. An example of use of this information for the optimization of the detection system for the estimation of joint kinematics from sEMG is reported.

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During face to face communication, it has been suggested that as much as 70% of what people communicate when talking directly with others is through paralanguage involving multiple modalities combined together (e.g. voice tone and volume, body language). In an attempt to render humancomputer interaction more similar to human-human communication and enhance its naturalness, research on sensory acquisition and interpretation of single modalities of human expressions have seen ongoing progress over the last decade. These progresses are rendering current research on artificial sensor fusion of multiple modalities an increasingly important research domain in order to reach better accuracy of congruent messages on the one hand, and possibly to be able to detect incongruent messages across multiple modalities (incongruency being itself a message about the nature of the information being conveyed). Accurate interpretation of emotional signals -quintessentially multimodal -would hence particularly benefit from multimodal sensor fusion and interpretation algorithms. In this paper we provide a state of the art multimodal fusion and describe one way to implement a generic framework for multimodal emotion recognition. The system is developed within the MAUI framework [31] and Scherer's Component Process Theory (CPT) [49,50,51,24,52], with the goal to be modular and adaptive. We want the designed framework to be able to accept different single and multi modality recognition systems and to automatically adapt the fusion algorithm to find optimal solutions. The system also aims to be adaptive to channel (and system) reliability.