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Posture and gesture analysis, together with the monitoring of body kinematics, is a field of increasing interest in bioengineering and several connected disciplines. In this paper, some typical features of distributed sensing systems are described, as well as a methodology to read signals from such systems. Theory, simulation, results, and some specific applications are shown. Strain gauges have been used as sensors and have been deposited directly onto textile fibers, demonstrating one way to realize a wearable sensor system. [ABSTRACT FROM PUBLISHER]

In recent years, an innovative technology based on polymeric conductors and semiconductors has undergone rapid growth. These materials offer several advantages with respect to metals and inorganic conductors: lightness, large elasticity and resilience, resistance to corrosion, flexibility, impact strength, etc. These properties are suitable for implementing wearable devices. In particular, a sensitive glove able to detect the position and the motion of fingers and a sensorized leotard have been developed. Here, the characterization of the strain-sensing fabric is presented. In the first section, the polymerization process used to realize the strain sensor is described. Then, the thermal and mechanical transduction properties of the strain sensor are investigated and a geometrical parameter to invariantly codify the sensor response during aging is proposed. Finally, a brief outline of ongoing applications is reported. [ABSTRACT FROM PUBLISHER]

Conductive elastomers are a novel strain sensing technology which can be unobtrusively embedded into a garment's fabric, allowing a new type of sensorized cloths for motion analysis. A possible application for this technology is remote monitoring and control of motor rehabilitation exercises. The present work describes a sensorized shirt for upper limb posture recognition. Supervised learning techniques have been employed to compare classification models for the analysis of strains, simultaneously measured at multiple points of the shirt. The instantaneous position of the limb was classified into a finite set of predefined postures, and the movement was decomposed in an ordered sequence of discrete states. The amount of information given by the observation of each sensor during the execution of a specific exercise was quantitatively estimated by computing the information gain for each sensor, which in turn allows the data-driven optimization of the garment. Real-time feedback on exercise progress can also be provided by reconstructing the sequence of consecutive positions assumed by the limb.