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A novel method devoted to the reconstruction of the joint angles in a kinematic chain is described. The reconstruction algorithm is based on the fusion of the information deriving from inertial sensors (accelerometers) and conductive elastomer strain sensors. Accelerometers provide a reliable reconstruction when they are employed as inclinometers in quasi-static conditions. They suffer from artifacts when they are used to detect fast movements or when interactions with the environment occur. The knowledge of the frequency components of the movement to be detected permits removal of these artifacts. Conversely, conductive elastomer sensors have a complex dynamic response, but they can easily provide the frequency content of the movement to be detected. A filtering strategy of the inertial sensor signals based on the elastomer sensor response provides a reliable reconstruction of joint variables during the movement.

This paper describes the design, the development and the preliminary testing of a wearable system able perform a real time estimation of the local curvature and the length of the spine lumbar arch. The system integrate and fuse information gathered from textile based piezoresistive sensor arrays and tri-axial accelerometers. E-textile strain sensing garments suffer from non-linearities, hysteresis and long transient, while accelerometers, used as inclinometers, present biased values and are affected by the system acceleration due to subject movements. In this work, focused on the wearability and comfort of the user, we propose a fusion of the information deriving from the two class of sensors to reduce their intrinsic errors affecting measurements. Comparative evaluation of system performances with stereophotogrammetric techniques shows a 2% error in lumbar arch length reconstruction.

Monitoring body kinematics has fundamental relevance in several biological and technical disciplines. In particular the possibility to know the posture exactly may furnish a main aid in rehabilitation topics. In the present work a collection of innovative and unobtrusive garments able to detect the posture and the movement of the human body are introduced. This paper deals with the design, the development and the realization of sensing garments, from the characterization of innovative comfortable and spreadable sensors to the methodologies employed to gather information on the posture and movement. Several new algorithms devoted to the device operation are presented and tested. Data derived from the sensing garment are analyzed and compared with the data derived from a traditional movement tracking system.

Monitoring body kinematics and analyzing posture and gesture is an area of major importance in bioengineering and several other connected disciplines such as rehabilitation, sport medicine and ergonomics. Recent developments of new smart materials consent the realization of a new generation of garments with distributed sensors. What we present here is a sensing glove able to detect the posture and movements of the hand.