Biosensing and environmental sensing for emergency and protection e-Textiles.

The ProeTEX project introduced for the first time a complete set of smart garments integrating sensors for the physiological and environmental monitoring of emergency operators. These "smart" garments have been deeply tested in emergency-like contexts by professional rescuers, in order to assess real-time acquisition, processing and transmission of data from moving subjects while operating in harsh conditions. Here we report an overview of the main results obtained during field trials performed in 2010 by Italian and French professional firefighters, in specialized training centers, while dressing the ProeTEX prototypes. Results clearly demonstrate the benefit and step forward of such a system in order to monitor and coordinate rescuers even during intervention far away from the emergency headquarter.

Heart rate and accelerometer data fusion for activity assessment of rescuers during emergency interventions.

The current state of the art in wearable electronics is the integration of very small devices into textile fabrics, the so-called ¿smart garment.¿ The ProeTEX project is one of many initiatives dedicated to the development of smart garments specifically designed for people who risk their lives in the line of duty such as fire fighters and Civil Protection rescuers. These garments have integrated multipurpose sensors that monitor their activities while in action. To this aim, we have developed an algorithm that combines both features extracted from the signal of a triaxial accelerometer and one ECG lead. Microprocessors integrated in the garments detect the signal magnitude area of inertial acceleration, step frequency, trunk inclination, heart rate (HR), and HR trend in real time. Given these inputs, a classifier assigns these signals to nine classes differentiating between certain physical activities (walking, running, moving on site), intensities (intense, mild, or at rest) and postures (lying down, standing up). Specific classes will be identified as dangerous to the rescuer during operation, such as, ¿subject motionless lying down¿ or ¿subject resting with abnormal HR.¿ Laboratory tests were carried out on seven healthy adult subjects with the collection of over 4.5 h of data. The results were very positive, achieving an overall classification accuracy of 88.8%.

Bio-inspired controller for a dexterous prosthetic hand based on Principal Components Analysis.

Controlling a dexterous myoelectric prosthetic hand with many degrees of freedom (DoFs) could be a very demanding task, which requires the amputee for high concentration and ability in modulating many different muscular contraction signals. In this work a new approach to multi-DoF control is proposed, which makes use of Principal Component Analysis (PCA) to reduce the DoFs space dimensionality and allow to drive a 15 DoFs hand by means of a 2 DoFs signal. This approach has been tested and properly adapted to work onto the underactuated robotic hand named CyberHand, using mouse cursor coordinates as input signals and a principal components (PCs) matrix taken from the literature. First trials show the feasibility of performing grasps using this method. Further tests with real EMG signals are foreseen.