The European IST project MyHeart developed strategies for smart clothing, utilizing body sensor networks and embedded signal processing for on-body ECG enhancement and motion activity classification.
The MyHeart project outlines technical strategies for integrating body sensor networks and on-body signal processing algorithms into smart clothing to enhance ECG signal quality and classify motion activity.
Smart clothes increase the efficiency of long-term non-invasive monitoring systems by facilitating the placement of sensors and increasing the number of measurement locations. Since the sensors are either garment-integrated or embedded in an unobtrusive way in the garment, the impact on the subject's comfort is minimized. However, the main challenge of smart clothing lies in the enhancement of signal quality and the management of the huge data volume resulting from the variable contact with the skin, movement artifacts, non-accurate location of sensors and the large number of acquired signals. This paper exposes the strategies and solutions adopted in the European 1ST project MyHeart to address these problems, from the definition of the body sensor network to the description of two embedded signal processing techniques performing on-body ECG enhancement and motion activity classification
Luprano et al. (Fri,) reported a other. Body sensor networks and on-body signal processing algorithms was evaluated. The European IST project MyHeart developed strategies for smart clothing, utilizing body sensor networks and embedded signal processing for on-body ECG enhancement and motion activity classification.