A newly developed textile integrated sensor shirt, called "ITcares" (Intelligent Textile for CArdio REspiratory Sensing), is presented. Textile integrated ECG sensors are known to be highly depended on the electrode-skin-impedance. Two main influence factors on the skin-electrode impedance are: 1. contact pressure and 2. moisture. Systematic measurements were performed with additional sensors to evaluate the ECG signal quality. Furthermore, signal-to-noise ratios were calculated as a quantitative measure.

Body sensor networks (BSN) are an important research topic due to various advantages over conventional measurement equipment. One main advantage is the feasibility to deploy a BSN system for 24/7 health monitoring applications. The requirements for such an application are miniaturization of the network nodes and the use of wireless data transmission technologies to ensure wearability and ease of use. Therefore, the reliability of such a system depends on the quality of the wireless data transmission. At present, most BSNs use ZigBee or other IEEE 802.15.4 based transmission technologies. Here, we evaluated the performance of a wireless transmission system of a novel BSN for biomedical applications in the 433 MHz ISM band, called Integrated Posture and Activity NEtwork by Medit Aachen (IPANEMA) BSN. The 433 MHz ISM band is used mostly by implanted sensors and thus allows easy integration of such into the BSN. Multiple measurement scenarios have been assessed, including varying antenna orientations, transmission distances and the number of network participants. The mean packet loss rate (PLR) was 0.63% for a single slave, which is comparable to IEEE 802.15.4 BSNs in the proximity of Bluetooth or WiFi networks. Secondly, an enhanced version is evaluated during on-body measurements with five slaves. The mean PLR results show a comparable good performance for measurements on a treadmill (2.5%), an outdoor track (3.4%) and in a climate chamber (1.5%).

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In this contribution, inertial and magnetic sensors are considered for real-time strapdown orientation tracking of human limb or robotic segment orientation. By using body sensor network integrated triaxial gyrometer, accelerometer, and magnetometer measurements, two orientation estimation filters are presented and subsequently designed for bias insensitive tracking of human gait. Both filters use quaternions for rotation representation, where preprocessing accelerometer and magnetometer data is conducted with the quaternion based estimation algorithm (QUEST) as a reference filter input. This results in a significant reduction of the complexity and calculation cost on the body sensor network. QUEST-based preprocessed attitude data is used for the designed extended Kalman filter (EKF) and a new complementary sliding mode observer. EKF-QUEST and complementary sliding mode observer are designed and tested in simulations and subsequently validated with a reference motion tracking system in treadmill tests.

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