Saim Kim

Influence of contact pressure and moisture on the signal quality of a newly developed textile ECG sensor shirt

A newly developed textile integrated sensor shirt, called ldquoITcaresrdquo (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.

On the Road to a Textile Integrated Bioimpedance Early Warning System for Lung Edema

Early detection of lung edema for patients suffering from chronic heart disease improves the medical treatment and can avoid committal of the patient to an intensive care unit. Therefore, an early warning system monitoring the amount of fluid in the lungs by measuring trans-thoracic bioimpedance outside the body has been developed. The proposed system(TiBIS) consists of a textile integrated measurement module and a Personal Digital Assistant for signal processing and user interaction.

Evaluation of a 433 MHz Band Body Sensor Network for Biomedical Applications

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%).

A versatile Body Sensor Network for health care applications

This paper presents the development of a Body Sensor Network, called the meditBSN, for health monitoring applications. It is designed around a MSP430F1611 micr meditBSN ocontroller and a CC1101 433 MHz ISM band transceiver IC. A Bioimpedance Spectroscopy measurement condition monitoring application has been developed using the meditBSN. It incorporates three 3-D accelerometers and a skin temperature probe. Additionally, a PDA software was developed to control BIS measurements as well as to analyze and save the data from the meditBSN.

Mobile Mining and Information Management in HealthNet Scenarios

Health and mobility of elderly people is gaining importance in aging societies. New communication-based methods to provide health services with personal health care devices are considered promising elements of first-class medical care services for everybody. To achieve this vision, several technological issues have to be solved: (i) body sensors to monitor vital functions have to be developed; (ii) these sensors should be integrated into textile structures to guarantee ease of use and patient acceptance; (iii) the collected sensor data has to be analyzed to detect emergency situations and to reduce the data volume; (iv) relevant data has to be integrated with other information systems in the work environment of medical experts. These challenges are addressed within the HealthNet project at RWTH Aachen University. The goal of the project is to develop a framework in which health professionals can remotely monitor and diagnose mobile patients. The described demonstration presents our results of the first three issues mentioned above while focussing on the employed data mining and management techniques.

Body sensor network-based strapdown orientation estimation: Application to human locomotion

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.

Orientierungsschätzung mit einem Sliding Mode-Beobachter auf Basis Body Sensor Network-integrierter Inertialsensorik

Zusammenfassung Dieser Beitrag beschreibt einen Sliding Mode-Beobachter, der auf einem Prozessmodell der menschlichen Bewegung basiert und dazu genutzt wird, die räumliche Orientierung von Körpersegmenten unter dynamischen Bedingungen zu bestimmen. Dazu kommt eine in ein Body Sensor Network integrierte Inertialsensorik zum Einsatz, mit der die Leistungsfähigkeit des Gesamtsystems in Laufband-Versuchen evaluiert wird.

Bootstrap aggregating decision tree for motion classification based on a textile-integrated and wearable sensorarray

In this work a system for instant classification of motion patterns is presented. It is based on a non-contact magnetic induction monitoring device, which is textile-integrated, wearable, and able to measure pulse and respiratory activity. The proposed classificator is based on a decision tree algorithm generated by bootstrap aggregating. Its accurate classification performance is validated with the help of a data set comprising five exemplary motion patterns. Furthermore, the dependance of the misclassification error on the input sample length is investigated.

Development of a system to measure local measurement conditions around textile electrodes

The three main influence factors on the interface between textile electrode an skin are: temperature, contact pressure and relative humidity. This paper presents first results of a prototype, which measures these local measurement conditions around textile electrodes. The wearable prototype is a data acquisition system based on a microcontroller with a flexible sensor sleeve. Validation measurements included variation of ambient temperature, contact pressures and sleeve material. Results show a good correlation with data found in literature.

A Body Sensor Network Based Support System for Automated Bioimpedance Spectroscopy Measurements

This paper presents the first application of a newly developed Body Sensor Network (BSN), called the meditBSN. It is deployed as a support system for mobile bio impedance spectroscopy (BIS) measurements. BIS measurements are used to monitor the water balance and nutrition status of patients. So far, only stationary measurement devices are available. One reason is that BIS measurements are prone to various influence factors like motion and temperature. Therefore, strict measurement protocols have to be followed to gain reproducible results.