Wireless, multipurpose in-home health monitoring platform: two case trials.

We propose a general purpose home area sensor network and monitoring platform that is intended for e-Health applications, ranging from elderly monitoring to early homecoming after a hospitalization period. Our monitoring platform is multipurpose, meaning that the system is easily configurable for various user needs and is easy to set up. The system could be temporarily rented from a service company by, for example, hospitals, elderly service providers, specialized physiological rehabilitation centers, or individuals. Our system consists of a chosen set of sensors, a wireless sensor network, a home client, and a distant server. We evaluated our concept in two initial trials: one with an elderly woman living in sheltered housing, and the other with a hip surgery patient during his rehabilitation phase. The results prove the functionality of the platform. However, efficient utilization of such platforms requires further work on the actual e-Health service concepts.

An intelligent ballistocardiographic chair using a novel SF-ART neural network and Biorthogonal wavelets.

This paper presents a comparative analysis of novel supervised fuzzy adaptive resonance theory (SF-ART), multilayer perceptron (MLP) and Multi Layer Perceptrons (MLP) neural networks over Ballistocardiogram (BCG) signal recognition. To extract essential features of the BCG signal, we applied Biorthogonal wavelets. SF-ART performs classification on two levels. At first level, pre-classifier which is self-organized fuzzy ART tuned for fast learning classifies the input data roughly to arbitrary (M) classes. At the second level, post-classification level, a special array called Affine Look-up Table (ALT) with M elements stores the labels of corresponding input samples in the address equal to the index of fuzzy ART winner. However, in running (testing) mode, the content of an ALT cell with address equal to the index of fuzzy ART winner output will be read. The read value declares the final class that input data belongs to. In this paper, we used two well-known patterns (IRIS and Vowel data) and a medical application (Ballistocardiogram data) to evaluate and check SF-ART stability, reliability, learning speed and computational load. Initial tests with BCG from six subjects (both healthy and unhealthy people) indicate that the SF-ART is capable to perform with a high classification performance, high learning speed (elapsed time for learning around half second), and very low computational load compared to the well-known neural networks such as MLP which needs minutes to learn the training material. Moreover, to extract essential features of the BCG signal, we applied Biorthogonal wavelets. The applied wavelet transform requires no prior knowledge of the statistical distribution of data samples.

A wireless ballistocardiographic chair.

This paper presents a wireless ballistocardiographic chair developed for the Proactive Health Monitoring project in the Institute of Signal Processing. EMFi sensors are used for BCG measurement and IEEE 802.15.4 RF link for radio communication between the chair and a PC. The chair measures two BCG signals from the seat and the backrest and a rough ECG signal from the armrests of the chair. The R-spike of the ECG signal can be used as a synchronisation point to extract individual BCG cardiac cycles. Also, two developed methods for extracting BCG cycles without using a reference ECG signal are presented and compared.