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No abstract

In recent years, mutual capacitive sensing made significant advances in the field of gathering implicit contextual data. These systems find broad usage in pervasive activity-recognition systems, installed stationary or made portable. In the domain of context recognition new ways of interaction with the environment opened up since conductive objects can be detected under certain conditions at distances up to 50 cm. This paper investigates an approach to recognize bed postures using mutual capacitance sensing. The overall goal is to develop a technological concept that can be applied to recognize bed postures of patients in elderly homes. The use of this contextual data may lead to many desired benefits in elderly care e.g. the better prevention of decubitus ulcer, a condition caused by prolonged pressure on the skin resulting in injuries to skin and underlying tissues. For this, we propose a low-cost grid of crossed wires of 48 measurement points placed between the mattress and the bed sheet. The experimental results analyze a set of five lying positions. We achieved for all tested individuals an accuracy of 80.76% and for several individuals of the same bodysize an accuracy of 93.8%

No abstract

Capacitive sensing is increasingly used to gather contextual information about humans. They can be used to create stationary or mobile systems for non-contact activity recognition. They are able to sense any conductive objects at distances up to 50 cm. This paper investigates an approach to classify bed postures using mutual capacitance sensing. The goal is to develop a system that prevents decubitus ulcers, which is a condition caused by prolonged pressure on the skin that can result in injuries to the skin and underlying tissues. The posture recognition is used to detect prolonged lying in a single pose and can notify care personnel. A low-cost grid of crossed wires is proposed that is placed between the mattress and the bed sheet that creates 48 measurement points. The experiments analyze a set of five bedding positions with 14 users. Using self-defined features, we achieved an accuracy of 80.8% for all users and an accuracy of 93.8% for individuals of similar body size. Refining the classification approach by directly classifying the raw data an overall accuracy of 90.5% was reached. By introducing an uncertainty threshold the classification is correct in 97.6% of cases