Reliability assessment in everyday-objects based physical-activity sensing using personal information

Mobile physical-activity sensing and recognition in daily life can support medical and physiotherapeutic therapies by providing additional objective information. In our future scenario the patient is not equipped with any specialized sensor devices but uses sensors that are embedded in everyday objects, such as clothes, machines for household and office, and personal transportation and communication means. Those devices benefit from their omnipresence and their unobtrusive usage, but sensor data may suffer from low and unpredictably changing reliability and quality, and also from ambiguity and uncertainty. In this paper, we propose a new method that can assess the reliability of physical-activity measurement data from unreliable sensors embedded in everyday objects and further increase the reliability by an error compensation method.