Configurable Offline Sensor Placement Identification for a Medical Device Monitoring Parkinson's Disease

Sensor placement identification in body sensor networks is an important feature, which could render such a system more robust, transparent to the user, and easy to wear for long term data collection. It can be considered an active measure to avoid the misuse of a sensing system, specifically as these platforms become more ubiquitous and, apart from their research orientation, start to enter industries, such as fitness and health. In this work we discuss the offline, fixed class, sensor placement identification method implemented in PDMonitor (R), a medical device for long-term Parkinson's disease monitoring at home. We analyze the stepwise procedure used to accurately identify the wearables depending on how many are used, from two to five, given five predefined body positions. Finally, we present the results of evaluating the method in 88 subjects, 61 Parkinson's disease patients and 27 healthy subjects, when the overall average accuracy reached 99.1%.

Automated summary

Sensor placement identification in body sensor networks plays a crucial role in improving system robustness and transparency, as well as user convenience for long-term data collection. This study discusses an offline, fixed class method for sensor placement identification in PDMonitor(R), achieving an overall average accuracy of 99.1% based on evaluations with 88 subjects.