alexa Mercury: A Wearable Sensor Network Platform for High-Fidelity Motion Analysis
Engineering

Engineering

Journal of Biosensors & Bioelectronics

Author(s): Konrad Lorincz

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This paper describes Mercury, a wearable, wireless sen- sor platform for motion analysis of patients being treated for neuromotor disorders, such as Parkinson's Disease, epilepsy, and stroke. In contrast to previous systems intended for short-term use in a laboratory, Mercury is designed to sup- port long-term, longitudinal data collection on patients in hospital and home settings. Patients wear up to 8 wire- less nodes equipped with sensors for monitoring movement and physiological conditions. Individual nodes compute high-level features from the raw signals, and a base station performs data collection and tunes sensor node parameters based on energy availability, radio link quality, and applica- tion specific policies. Mercury is designed to overcome the core challenges of long battery lifetime and high data fidelity for long-term studies where patients wear sensors continuously 12 to 18 hours a day. This requires tuning sensor operation and data transfers based on energy consumption of each node and pro- cessing data under severe computational constraints. Mer- cury provides a high-level programming interface that allows a clinical researcher to rapidly build up different policies for driving data collection and tuning sensor lifetime. We present the Mercury architecture and a detailed evaluation of two applications of the system for monitoring patients with Parkinson's Disease and epilepsy.

This article was published in Embedded Networked Sensor Systems and referenced in Journal of Biosensors & Bioelectronics

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