Mercury: a wearable sensor network platform for high-fidelity motion analysis

  • Authors:

  • Konrad Lorincz;Bor-rong Chen;Geoffrey Werner Challen;Atanu Roy Chowdhury;Shyamal Patel;Paolo Bonato;Matt Welsh

  • Affiliations:

  • Harvard University;Harvard University;Harvard University;Harvard University;Spaulding Rehabilitation Hospital;Spaulding Rehabilitation Hospital;Harvard University

  • Venue:
  • Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
  • Year:
  • 2009

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Abstract

This paper describes Mercury, a wearable, wireless sensor 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 support long-term, longitudinal data collection on patients in hospital and home settings. Patients wear up to 8 wireless 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 application 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 processing data under severe computational constraints. Mercury 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.