research-article

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

Authors:

Konrad Lorincz,

Geoffrey Werner Challen,

Atanu Roy Chowdhury,

Matt WelshAuthors Info & Claims

SenSys '09: Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems

Pages 183 - 196

https://doi.org/10.1145/1644038.1644057

Published: 04 November 2009 Publication History

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.

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Index Terms

Mercury: a wearable sensor network platform for high-fidelity motion analysis
1. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems
2. Software and its engineering
  1. Software creation and management
    1. Software development techniques

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cover image ACM Conferences

SenSys '09: Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems

November 2009

438 pages

ISBN:9781605585192

DOI:10.1145/1644038

General Chair:
David Culler
University of California, Berkeley
,
Program Chairs:
Jie Liu
Microsoft Research
,
Matt Welsh
Harvard University

Copyright © 2009 ACM.

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Published: 04 November 2009

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SenSys09: The 7th ACM Conference on Embedded Network Sensor Systems

November 4 - 6, 2009

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https://dl.acm.org/doi/10.4018/IJDST.338881
Nasrullah AAnwar F(2024)HAEST: Harvesting Ambient Events to Synchronize Time across Heterogeneous IoT Devices2024 IEEE 30th Real-Time and Embedded Technology and Applications Symposium (RTAS)10.1109/RTAS61025.2024.00029(265-279)Online publication date: 13-May-2024
https://doi.org/10.1109/RTAS61025.2024.00029
Kim JShin JChoi S(2024)Jump Rope Exercise Assistance ProgramIEEE Access10.1109/ACCESS.2024.349651012(169149-169162)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3496510
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Yhdego HPaolini CAudette M(2023)Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility StudyApplied Sciences10.3390/app1308498813:8(4988)Online publication date: 16-Apr-2023
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