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Processing body sensor data streams for continuous physiological monitoring

Published: 29 March 2010 Publication History

Abstract

With the recent advancement in the wearable sensor technology, various body sensor network systems are being incorporated in the garments to monitor continuous physiological as well as motor behavior of an individual. The raw physiological time series data coming from on-body sensors requires a thorough analysis for extraction of meaningful information. In addition, extracted information need to be presented/recommended to monitoring personnel/self to derive the high-level interpretation of the physiological state without having domain knowledge.
In this paper, we propose a knowledge management system that extracts and conveys the information of the physiological states using individualized factor analysis model. The factor analysis based on the quantitative features extracted from the raw data streams provides the hidden knowledge components in the form of latent factors. We tested this system on the raw electromyogram signals from the hand muscles collected during the continuous monitoring of repetitive hand movements, where the hidden information in the form of intensity level of the activity and the muscle fatigue was extracted from the time and frequency domain features.

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    cover image ACM Conferences
    MIR '10: Proceedings of the international conference on Multimedia information retrieval
    March 2010
    600 pages
    ISBN:9781605588155
    DOI:10.1145/1743384
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 29 March 2010

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    Author Tags

    1. electromyogram
    2. factor analysis
    3. fatigue
    4. feature extraction
    5. physiological
    6. wearable sensors

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    MIR '10: International Conference on Multimedia Information Retrieval
    March 29 - 31, 2010
    Pennsylvania, Philadelphia, USA

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    • (2017)Cloud-centric IoT based student healthcare monitoring frameworkJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-017-0520-69:5(1293-1309)Online publication date: 17-Jun-2017
    • (2014)Predicting person's Zheng states using the heterogeneous sensor data by the semi-subjective teaching of TCM doctors2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2014.6973980(636-641)Online publication date: Oct-2014
    • (2013)Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and ChallengesSensors10.3390/s13121747213:12(17472-17500)Online publication date: 17-Dec-2013
    • (2013)Evaluating the Effect of Different Mode's Attributes on the Subjective Classification in the Case of TCMProceedings of the 2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation10.1109/CIMSim.2013.35(171-176)Online publication date: 24-Sep-2013
    • (2011)Sensor data analysis for equipment monitoringKnowledge and Information Systems10.1007/s10115-010-0365-128:2(333-364)Online publication date: 1-Aug-2011

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