Processing body sensor data streams for continuous physiological monitoring
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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In most research in health monitoring systems, simple statistical parameters or statistical functions are adequate to formulate the data and retrieve expected information.
“In the most of research in health monitoring systems, dealing with the simple statistical parameters (e.g., mean, variance etc.) or statistical functions e.g., risk function [61], factor analysis [68]) would be adequate to formulate the data and retrieve the expected information [38].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [68]
Bayesian networks, fuzzy state machines, logistic regression models, and association rules have been used for specific health monitoring tasks.
“Some other examples are mentioned here: Bayesian network for classifying stress level [26,39] or for predicting patient's state [72], Fuzzy state machine for estimating the health status to provide alerts [68], logistic regression models [42] and association rules [41] to predict stress level and hospitalization of hemodialysis patients, respectively.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [68]
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Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452 · see citing works
- Authors
- Pradhan, G. N., Chattopadhyay, R., & Panchanathan, S.
- Affiliations
- Arizona State University (US)
- Venue
- Proceedings of the international conference on Multimedia information retrieval
- Publisher
- ACM
- Published
- 2010-03-29
- Pages
- 479-486
- OpenAlex
- W2041341278
- Cited by
- 7
- References
- 20
- Citations by year
- 2022: 1 · 2017: 1 · 2014: 1 · 2013: 2
- FWCI
- 1.10
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Multimedia Information Retrieval (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- wireless body area networks, wearable sensors, physiological monitoring, knowledge management systems, factor analysis, latent variables, electromyography, muscle fatigue, repetitive hand movements, physiological time series, time and frequency domain features
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