Extraction and Analysis of features acquired by wearable sensors network
Abstract
The developed system aims to collect and analyze information valuable for estimating the severity of a health episode regarding patients suffering from chronic diseases. The system addresses a wearable platform, based on multi-parametric sensor data, a device which collects the data and a Smart Assistant Device, responsible for analyzing the collected multi-parametric data and facilitating the monitor of people suffering from chronic diseases in long-stay setting. The wearable platform integrates several sensors collecting information regarding motion, ECG, respiration, oxygen saturation (SpO2), temperature and various other parameters. The Smart Assistant Device, after applying pre-processing and feature extraction techniques, fuses all sensor acquired data with information inserted by the patient. An Intelligent System is developed to analyze sensors' acquired and patient inputted data, facilitating data mining techniques, and decides upon the severity of a pathological episode that was appeared to the patient.
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Feature extraction provides a meaningful representation of wearable sensor data that can formulate the relation of raw data with expected knowledge for decision making.
“Especially in wearable sensor data, according to the magnitude and complexity of the raw data, feature extraction provides a meaningful representation of the sensor data which can formulate the relation of raw data with the expected knowledge for decision making [64].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [64]
For ECG signals, although Bsoul et al. proposed dozens of features, the main focus of research is to consider R points and their properties such as R-R intervals and pick counts.
“As an example, for the works studying ECG signals, although Bsoul et al. [65] have proposed dozens of features, the main focus of research is to consider R points (the pick point of each beat), and its properties in ECG pulses such as R-R intervals, pick counts, etc. [39,64].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [64]
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Monitoring Methods of Human Body Joints: State-of-the-Art and Research Challenges
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Self-citationA Support Vector Machine Approach for Categorization of Patients Suffering from Chronic Diseases
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Self-citation
- Authors
- Bellos, C. C., Papadopoulos, A., Rosso, R., & Fotiadis, D. I.
- Affiliations
- Foundation for Research and Technology Hellas (GR)
- Venue
- Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine
- Publisher
- IEEE
- Published
- 2010-11
- Pages
- 1-4
- OpenAlex
- W2548553586
- Cited by
- 21
- References
- 11
- Citations by year
- 2022: 2 · 2021: 1 · 2019: 1 · 2018: 2 · 2017: 5 · 2015: 2 · 2014: 1 · 2013: 2 · 2012: 3
- FWCI
- 5.15
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Information Technology and Applications in Biomedicine (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- wearable sensors, chronic disease monitoring, feature extraction, long-stay care, ECG, respiratory monitoring, oxygen saturation, body temperature, intelligent health systems
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