An approach for real-time stress-trend detection using physiological signals in wearable computing systems for automotive drivers
Abstract
Fast and credible identification and estimation of driver's stress-level and stress-type from sensed physiological signals has been one of the critical research areas in the recent past. Several good metrics and mechanisms involving bioelectric signals like the Galvanic Skin Response (GSR), Electrocardiogram (ECG) and the Photoplethysmography (PPG) have been identified by the scholars over the years. This paper discusses the features extracted from physiological data collected in five different scenarios and their usefulness with the help of statistical trend analysis methods. The algorithm developed comprises of a novel shape-based feature weight allocation approach and a technique for credible online realtime stress-trend detection. Such a stress-trend detection by the mesh of embedded sensory elements residing in the e-fabric of a wearable computing system will help in reducing chances of fatal driving errors by the way of in-time activation of alerts and actuation of corresponding safety / recovery procedures.
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Few papers use domain knowledge and predefined information to detect anomalies for decision making, such as sleep episodes and hazardous stress levels.
“Few of the papers have used domain knowledge and predefined information to detect anomalies for decision making such as anomaly detection in sleep episodes [34,35], and finding hazardous stress levels [36].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [36]
For filtering artifacts, designed modules have usually applied threshold-based methods or used statistical tools to interpolate missing data points.
“According to the literature, for filtering artifacts, designed modules have usually applied threshold-based methods to filter sensor data [36,60] or used statistical tools to interpolate the missing data points [61].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [36]
In the time domain, extracted features usually include basic waveform characters and statistical parameters such as mean, variance, pick counts.
“In the time domain, the extracted features usually include basic waveform characters and statistical parameters related to the visible attributes in data stream such as mean, variance, pick counts, etc. [36,61].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [36]
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- Authors
- Singh, R. R., Conjeti, S., & Banerjee, R.
- Affiliations
- Birla Institute of Technology and Science, Pilani (IN)
- Venue
- 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)
- Publisher
- IEEE
- Published
- 2011-10
- Pages
- 1477-1482
- OpenAlex
- W1980734291
- Cited by
- 27
- References
- 24
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2022: 2 · 2021: 1 · 2020: 3 · 2019: 4 · 2018: 1 · 2017: 1 · 2016: 2 · 2015: 2 · 2014: 1 · 2013: 4 · 2012: 2
- FWCI
- 3.56
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Intelligent Transportation Systems (ITSC) (publishedVersion)
- Funders
- Dartmouth College
- Birla Institute of Technology and Science, Pilani
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- driver stress detection, physiological signals, wearable computing, skin conductance, electrocardiogram, photoplethysmography, real-time monitoring, driver fatigue
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