Unit-Independent Low-Rate Wrist GSR Processing for Stress Detection Using Phasic nSCR Features
arXiv:2607.08007v1 Announce Type: cross Abstract: Galvanic skin response (GSR) is widely used for stress detection, but wrist-based GSR remains challenging because its absolute amplitude can differ substantially from laboratory-grade palmar measurements. In this paper, we propose a unit-independent low-rate wrist GSR processing pipeline to extract the number of skin conductance responses per minute (nSCR/min) as a stress-related feature. We collect paired wrist and palmar GSR recordings from 31 participants during sitting baseline, standing baseline, neutral speaking, and the Trier Social Stre
The proliferation of wearable technology and increasing focus on mental well-being are driving demand for more effective and less intrusive stress detection methods, pushing research into refined biometric analysis.
Improving the accuracy and reliability of wrist-based stress detection can democratize access to continuous physiological monitoring, enabling proactive mental health management and personalized interventions.
This research offers a method to make wrist-based GSR data more comparable to laboratory-grade palmar measurements, potentially unlocking more robust and accessible stress detection outside clinical settings.
- · Wearable tech companies
- · Mental health support services
- · Personal wellness platforms
- · Providers of highly specialized lab-based stress assessments
More accurate and pervasive stress detection capabilities become available in consumer wearables.
Increased availability of real-time stress data leads to personalized mental health nudges and early detection of burnout or anxiety.
The integration of such data with AI agents could create highly personalized 'digital well-being companions' anticipating and mitigating individual stress triggers.
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Read at arXiv cs.LG