title: Cluster-based analysis for personalized stress evaluation using physiological signals.
abstract: Technology development in wearable sensors and biosignal processing has made it possible to detect human stress from the physiological features. However, the intersubject difference in stress responses presents a major challenge for reliable and accurate stress estimation. This research proposes a novel cluster-based analysis method to measure perceived stress using physiological signals, which accounts for the intersubject differences. The physiological data are collected when human subjects undergo a series of task-rest cycles, incurring varying levels of stress that is indicated by an index of the State Trait Anxiety Inventory. Next, a quantitative measurement of stress is developed by analyzing the physiological features in two steps: 1) a k -means clustering process to divide subjects into different categories (clusters), and 2) cluster-wise stress evaluation using the general regression neural network. Experimental results show a significant improvement in evaluation accuracy as compared to traditional methods without clustering. The proposed method is useful in developing intelligent, personalized products for human stress management.
authors.forename: Qianli
authors.initials: Q
authors.lastname: Xu
authors.forename: Tin Lay
authors.initials: TL
authors.lastname: Nwe
authors.forename: Cuntai
authors.initials: C
authors.lastname: Guan
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods; D004576:Electromyography / Q000379:methods; D005260:Female; D005712:Galvanic Skin Response; D006339:Heart Rate; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000379:methods*; D010092:Oximetry / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013315:Stress, Psychological / Q000175:diagnosis* / Q000503:physiopathology* / Q000523:psychology