Combining multiple human physiological signals using fuzzy logic to determine stress caused by battle dress uniforms

Abstract

Abstract This study presents a novel stress index for clothing using physiological signals to estimate stress induced by battle dress uniforms (BDU) during physical activity. The approach uses a fuzzy logic-based nonlinear mapping to compute the stress from physiological signals. Ten healthy men performed a battery of physical activities in a controlled environment. Heart rate (HR), respiration rate (RR), skin temperature (ST), and galvanic skin response (GSR) were measured continuously for the participants during activity wearing three kinds of clothing (two BDUs and a control garment). The individual physiological responses were combined using a fuzzy-logic system to derive a stress measure called Clothed Activity Stress Index (CASI). Repeated measures ANOVA showed that the garments significantly (α = .05) affected the HR (p < .001) and RR (p < .001). In addition, interactions between the activity and garment were significant for HR, RR, and ST (p < .001, p < .001, p < .036). The physiological measures differed significantly between rest and activity for the two uniforms. The stress indices (ranging between 0 and 1) during rest and activity were 0.24 and 0.35 for control, 0.27 and 0.43 for BDU-1, and 0.33 and 0.44 for BDU-2. It is shown here that clothing systems impact human stress levels to a measurable level. This computational approach is applicable to measure stress caused by protective wear under different operational conditions and can be suitable for sports and combat gears. Article Highlights A computational approach to non-linearly map human physiological signals and stress is presented. The stress caused by functional clothing systems is estimated using a fuzzy-logic mapping system for battle dress uniforms. Heart and respiration rates are highly sensitive to stress, while skin temperature and galvanic skin response are moderately sensitive.