The relation between physical and mental load, and the course of physiological functions and cognitive performance

Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology

In recent years, affective computing has emerged as a promising approach to studying user experience, replacing subjective methods that rely on participants' self-evaluation. Affective computing uses biometrics to recognize people's emotional states as they interact with a product. However, the cost of medical-grade biofeedback systems is prohibitive for researchers with limited budgets. An alternative solution is to use consumer-grade devices, which are more affordable. However, these devices require proprietary software to collect data, complicating data processing, synchronization, and integration. Additionally, researchers need multiple computers to control the biofeedback system, increasing equipment costs and complexity. To address these challenges, we developed a low-cost biofeedback platform using inexpensive hardware and open-source libraries. Our software can serve as a system development kit for future studies. We conducted a simple experiment with one participant to validate the platform's effectiveness, using one baseline and two tasks that elicited distinct responses. Our low-cost biofeedback platform provides a reference architecture for researchers with limited budgets who wish to incorporate biometrics into their studies. This platform can be used to develop affective computing models in various domains, including ergonomics, human factors engineering, user experience, human behavioral studies, and human-robot interaction.

Workers' whole day workload and next day cognitive performance

Workload experienced over the whole day, not just work periods, may impact worker cognitive performance. We hypothesized that experiencing greater than typical whole day workload would be associated with lower visual processing speed and lower sustained attention ability, on the next day. To test this, we used dynamic structural equation modeling to analyze data from 56 workers with type 1 diabetes. For a two-week period, on smartphones they answered questions about whole day workload at the end of each day, and completed cognitive tests 5 or 6 times throughout each day. Repeated smartphone cognitive tests were used, instead of traditional one- time cognitive assessment in the laboratory, to increase the ecological validity of the cognitive tests. Examples of reported occupations in our sample included housekeeper, teacher, physician, and cashier. On workdays, the mean number of work hours reported was 6.58 (SD 3.5). At the within-person level, greater whole day workload predicted decreased mean processing speed the next day (standardized estimate=-0.10, 95% CI -0.18 to -0.01) using a random intercept model; the relationship was not significant and only demonstrated a tendency toward the expected effect (standardized estimate= -0.07, 95% CI -0.15 to 0.01) in a model with a random intercept and a random regression slope. Whole day workload was not found to be associated with next-day mean sustained attention ability. Study results suggested that just one day of greater than average workload could impact next day processing speed, but future studies with larger sample sizes are needed to corroborate this finding.