Human Factors & Ergonomics Program

Prolonged sitting time is associated with negative health outcomes, including physical discomfort. Chair design affects seat posture, micro-movement, and pressure distribution, which impacts the user experience. This study investigated the impact of mesh versus foam material, on comfort, preference, and subjective performance. Two chairs were used over a three-hour bout of sitting on two different days. Analysis showed no significant difference in pain, comfort, discomfort, or fatigue between chairs, but pain increased in both chairs during the last hour of sitting. The foam chair...

Background: Sedentary behavior, which is highly prevalent among office workers, is associated with multiple health disorders, including those of the musculoskeletal and cardiometabolic systems. Although prior studies looked at postures or physical activity during work or leisure time, few analyzed both posture and movement throughout the entire day.

Objective: This cross-sectional pilot study examined the movement behavior of sedentary office workers during both work and leisure time to explore its association with musculoskeletal discomfort (MSD)...

Numerous ear-related wearables require precise measurements of the external acoustic meatus (EAM) to optimise function and comfort. The purpose of this study is to describe a novel methodology for measuring the EAM. A total of 23 measurement variables (18 novel) of the EAM from the entrance to the second bend were collected on 700 Chinese subjects (age: 15-83) using casting and 3D scanning over seven age spans: 10, 20, 30, 40, 50, 60 s and 70+. The ear horizontal plane was identified as a new reference plane for measurements and the medial concha was selected as the reference point...

Objective: The purpose of this study was to develop an approach to predict hand posture (pinch versus grip) and grasp force using forearm surface electromyography (sEMG) and artificial neural networks (ANNs) during tasks that varied repetition rate and duty cycle.

Background: Prior studies have used electromyography with machine learning models to predict grip force but relatively few studies have assessed whether both hand posture and force can be predicted, particularly at varying levels of duty cycle and repetition rate.

Method:...