Abstract
Healthcare workers experience high musculoskeletal injury rates due to frequent bending and lifting while wearing heavy attire. In a series of studies, we aim to develop wearable vibrotactile feedback devices which will reduce maladaptive posture without interfering with simultaneous cognitive and sensorimotor tasks. We report two such studies here. Study 1 tested two algorithms for delivering timely feedback, while Study 2 investigated optimal stimulus parameters. In Study 1, two algorithms for characterizing maladaptive posture were tested – one based on Rapid Upper Limb Assessment (RULA) thresholds, and one using exposure variation analysis (EVA), which accounts for cumulative posture risk. Participants received feedback while performing a modified hand-tool dexterity task. Relative to a control condition, EVA feedback reduced average time spent in maladaptive postures (Control = 74.2s, RULA = 20.4s, EVA = 5.5s), and also required less stimulation than RULA-based feedback (RULA = 10.4 cues, EVA = 4.6 cues) without increasing task duration or perceived cognitive workload. Feedback was high-strength and long-duration, however, which may not be optimal for real-world use. In Study 2, we stimulated participants at three short durations using staircasing so that strength converged on absolute threshold. Psychometric functions showed that longer-duration stimuli were detectable at lower strength. Feedback strength can therefore be lowered without reducing detection rates. Future studies will use this Study 2 data to control the detectability of stimuli during simultaneous cognitive and sensorimotor tasks, thus manipulating attentional load. Ultimately, our aim is to find the ideal stimulus properties which balance detectability, non-interference, and user comfort.
