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Advancements in telework have increased occupational flexibility for employees and employers alike. However, while effective telework requires planning, the COVID-19 pandemic required many employees to quickly shift to working from home without making sure the requirements for telework were in place beforehand. This study evaluated the transition to telework on university faculty and staff and investigated the effect of telework setup and ergonomics training on work-related discomfort in the at-home environment. Respondents reported increases in new or worsening pain since working from home of 24% and 51%, respectively, suggesting an immediate need for ergonomic interventions, including workstation evaluations, ergonomic training, and individual ergonomic assessments, for those who work from home.

Gait adaptations, in response to novel environments, devices or changes to the body, can be driven by the continuous optimization of energy expenditure. However, whether energy optimization involves implicit processing (occurring automatically and with minimal cognitive attention), explicit processing (occurring consciously with an attention-demanding strategy) or both in combination remains unclear. Here, we used a dual-task paradigm to probe the contributions of implicit and explicit processes in energy optimization during walking. To create our primary energy optimization task, we used lower-limb exoskeletons to shift people's energetically optimal step frequency to frequencies lower than normally preferred. Our secondary task, designed to draw explicit attention from the optimization task, was an auditory tone discrimination task. We found that adding this secondary task did not prevent energy optimization during walking; participants in our dual-task experiment adapted their step frequency toward the optima by an amount and at a rate similar to participants in our previous single-task experiment. We also found that performance on the tone discrimination task did not worsen when participants were adapting toward energy optima; accuracy scores and reaction times remained unchanged when the exoskeleton altered the energy optimal gaits. Survey responses suggest that dual-task participants were largely unaware of the changes they made to their gait during adaptation, whereas single-task participants were more aware of their gait changes yet did not leverage this explicit awareness to improve gait adaptation. Collectively, our results suggest that energy optimization involves implicit processing, allowing attentional resources to be directed toward other cognitive and motor objectives during walking.

Introduction. Measures of metabolic energy expenditure can provide valuable insight into healthy and impaired gait, the design and control of assistive devices, and rehabilitation progress. The gold standard for estimating energy expenditure during locomotion is indirect calorimetry, where oxygen use is captured at the mouth. Although accurate, indirect calorimetry systems are expensive, cumbersome, and often limited to lab settings. Objective. The aim of our research is to develop a lightweight, portable, and low-cost method for accurately estimating energy expenditure using wearable sensors. Our method must meet the following design criteria: i. estimate walking and running energy expenditure within 5% error of gold standard measures, ii. maintain accuracy given changes to terrain and external loads, iii. provide a continuous estimate with estimate intervals a maximum of one minute apart, and iv. cost under $1000. Methods. In pilot testing, we instrumented two participants (male, 21-22 years, 84-90 kg, 1.88-1.90m) with indirect calorimetry to measure gold standard energy expenditure, as well as the following wearable sensors: an accelerometer at the pelvis and foot, a heart rate monitor, and a respiratory belt. The participants walked and ran on a predefined outdoor route on Queen’s campus, including sections with distinct average inclines (0% and 5%). Participants also wore ankle weights (3% body weight) for particular sections of the route. We will use a multiple regression analysis, with cross-validation design, to predict energy expenditure using custom metrics derived from the wearable sensors.