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Low back disorders (LBDs) are a leading occupational health issue. Wearable sensors, such as inertial measurement units (IMUs) and/or pressure insoles, could automate and enhance the ergonomic assessment of LBD risks during material handling. However, much remains unknown about which sensor signals to use and how accurately sensors can estimate injury risk. The objective of this study was to address two open questions: (1) How accurately can we estimate LBD risk when combining trunk motion and under-the-foot force data (simulating a trunk IMU and pressure insoles used together)? (2) How much greater is this risk assessment accuracy than using only trunk motion (simulating a trunk IMU alone)? We developed a data-driven simulation using randomized lifting tasks, machine learning algorithms, and a validated ergonomic assessment tool. We found that trunk motion-based estimates of LBD risk were not strongly correlated (r range: 0.20–0.56) with ground truth LBD risk, but adding under-the-foot force data yielded strongly correlated LBD risk estimates (r range: 0.93–0.98). These results raise questions about the adequacy of a single IMU for LBD risk assessment during material handling but suggest that combining an IMU on the trunk and pressure insoles with trained algorithms may be able to accurately assess risks.

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Pressure sensing insoles enable us to estimate forces under the feet during activities such as running, which can provide valuable insight into human movement. Pressure insoles also afford the opportunity to collect more data in more representative environments than can be achieved in laboratory studies. One key challenge with real-world use of pressure insoles is limited battery life which restricts the amount of data that can be collected on a single charge. Reducing sampling frequency is one way to prolong battery life, at the cost of decreased measurement accuracy, but this trade-off has not been quantified, which hinders decision-making by researchers and developers. Therefore, we characterized the effect of decreasing sampling frequency on peak force estimates from pressure insoles (Novel Pedar, 100 Hz) across a range of running speeds and slopes. Data were downsampled to 50, 33, 25, 20, 16 and 10 Hz. Force peaks were extracted due to their importance in biomechanical algorithms trained to estimate musculoskeletal forces and were compared with the reference sampling frequency of 100 Hz to compute relative errors. Peak force errors increased exponentially from 0.7% (50 Hz) to 9% (10 Hz). However, peak force errors were <3% for all sampling frequencies down to 20 Hz. For some pressure insoles, sampling rate is inversely proportional to battery life. Therefore, these findings suggest that battery life could be increased up to 5x at the expense of 3% errors. These results are encouraging for researchers aiming to deploy pressure insoles for remote monitoring or in longitudinal studies.

Exoskeletons and exosuits (exos) are wearable devices that physically assist movement. User comfort is critically important for societal adoption of exos. Thermal comfort (a person’s satisfaction with their thermal environment) represents a key design challenge. Exos must physically attach/interface to the body to apply forces, and these interfaces inevitably trap some heat. It is envisioned that thermal comfort could be improved by designing mode-switching exo interfaces that temporarily loosen around a body segment when assistive forces are not being applied. To inform exo design, a case series study (N = 4) based on single-subject design principles was performed. Our objective was to assess individual responses to skin temperature and thermal comfort during physical activity with a Loose leg-sleeve interface compared with a Form-Fitting one, and immediately after a Form-Fitting sleeve switched to Loose. Skin under the Loose sleeve was 2–3 °C (4–6 °F) cooler after 25 min of physical activity, and two of four participants reported the Loose sleeve improved their thermal comfort. After completion of the physical activity, the Form-Fitting sleeve was loosened, causing a 2–4 °C (3–8 °F) drop in skin temperature underneath for all participants, and two participants to report slightly improved thermal comfort. These findings confirmed that an exo that can quickly loosen its interface when assistance is not required—and re-tighten when it is— has the potential to enhance thermal comfort for some individuals and environments. More broadly, this study demonstrates that mode-switching mechanisms in exos can do more than adjust physical assistance: they can also exploit thermodynamics and facilitate thermoregulation in a way that enhances comfort for exo users.