Benchmarking commercially available soft and rigid passive back exoskeletons for an industrial workplace.

Low-back pain is a common occupational hazard for industrial workers. Several studies show the advantages of using rigid and soft back-support passive exoskeletons and exosuits (exos) to reduce the low-back loading and risk of injury. However, benefits of using these exos have been shown to be task-specific. Therefore, in this study, we developed a benchmarking approach to assess exos for an industrial workplace at Hankamp Gears B.V. We assessed two rigid (Laevo Flex, Paexo back) and two soft (Auxivo Liftsuit 1.0, and Darwing Hakobelude) exos for tasks resembling the workplace. We measured the assistive moment provided by each exo and their respective influence on muscle activity as well as the user's perception of comfort and exertion. Ten participants performed four lifting tasks (Static hold, Asymmetric, Squat, and Stoop), while their electromyography and subjective measures were collected. The two rigid exos provided the largest assistance during the Dynamic tasks. Reductions in erector spinae activity were seen to be task-specific, with larger reductions for the two rigid exos. Overall, Laevo Flex offered a good balance between assistive moments, reductions in muscle activity, as well as user comfort and reductions in perceived exertion. Thus, we recommend benchmarking exos for intended use in the industrial workplace. This will hopefully result in a better adoption of the back-support exoskeletons in the workplace and help reduce low-back pain.

Electromyography-driven musculoskeletal models with time-varying fatigue dynamics improve lumbosacral joint moments during lifting.

Muscle fatigue is prevalent across different aspects of daily life. Tracking muscle fatigue is useful to understand muscle overuse and possible risk of injury leading to musculoskeletal disorders. Current fatigue models are not suitable for real-world settings as they are either validated using simulations or non-functional tasks. Moreover, models that capture the changes to muscle activity due to fatigue either assume a linear relationship between muscle activity and muscle force or utilize a simple muscle model. Personalised electromygraphy (EMG)-driven musculoskeletal models (pEMS) offer person-specific approaches to model muscle and joint kinetics during a wide repertoire of daily life tasks. These models utilize EMG, thus capturing central fatigue-dependent changes in multi-muscle bio-electrical activity. However, the peripheral muscle force decay is missing in these models. Thus, we studied the influence of fatigue on a large scale pEMS of the trunk. Eleven healthy participants performed functional asymmetric lifting task. Average peak body-weight normalized lumbosacral moments (BW-LM) were estimated to be 2.55 ± 0.26 Nm/kg by reference inverse dynamics. After complete exhaustion of the lower back, the pEMS overestimated the peak BW-LM by 0.64 ± 0.37 Nm/kg. Then, we developed a time-varying muscle force decay model resulting in a time-varying pEMS (t-pEMS). This reduced the difference between BW-LM estimated by the t-pEMS and reference to 0.49 ± 0.14 Nm/kg. We also showed that five fatiguing contractions are sufficient to calibrate the t-pEMS. Thus, this study presents a person and muscle specific model to track fatigue during functional tasks.

Trunk extensor muscle endurance and its relationship to action potential conduction velocity and spectral parameters estimated using high-density electromyography.

Trunk extensor muscle fatigue typically manifests as a decline in spectral content of surface electromyography. However, previous research on the relationship of this decline with trunk extensor muscle endurance have shown inconsistent results. The decline of spectral content mainly reflects the decrease in average motor unit action potential conduction velocity (CV). We evaluated whether the rate of change in CV, as well as two approaches employing the change in spectral content, are related to trunk extensor muscle endurance. Fourteen healthy male participants without a low-back pain history performed a non-strictly controlled static forward trunk bending trial until exhaustion while standing. For 13 participants, physiologically plausible CV estimates were obtained from high-density surface electromyography bilaterally from T6 to L5. Laterally between L1 and L2, the linear rate of CV change was strongly correlated to endurance time (R2 = 0.79), whereas analyses involving the linear rate of change in spectral measures showed a lower (R2 = 0.38) or no correlation. For medial electrode locations, estimating CV and its relationship with endurance time was less successful, while the linear rate of change in spectral measures correlated moderately to endurance time (R2 = 0.44; R2 = 0.56). This study provides guidance on monitoring trunk extensor muscle fatigue development using electromyography.

Low back muscle action potential conduction velocity estimated using high-density electromyography.

While a decreasing spectral content of surface electromyography reflects low back muscle fatigue development, reliability of these decreases may be insufficient. Decreasing frequency content is largely determined by decreasing average motor unit action potential conduction velocities (CV), which is considered a more direct measure of muscle fatigue development. However, for the low back muscles it has been proven difficult to identify propagating potentials and consequently estimate the CV. The aim of this study was to estimate the low back muscle CV from high-density multi-channel electromyography by using peak-delay and cross-correlation methods. Fourteen healthy male participants without a history of low-back pain performed a 30 degrees lumbar flexion trial until exhaustion while standing. For 10 out of the 14 participants (118 out of 560 sites) realistic CV estimates were obtained using both methods, the majority likely over the iliocostalis lumborum muscle. Between-method CV differences appeared to be small. Close to the spine a considerable number of sites (79) yielded systematically overestimated low back muscle CV values. Estimating low back muscle CV may allow additional insight into low back muscle fatigue development and potentially improve its monitoring using (high-density) surface electromyography.

Muscle activity and architecture as a predictor of hand-grip strength.

BACKGROUND: Grip strength is a powerful predictor of disability as well as a good indicator of physical activity. OBJECTIVES: This study aimed to relate ultrasound (US) and electromyography (EMG) simultaneously to maximum hand-grip strength during an isometric contraction. APPROACH: This is a cross-sectional study. Data acquisition was done with a dynamometer, US and EMG. Outcome variables included maximum strength during the hand-grip gesture, maximum muscle activity and change in muscle thickness. A non-linear regression analysis was performed to analyse the relationship between all outcome variables. MAIN RESULTS: A total of 38 subjects (18 men and 20 women) participated in the study. The mean results for hand-grip strength were 25.50 (SD 6.55) kg of maximum strength, a change in muscle thickness of 1.83 (SD 0.75) mm and an EMG activity of 499.29 (SD 224.20) µV. Hand-grip strength had a high correlation with muscle thickness (R 2 = 0.61) and EMG activity (R 2 = 0.95). The correlation between maximum muscle activity and change in muscle thickness was R 2 = 0.83. SIGNIFICANCE: The results of the present study demonstrate that this new method based on electromyographic activity and muscule architecture could be important in the development of the hand-grip test.