Modeling the metabolic reductions of a passive back-support exoskeleton.

Despite several attempts to quantify the metabolic savings resulting from the use of passive back-support exoskeletons (BSEs), no study has modeled the metabolic change while wearing an exoskeleton during lifting. The objectives of this study were to: 1) quantify the metabolic reductions due to the VT-Lowe's exoskeleton during lifting; and 2) provide a comprehensive model to estimate the metabolic reductions from using a passive BSE. In this study, 15 healthy adults (13 males, 2 females) of ages 20-34 yr (mean = 25.33, SD = 4.43) performed repeated freestyle lifting and lowering of an empty box and a box with 20% of their bodyweight. Oxygen consumption and metabolic expenditure data were collected. A model for metabolic expenditure was developed and fitted with the experimental data of two prior studies and the without-exoskeleton experimental results. The metabolic cost model was then modified to reflect the effect of the exoskeleton. The experimental results revealed that VT-Lowe's exoskeleton significantly lowered the oxygen consumption by ∼9% for an empty box and 8% for a 20% bodyweight box, which corresponds to a net metabolic cost reduction of ∼12% and ∼9%, respectively. The mean metabolic difference (i.e., without-exo minus with-exo) and the 95% confidence interval were 0.36 and (0.2-0.52) W/kg for 0% body weight and 0.43 and (0.18-0.69) W/kg for 20% body weight. Our modeling predictions for with-exoskeleton conditions were precise, with absolute freestyle prediction errors of <2.1%. The model developed in this study can be modified based on different study designs, and can assist researchers in enhancing designs of future lifting exoskeletons.NEW & NOTEWORTHY We present a new model of the metabolic cost of repetitive lifting, and how that is affected by wearing a passive back support exoskeleton. We compute the effective biomechanical efficiencies of moving the body and a carried load during lifting, and determine the effect of an exoskeleton's efficiency on its metabolic reduction. This model is useful for understanding the effects of exoskeletons on the body and for designing future exoskeletons.

Motion Inference Using Sparse Inertial Sensors, Self-Supervised Learning, and a New Dataset of Unscripted Human Motion.

In recent years, wearable sensors have become common, with possible applications in biomechanical monitoring, sports and fitness training, rehabilitation, assistive devices, or human-computer interaction. Our goal was to achieve accurate kinematics estimates using a small number of sensors. To accomplish this, we introduced a new dataset (the Virginia Tech Natural Motion Dataset) of full-body human motion capture using XSens MVN Link that contains more than 40 h of unscripted daily life motion in the open world. Using this dataset, we conducted self-supervised machine learning to do kinematics inference: we predicted the complete kinematics of the upper body or full body using a reduced set of sensors (3 or 4 for the upper body, 5 or 6 for the full body). We used several sequence-to-sequence (Seq2Seq) and Transformer models for motion inference. We compared the results using four different machine learning models and four different configurations of sensor placements. Our models produced mean angular errors of 10-15 degrees for both the upper body and full body, as well as worst-case errors of less than 30 degrees. The dataset and our machine learning code are freely available.

Quantification of Postures for Low-Height Object Manipulation Conducted by Manual Material Handlers in a Retail Environment.

Occupational Applications Manual material handlers performing stocking tasks spent substantial amounts of time in bent postures but used traditional stoops and squats infrequently. Instead, they often used split-legged stoops and squats, where one foot is further forward than the other, and one-legged ("golfer's") lifts. During object manipulation, the distance workers reached away from their body, and the height at which they manipulated objects, were correlated with the posture used by the worker. Workers also stayed in different postures for different lengths of time. It is likely that certain postures are more comfortable for the workers to remain in, provide additional mobility or operational radius, or require less energy to use. Understanding these factors in more detail could lead to improved worker training programs, where the postures taught not only have low injury risk but are comfortable so are actually adopted and used by the workers.

Technical Abstract Background Musculoskeletal disorders are relatively common among manual material handlers. This may be due in part to challenging postures used by workers. Purpose Studying the kinematics of manual material handlers in the workplace will provide quantitative data on how they move and what postures they adopt. With these data, some insights can be determined about why workers chose certain postures. Methods We conducted an on-site workplace study to capture the full-body kinematics of manual material handlers (stockers) using inertial measurement units. We organized the observed bends into six classes: stooping, fore-aft squatting, split-legged stooping with one-heel raised, split-legged stooping with no heels raised, one-legged lifting, and mixed lifting, which include multiple forms while remaining bent. These classes were based on a new general classification of bending and lifting postures that we developed, which enumerates all of the possible forms. We quantified how frequently and for what duration the workers bent and lifted, and determined how often they performed asymmetric motions while bending. We determined the range of motion of the hand positions during each bent posture, which provides a measure of the workspace afforded by the posture. Results Workers rarely used symmetric squats and infrequently used symmetric stoops typically studied in lab settings. Instead, they used a variety of different postures that have not been well-characterized. Of the 666 bending postures recorded during the experiment, 27.3% were stoops lifts, 22.1% were one-legged lifts, 20.3% were split-legged bends with both heels on the ground, and 12.3% were split-legged bends with a heel raised. Only 4.6% of the postures were squats and only one participant used this posture. Different bending postures were correlated with different ranges of hand position used in object manipulation. One-legged lifting corresponded to bends with the hands furthest away from the body along the sagittal axis. Conclusions While our study was exploratory, we observed many kinematic forms that have not been studied much in the past, such as split-legged stooping and one-legged lifting, suggesting that future work should be done to understand the biomechanics of these postures.

A passive exoskeleton reduces peak and mean EMG during symmetric and asymmetric lifting.

The VT-Lowe's exoskeleton is a novel passive lift-assistive device designed to offload the back muscles during repetitive lifting. In this study, the effect of the exoskeleton on electromyographic (EMG) signals was investigated in four different lifting types (stoop, squat, freestyle and asymmetric) and two box weights (0% and 20% of body weight). Twelve young healthy adults ages 18-31 years (mean = 22.75, SD = 4.35) were participants. The EMG signals for twelve muscles (iliocostalis erector spinae (IL), longissimus erector spinae (LT), multifidus (MF), bicep femoris (BF), vastus lateralis (VL) and abdominal external oblique (AEO) muscles) were measured. The exoskeleton significantly decreased the peak and mean activity of back muscles (IL and LT) by 31.5% and 29.3%, respectively, for symmetric lifts and by 28.2% and 29.5%, respectively, for asymmetric lifts. The peak and mean EMG of leg muscles were significantly reduced by 19.1% and 14.1% during symmetric lifts, and 17.4% and 14.6% during asymmetric lifts. Although the exoskeleton reduced the activation of back and leg muscles, it slightly increased the activity of external oblique muscles, although this was not statistically significant. In conclusion, the exoskeleton is promising as a lift-assist device for manual material handlers and workers performing repetitive lifting.