Removing energy with an exoskeleton reduces the metabolic cost of walking

Shepertycky, Michael; Burton, Sarah; Dickson, Andrew; Liu, Yanfei; Li, Qingguo

doi:10.1126/science.aba9947

Cited by 68 publications

(59 citation statements)

References 84 publications

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“…We found that full-time workers exposed to physical job demands are at higher risk for burnout. This result and the previously found adverse health effects of physically demanding work (Coenen et al 2020;Holtermann et al 2018) highlight the importance of organizational preventive measures such as supervisor support (Clays et al 2016), decision authority, skill discretion (Viotti et al 2017), workplace strength training (Sundstrup et al 2020), and exoskeletons (Shepertycky et al 2021) to buffer the negative effects of high physical job demands. Further, to prevent incomplete recovery from work, it seems advisable that full-time working employees with physically demanding work do not overly engage in recreational physical activities during non-work time.…”

Section: Practical Implicationsmentioning

confidence: 66%

The physical activity paradox: a longitudinal study of the implications for burnout

Vries

Bakker

2021

Int Arch Occup Environ Health

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Purpose This study investigates the independent and interactive associations of physical job demands and three types of off-job physical activity (during transportation, household, and recreation) with burnout. We use a recently proposed new conceptualization and assessment of burnout including core and secondary burnout symptoms. We predicted that physical job demands would be positively and the three types of off-job physical activity would be negatively related to burnout. Further, we hypothesized that the negative relations between the three types of off-job physical activity and burnout would be stronger for employees with low (vs. high) physical job demands. Methods To test our hypotheses, we conducted a two-wave survey study among a heterogeneous sample of full-time workers (N = 355), using a longitudinal design with a half-year time lag. We tested cross-sectional, prospective and longitudinal relations. Results Hierarchical regression analyses partly confirmed our predictions. Cross-sectionally and prospectively, it was shown that physical job demands were positively related to burnout symptoms. In addition, off-job physical activity was negatively related to primary and secondary burnout symptoms among employees with low physical job demands and positively related to burnout symptoms among employees with high physical job demands. However, these relationships disappeared when investigated longitudinally. Conclusion Together, these findings suggest that not all off-job physical activities can prevent burnout, and that potential positive effects of physical activity during off-job time may depend on employees’ physical activity level at work.

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Section: Practical Implicationsmentioning

confidence: 66%

The physical activity paradox: a longitudinal study of the implications for burnout

Vries

Bakker

2021

Int Arch Occup Environ Health

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“…Finally, outcomes of our predictive model could be used to inform investments into future performance optimization or enhancement techniques and technologies (Bostrom and Sandberg, 2009;Colzato, 2018;Feltman et al, 2019;Brunyé et al, 2020). For example, if physical encumbrance and exertion is particularly influential for sustained performance across domains of interest, it may warrant increased investment in strength and endurance training, lower-extremity exoskeletons, or other approaches for reducing physical and physiological burden of continuous operations (Gregorczyk et al, 2010;Ozaki et al, 2013;Scribbans et al, 2016;Seo et al, 2018;Wei et al, 2020;Shepertycky et al, 2021). The same might be said for sleep or stress, identifying methods for increasing the quality or duration of naps and overnight sleep (Irwin et al, 2008;Ketz et al, 2018;Robinson et al, 2018), and/or reducing the intensity of stress responses (Stanley et al, 2011;Jaremko and Meichenbaum, 2013).…”

Section: Software Tool Implementationmentioning

confidence: 99%

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach

et al. 2021

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Wearable technologies for measuring digital and chemical physiology are pervading the consumer market and hold potential to reliably classify states of relevance to human performance including stress, sleep deprivation, and physical exertion. The ability to efficiently and accurately classify physiological states based on wearable devices is improving. However, the inherent variability of human behavior within and across individuals makes it challenging to predict how identified states influence human performance outcomes of relevance to military operations and other high-stakes domains. We describe a computational modeling approach to address this challenge, seeking to translate user states obtained from a variety of sources including wearable devices into relevant and actionable insights across the cognitive and physical domains. Three status predictors were considered: stress level, sleep status, and extent of physical exertion; these independent variables were used to predict three human performance outcomes: reaction time, executive function, and perceptuo-motor control. The approach provides a complete, conditional probabilistic model of the performance variables given the status predictors. Construction of the model leverages diverse raw data sources to estimate marginal probability density functions for each of six independent and dependent variables of interest using parametric modeling and maximum likelihood estimation. The joint distributions among variables were optimized using an adaptive LASSO approach based on the strength and directionality of conditional relationships (effect sizes) derived from meta-analyses of extant research. The model optimization process converged on solutions that maintain the integrity of the original marginal distributions and the directionality and robustness of conditional relationships. The modeling framework described provides a flexible and extensible solution for human performance prediction, affording efficient expansion with additional independent and dependent variables of interest, ingestion of new raw data, and extension to two- and three-way interactions among independent variables. Continuing work includes model expansion to multiple independent and dependent variables, real-time model stimulation by wearable devices, individualized and small-group prediction, and laboratory and field validation.

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“…During exercise, the unpowered exoskeleton stores energy such as elastic potential energy to provide assistance for the next moment of exercise [13]. Even an unpowered exoskeleton (which designed by Michael et al) utilizes the collected energy to generate electricity and reduces metabolic consumption [14]. Compared to powered exoskeletons, unpowered exoskeletons are lighter.…”

Section: Introductionmentioning

confidence: 99%

A Novel Lightweight Wearable Soft Exosuit for Reducing the Metabolic Rate and Muscle Fatigue

Chen

Wang

et al. 2021

Biosensors

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Wearable robotic devices have been proved to considerably reduce the energy expenditure of human walking. It is not only suitable for healthy people, but also for some patients who require rehabilitation exercises. However, in many cases, the weight of soft exosuits are relatively large, which makes it difficult for the assistant effect of the system to offset the metabolic consumption caused by the extra weight, and the heavy weight will make people uncomfortable. Therefore, reducing the weight of the whole system as much as possible and keeping the soft exosuit output power unchanged, may improve the comfort of users and further reduce the metabolic consumption. In this paper, we show that a novel lightweight soft exosuit which is currently the lightest among all known powered exoskeletons, which assists hip flexion. Indicated from the result of experiment, the novel lightweight soft exosuit reduces the metabolic consumption rate of wearers when walking on the treadmill at 5 km per hour by 11.52% compared with locomotion without the exosuit. Additionally, it can reduce more metabolic consumption than the hip extension assisted (HEA) and hip flexion assisted (HFA) soft exosuit which our team designed previously, which has a large weight. The muscle fatigue experiments show that using the lightweight soft exosuit can also reduce muscle fatigue by about 10.7%, 40.5% and 5.9% for rectus femoris, vastus lateralis and gastrocnemius respectively compared with locomotion without the exosuit. It is demonstrated that decreasing the weight of soft exosuit while maintaining the output almost unchanged can further reduce metabolic consumption and muscle fatigue, and appropriately improve the users’ comfort.

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Removing energy with an exoskeleton reduces the metabolic cost of walking

Cited by 68 publications

References 84 publications

The physical activity paradox: a longitudinal study of the implications for burnout

The physical activity paradox: a longitudinal study of the implications for burnout

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach

A Novel Lightweight Wearable Soft Exosuit for Reducing the Metabolic Rate and Muscle Fatigue

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