Wearable Technologies 是第一本专门发表可穿戴设备相关原创研究、评论以及行业进展的期刊。可穿戴技术在康复、工业、环境/勘探、伤害预防、军事、医疗诊断、动物、运动及休闲等领域均发挥着重要作用，而本期刊旨在为可穿戴技术所有相关领域研究提供一个交流平台，重点关注可穿戴技术的设计、控制和力学。期刊涵盖主题包括但不限于：外骨骼、机器护甲、假肢、智能矫正装置、软性可穿戴机器人、新型可穿戴传感器技术和可穿戴显示器的开发、机电一体化、人机交互、人机闭环优化以及物理人机交互控制器。
注意：本期刊不接收讨论临床试验结果的相关论文
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Exofworkethlon：评估工业外骨骼的前瞻性研究方法
Exoworkathlon: A prospective study approach for the evaluation of industrial exoskeletons
VerenaKopp, Mirjam Holl, Marco Schalk, Urban Daub, Enrique Bances, Braulio García,Ines Schalk, Jörg Siegert and Urs Schneider
# exoskeletons’evaluation
# occupationalhealth
# realisticworking tasks
摘要：
Industrialexoskeletons have recently gained importance as ergonomic interventions forphysically demanding work activities. The growing demand for exoskeletons isleading to a need for new knowledge on the effectiveness of these systems. TheExoworkathlon, as a prospective study approach, aims to assess exoskeletons inrealistic use cases and to evaluate them neutrally in their entirety. For thispurpose, a first set of four realistic Parcours was developed with experts fromrelevant industries, the German Social Accident Insurance, and the FederalInstitute for Occupational Safety and Health. In addition, a set of ratings wasdefined to assess subjective user feedback, work quality, and objectivephysiological parameters. Exoworkathlon aims to bring together developers,researchers, and end-users, strengthen collaborative exchanges, and promote aplatform for the prospective holistic data collection for exoskeletonevaluation. In this article, the focus is on the background and methodology ofExoworkathlon.
原文链接：
https://doi.org/10.1017/wtc.2022.17
扫码快速阅读原文：
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基于肌电图的汽车装配过程中上体外骨骼疲劳评估
Electromyography-based fatigue assessment of an upper body exoskeleton during automotive assembly
JasonC. Gillette, Shekoofe Saadat and Terry Butler
# overhead work
# shoulder
# threshold limit values
摘要：
The purpose of this study was to assess an upper body exoskeleton during automotive assembly processes that involve elevated arm postures. Sixteen team members at Toyota Motor Manufacturing Canada were fitted with a Levitate Airframe, and each team member performed between one and three processes with and without the exoskeleton. A total of 16 assembly processes were studied. Electromyography (EMG) data were collected on the anterior deltoid, biceps brachii, upper trapezius, and erector spinae. Team members also completed a usability survey. The exoskeleton significantly reduced anterior deltoid mean active EMG amplitude (p = .01, Δ = −3.2 %MVC, d = 0.56 medium effect) and fatigue risk value (p < .01, Δ = −5.1 %MVC, d = 0.62 medium effect) across the assembly processes, with no significant changes for the other muscles tested. A subset of nine assembly processes with a greater amount of time spent in arm elevations at or above 90° (30 vs. 24%) and at or above 135° (18 vs. 9%) appeared to benefit more from exoskeleton usage. For these processes, the exoskeleton significantly reduced anterior deltoid mean active EMG amplitude (p < .01, Δ = −5.1 %MVC, d = 0.95 large effect) and fatigue risk value (p < .01, Δ = −7.4 %MVC, d = 0.96 large effect). Team members responded positively about comfort and fatigue benefits, although there were concerns about the exoskeleton hindering certain job duties. The results support quantitative testing to match exoskeleton usage with specific job tasks and surveying team members for perceived benefits/drawbacks.
原文链接：
https://doi.org/10.1017/wtc.2022.20
扫码快速阅读原文：
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利用深度神经网络对经股截肢者的电动假腿进行无缝直观控制
Seamless and intuitive control of a powered prosthetic leg using deep neural network for transfemoral amputees
Minjae Kim, Ann M. Simon and Levi J. Hargrove
# ambulation modes
# deep learning
# impedance control
# an open-source bionic leg
# prosthetics
摘要：
Powered prosthetic legs are becoming a promising option for amputee patients. However, developing safe, robust, and intuitive control strategies for powered legs remains one of the greatest challenges. Although a variety of control strategies have been proposed, creating and fine-tuning the system parameters is time-intensive and complicated when more activities need to be restored. In this study, we developed a deep neural network (DNN) model that facilitates seamless and intuitive gait generation and transitions across five ambulation modes: level-ground walking, ascending/descending ramps, and ascending/descending stairs. The combination of latent and time sequence features generated the desired impedance parameters within the ambulation modes and allowed seamless transitions between ambulation modes. The model was applied to the open-source bionic leg and tested on unilateral transfemoral users. It achieved the overall coefficient of determination of 0.72 with the state machine-based impedance parameters in the offline testing session. In addition, users were able to perform in-laboratory ambulation modes with an overall success rate of 96% during the online testing session. The results indicate that the DNN model is a promising candidate for subject-independent and tuning-free prosthetic leg control for transfemoral amputees.
原文链接：
https://doi.org/10.1017/wtc.2022.19
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