多功能和非多功能职业背部支撑外骨骼:实验室和现场研究的比较

IF 3.4 Q2 ENGINEERING, BIOMEDICAL Wearable technologies Pub Date : 2021-09-21 eCollection Date: 2021-01-01 DOI:10.1017/wtc.2021.9
Tommaso Poliero, Matteo Sposito, Stefano Toxiri, Christian Di Natali, Matteo Iurato, Vittorio Sanguineti, Darwin G Caldwell, Jesús Ortiz
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引用次数: 0

摘要

摘要职业背部支撑外骨骼的辅助策略主要集中在举重任务上。然而,在职业场景中,重要的是不仅要考虑起重,还要考虑其他活动。这可以利用人类活动识别算法来实现,该算法可以识别用户正在执行的任务并触发适当的辅助策略。我们将这种能力称为外骨骼的多功能性。为了评估多功能性,我们建议同时关注设备减少肌肉激活的能力(功效)和它与用户的互动(动态贴合)。为此,我们进行了一项实验研究,涉及10美元的健康受试者,他们复制了一家制造厂的工作活动。为了比较多功能和非多功能外骨骼,我们的设备XoTrunk采用了两种不同的策略进行控制。相应地,我们收集了肌肉活动、运动学变量和用户的主观反馈。此外,我们还评估了该设备的任务识别性能。结果表明,XoTrunk在举重和负重时能够减少高达40美元和30美元的肌肉激活。然而,非通用控制策略阻碍了用户的自然步态(例如,髋关节屈曲减少$-24\%$),这可能会降低外骨骼的接受度。检测携带活动并调整控制策略,导致步态更加自然(例如,髋关节屈曲增加$+9\%$)。本文分析的分类器显示出良好的性能(在线准确率>91%)。最后,我们进行了9个小时的现场测试,涉及4名用户。关于外骨骼多功能性的初步主观反馈,在本工作的最后给出。
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Versatile and non-versatile occupational back-support exoskeletons: A comparison in laboratory and field studies.

Assistive strategies for occupational back-support exoskeletons have focused, mostly, on lifting tasks. However, in occupational scenarios, it is important to account not only for lifting but also for other activities. This can be done exploiting human activity recognition algorithms that can identify which task the user is performing and trigger the appropriate assistive strategy. We refer to this ability as exoskeleton versatility. To evaluate versatility, we propose to focus both on the ability of the device to reduce muscle activation (efficacy) and on its interaction with the user (dynamic fit). To this end, we performed an experimental study involving healthy subjects replicating the working activities of a manufacturing plant. To compare versatile and non-versatile exoskeletons, our device, XoTrunk, was controlled with two different strategies. Correspondingly, we collected muscle activity, kinematic variables and users' subjective feedbacks. Also, we evaluated the task recognition performance of the device. The results show that XoTrunk is capable of reducing muscle activation by up to in lifting and in carrying. However, the non-versatile control strategy hindered the users' natural gait (e.g., reduction of hip flexion), which could potentially lower the exoskeleton acceptance. Detecting carrying activities and adapting the control strategy, resulted in a more natural gait (e.g., increase of hip flexion). The classifier analyzed in this work, showed promising performance (online accuracy > 91%). Finally, we conducted 9 hours of field testing, involving four users. Initial subjective feedbacks on the exoskeleton versatility, are presented at the end of this work.

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