Collaborative Research: NRI: INT: Customizable Lower-Limb Wearable Robot using Soft-Wearable Sensor to Assist Occupational Workers

该奖项支持一个综合性的合作项目，以开发个性化的下肢辅助可穿戴机器人，减少人类在体力密集型活动中的努力，如举重。该机器人的工作原理是使用可穿戴的软电子设备来感知用户的身体努力，并相应地做出反应以减少这种努力。 这项工作在涉及重量的工厂劳动中有重要的应用，因为过度用力伤害既昂贵又经常致残。 本研究探讨了这种可穿戴机器人策略在减少人类工作量方面的有效性，并制定了提高可穿戴机器人和软可穿戴电子产品利用率的策略。使用基于各种度量的估计方法来定制对每个个体人类穿戴者的机器人辅助。该估计方法也可以用于设计使用可穿戴机器人的训练方法。软可穿戴传感器将用于机器人以及与诊断，监测和治疗相关的医疗应用。拟议的项目通过开发基于项目的可穿戴机器人课程，并支持研究生和本科生在独立研究和论文研究中的指导，将研究和教育结合起来。该项目通过帮助维护可穿戴机器人试验台来加强教育和研究的基础设施。研究成果将通过出版物、软件和数据集广泛传播。研究小组成员有三个目标，有助于可穿戴机器人个性化援助的可定制性的目标。首先，定制过程将通过识别替代优化标准来改进，以有效地估计用户在体力密集型活动期间的体力。这将通过使用常规生理传感器（诸如肌肉活动传感器）对用户努力进行快速且稳健的估计，然后使用新的软可穿戴电子器件进行估计来实现。其次，这项工作将增强软可穿戴电子产品，目标是改进和取代传统传感器。相关的任务将探索使用现有的软可穿戴电子设备作为传感器的可行性，然后迭代地改进电子设备和估计方法，以准确地感知和估计生理状态。第三，该研究将整合和评估在体力密集型活动中使用软可穿戴传感器测量所实现的个性化辅助，例如使用脚踝外骨骼进行举重。该任务将使用适当的指标，例如任务的能量消耗率和肌肉活动。这项工作将导致定制（个性化）援助可从可穿戴机器人的身体密集型活动和软可穿戴传感器系统，以评估用户的身体状况，并提供实时反馈。评价结果可用于采取干预措施，以减轻或防止现有危害和由此对工人造成的伤害;因此，这项研究的结果将有利于在工厂，仓库，该提案由疾病控制和预防中心（CDC）的国家职业安全与健康研究所（NIOSH）资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award supports an integrative, collaborative project to develop a personalized lower-limb assistive wearable robot that reduces human effort during physically intensive activities, such as lifting. The robot works by sensing a user’s physical effort using soft-wearable electronics and responding accordingly to reduce this effort. This work has significant applications in factory labor involving weights, as overexertion injuries are both costly and frequently disabling. This research investigates the effectiveness of this wearable robot strategy in reducing human effort and develops strategies to improve the utilization of wearable robots and soft wearable electronics. The robot assistance to each individual human wearer is customized using an estimation method based on various metrics. The estimation method can also be used to design a training method using a wearable robot. The soft wearable sensors will be useful in robotics as well as medical applications related to diagnosis, monitoring, and therapeutics. The proposed project integrates research and education by developing a project-based course on wearable robotics and supporting graduate and undergraduate student mentoring in independent research and thesis studies. The project strengthens the infrastructure for education and research by helping maintain wearable robot testbeds. The research results will be broadly disseminated through publications, software, and data sets. The research team members have three objectives that contribute to the goal of customizability in wearable robot personalized assistance. First, the customization process will be improved by identifying alternative optimization criteria to efficiently estimate the user’s physical effort during physically intensive activities. This will be accomplished through a rapid and robust estimate of the user effort using a conventional physiological sensor, such as a muscle activity sensor, followed by an estimate using new soft wearable electronics. Second, the work will enhance soft-wearable electronics with the goal of improving on and replacing conventional sensors. Associated tasks will explore the feasibility of using existing soft wearable electronics as sensors and then iteratively improve the electronics and estimation method to accurately sense and estimate physiological status. Third, the study will integrate and evaluate the personalized assistance achieved using soft wearable sensor measurements in a physically intensive activity, such as lifting using an ankle exoskeleton. This task will use appropriate metrics such as energy expenditure rate of the task and muscle activity. The work will result in customized (personalized) assistance available from a wearable robot for physically intensive activities and a soft wearable sensor system to evaluate the physical status of the user and provide real-time feedback. The evaluation outcomes can be applied in interventions to mitigate or prevent existing hazards and resulting injuries to workers; thus, the results of this research will benefit human laborers in factories, warehouses, and other industrial workplaces.This proposal was funded with the National Institute for Occupational Safety and Health (NIOSH) in the Center for Disease Control and Prevention (CDC).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.