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

合作研究：NRI：INT：使用软穿戴传感器协助职业工人的可定制下肢可穿戴机器人

• 批准号: 2024863
• 负责人: Myunghee Kim
• 金额: $ 44.92万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024863&HistoricalAwards=false
• 关键词: Collaborative; Research; NRI; INT; Customizable

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.

该奖项支持一个综合的协作项目，以开发一个个性化的下limb辅助可穿戴机器人，该机器人在身体强化活动（例如提升）中减少人类的努力。该机器人使用可软磨性电子设备来感知用户的体力工作，并做出相应的响应以减少这项工作。这项工作在工厂劳动中有重大应用涉及重量，因为过度劳动既昂贵又经常残疾。这项研究调查了这种可穿戴机器人策略在减少人类努力和制定策略以改善可穿戴机器人和柔软可穿戴电子产品的策略方面的有效性。使用基于各种指标的估算方法对每个人佩戴者的机器人援助进行定制。估算方法还可以用于使用可穿戴机器人设计训练方法。柔软的可穿戴传感器将在机器人以及与诊断，监测和治疗相关的医疗应用中有用。拟议的项目通过开发有关可穿戴机器人的项目课程，并在独立研究和论文研究中为毕业生和本科生的心理研究来整合研究和教育。该项目通过帮助维护可穿戴机器人测试床来实现教育和研究的基础设施。研究结果将通过出版物，软件和数据集广泛传播。研究小组成员有三个目标，可以促进可穿戴机器人个性化帮助的可定制性。首先，通过确定替代优化标准以有效地估算用户在物理密集的活动中的体育努力来改善自定义过程。这将通过使用传统的物理传感器（例如肌肉活动传感器）对用户努力进行快速且可靠的估计来实现，然后使用新的软可穿戴电子设备进行估计。其次，这项工作将增强可软磨性电子设备，以改善和替换传统传感器。相关的任务将探索使用现有的柔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算方法，以准确感知和估计物理状态。第三，这项研究将在物理强度的活动中使用柔软的可穿戴传感器测量结果整合和评估实现的个性化辅助，例如使用脚踝外骨骼提升。此任务将使用适当的指标，例如任务和肌肉活动的能量消耗率。这项工作将为可穿戴机器人提供定制的（个性化的）帮助，以进行身体密集的活动和柔软的可穿戴传感器系统，以评估用户的身体状况并提供实时反馈。可以在干预措施中应用评估结果，以减轻或防止现有的危害并导致工人受伤；因此，这项研究的结果将使工厂，仓库和其他工业工作中的人类劳动者受益。该提案是由国家职业安全与健康研究所（NIOSH）在疾病控制与预防中心（CDC）中资助的。该奖项反映了NSF的法定任务，并通过评估了基金会的智力效果，并通过评估了Crricatial Merit和Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and Broadia and broaderia and Broadise and broader and and and and and and anderer。

项目成果

Soft wearable flexible bioelectronics integrated with an ankle-foot exoskeleton for estimation of metabolic costs and physical effort

• DOI: 10.1038/s41528-023-00239-2
• 发表时间: 2023-01-25
• 期刊: NPJ FLEXIBLE ELECTRONICS
• 影响因子: 14.6
• 作者: Kim, Jihoon;Kantharaju, Prakyath;Yeo, Woon-Hong
• 通讯作者: Yeo, Woon-Hong

Reducing Squat Physical Effort Using Personalized Assistance From an Ankle Exoskeleton

• DOI: 10.1109/tnsre.2022.3186692
• 发表时间: 2022-06-27
• 期刊: IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
• 影响因子: 4.9
• 作者: Kantharaju, Prakyath;Jeong, Hyeongkeun;Kim, Myunghee
• 通讯作者: Kim, Myunghee

Phase-Plane Based Model-Free Estimation of Steady-State Metabolic Cost

• DOI: 10.1109/access.2022.3205629
• 发表时间: 2022-01-01
• 期刊: IEEE ACCESS
• 影响因子: 3.9
• 作者: Kantharaju, Prakyath;Kim, Myunghee
• 通讯作者: Kim, Myunghee

Evaluation of Lower Limb Exoskeleton for Improving Balance during Squatting Exercise using Center of Pressure Metrics

• DOI: 10.1177/1071181322661447
• 发表时间: 2022-09
• 期刊: Proceedings of the Human Factors and Ergonomics Society Annual Meeting
• 作者: Sruthi Ramadurai;Michael Jacobson;Prakyath Kantharaju;Hyeon-Seong Jeong;Hee-seon Jeong;Myunghee Kim