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协作项目，开发一种个性化的下肢辅助可穿戴机器人，该机器人可以在举重等体力密集型活动中减少人类的体力。该机器人通过使用软可穿戴电子设备感知用户的体力，并做出相应的响应来减少体力。这项工作在工厂劳动重量方面具有重要的应用，因为过度劳累伤害不仅会造成高昂的成本，而且经常导致残疾。这项研究调查了这种可穿戴机器人策略在减少人力方面的有效性，并制定了提高可穿戴设备利用率的策略。使用基于各种指标的估计方法来定制对每个人类佩戴者的机器人辅助。该估计方法还可用于设计使用可穿戴机器人的训练方法。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独立研究和论文研究的指导，将研究和教育结合起来。教育基础设施和研究团队成员将通过帮助维护可穿戴机器人测试台来广泛传播研究成果，以实现可穿戴机器人个性化辅助的可定制性目标。通过确定替代优化标准来有效估计用户在体力密集活动期间的体力消耗，可以通过使用传统的生理传感器（例如肌肉活动传感器）快速而可靠地估计用户的体力消耗来实现，然后进行估计。使用新型软可穿戴设备其次，这项工作将增强软可穿戴电子设备，目标是改进和替代传统传感器。相关任务将探索使用现有软可穿戴电子设备作为传感器的可行性，然后迭代改进电子设备和估计方法以准确地感知和测量。第三，该研究将整合和评估在体力密集型活动（例如使用脚踝外骨骼举重）中使用软可穿戴传感器测量实现的个性化帮助，该任务将使用适当的指标，例如任务的能量消耗率和能量消耗率。肌肉活动。将导致可穿戴机器人为体力密集型活动提供定制（个性化）帮助，并使用软可穿戴传感器系统来评估用户的身体状态并提供实时反馈。评估结果可应用于缓解或预防的干预措施。现有的危害及其对工人造成的伤害；因此，这项研究的结果将使工厂、仓库和其他工业工作场所的工人受益。该提案由疾病中心的国家职业安全与健康研究所 (NIOSH) 资助控制与预防（CDC）。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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

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

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

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