MRI: Acquisition of Biomechanical Movement Baselining Technology Suite for Wearable Technology and Stretch Sensor-based Validation of Lower Body Characteristics

MRI：获取用于可穿戴技术的生物力学运动基线技术套件以及基于拉伸传感器的下半身特征验证

• 批准号: 2215776
• 负责人: Reuben Burch
• 金额: $ 53.9万
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2215776&HistoricalAwards=false
• 关键词: MRI; Acquisition; Biomechanical; Movement; Baselining

Human performance engineering at Mississippi State University, conducted by a large, interdisciplinary team, representing diverse researchers and practitioners in academics, athletics, & industry, develops and validates wearable technologies that can be used to capture human movement in realistic environments. Testing in labs is critical for knowledge advancement. The instrument suite consists of state-of-the-art Gait Real-time Analysis Interactive Lab (GRAIL) system and surface electromyography (sEMG) sensors for human performance assessment. The broader impacts of the projects arising with use of the proposed technology suite will increase research capacity and permit the crossing of scalability barriers for current wearable technology prototypes developed through the human performance engineering research and development program. This human performance laboratory suite will empower interdisciplinary researchers to advance the health and welfare of the American public through new types of wearable technology solutions and technology validation. The success of such research will enhance existing partnerships between MSU and athletic, industrial, military, and medical partners while further growing the infrastructure for research and education with an interdisciplinary focus.The acquired system build supports research of the human performance engineering team, including a project entitled “From the Ground Up – Using Soft Robotic Sensors (SRS) to Create a Foot and Ankle Wearable that Accurately Captures Real-time, Kinematic and Kinetic Data During Athletic Training” by extending the capability of current instrumentation. The combined GRAIL and sEMG system provides a more accurate, convenient, and safe platform for the generation of large data sets, especially with clinically at-risk populations. Activities enabled by the instrument suite advance transformative research within the field of human factors engineering by validating Soft Robotic Sensors (SRS) as a motion capture tool for all lower limb segments in the human body. The instrument suite will allow for more control and repeatability during participant testing of complex, dynamic movements (gait, slip/trip perturbations), and will enable a much faster data collection process, allowing the research team to properly utilize state-of-the-art deep learning approaches requiring large amounts of data. Secondary transformative research areas include: (1) Gait-based disabilities in pediatrics, (2) Chronic ankle instability, (3) fall prevention, (4) fatigue and unsafe behavior identification systems, and (5) autonomous vehicles including pedestrians and their behaviors around autonomy.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.

密西西比州立大学的人体表现工程由一个大型跨学科团队进行，代表学术界、体育界和工业界的不同研究人员和从业者，开发和验证可用于捕捉现实环境中人体运动的可穿戴技术。实验室测试对于知识进步至关重要。该仪器套件由最先进的步态实时分析交互式实验室 (GRAIL) 系统和用于人体表现评估的表面肌电 (sEMG) 传感器组成。使用拟议技术套件所产生的项目的更广泛影响将提高研究能力，并允许跨越通过人体表现工程研究和开发计划开发的当前可穿戴技术原型的可扩展性障碍。该人类表现实验室套件将使跨学科研究人员能够通过新型可穿戴技术解决方案和技术验证来促进美国公众的健康和福利。此类研究的成功将加强密歇根州立大学与体育、工业、军事和医疗合作伙伴之间的现有合作伙伴关系，同时进一步发展具有跨学科重点的研究和教育基础设施。所获得的系统构建支持人类表现工程团队的研究，包括一个题为“从头开始——使用软机器人传感器（SRS）创建足部和踝部可穿戴设备，在运动过程中准确捕捉实时、运动学和动力学数据”的项目。 通过扩展当前仪器的功能来进行运动训练”。 GRAIL 和 sEMG 系统相结合，为生成大型数据集（尤其是临床高危人群）提供了更准确、方便和安全的平台。该仪器套件支持的活动通过验证软机器人传感器（SRS）作为人体所有下肢节段的运动捕捉工具，推进了人因工程学领域的变革性研究。该仪器套件将允许在复杂的动态运动（步态、滑动/行程扰动）的参与者测试过程中实现更多的控制和可重复性，并将实现更快的数据收集过程，使研究团队能够正确利用需要大量数据的最先进的深度学习方法。次要变革性研究领域包括：(1) 儿科基于步态的残疾，(2) 慢性踝关节不稳定，(3) 跌倒预防，(4) 疲劳和不安全行为识别系统，以及 (5) 包括行人在内的自动驾驶车辆及其围绕自主的行为。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进行评估，被认为值得支持 审查标准。