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CAREER: Adaptive Sonification to Improve Balance during Everyday Mobility

职业：自适应可听化以改善日常移动中的平衡

基本信息

批准号：
1944207
负责人：
Antonia Zaferiou
金额：
$ 82.22万
依托单位：
Stevens Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1944207&HistoricalAwards=false
关键词：
CAREER Adaptive Sonification Improve Balance

项目摘要

This Faculty Early Career Development (CAREER) grant will use machine learning and wearable technology to identify balance and gait deficits in fall-prone older adults and to deliver personalized auditory biofeedback ("sonified biofeedback") designed to improve dynamic balance while walking. Turning has been linked to recurrent falls in older adults because it imposes mechanical conflict between balancing and changing direction. Falls in older adults frequently lead to injury and sometimes death. By some accounts, turning-while-walking can comprise up to 50 percent of steps taken in any given day. Retraining balance strategies used during turning-while-walking has potential to reduce fall risk. This project will identify relationships between person-specific balance strategies used during turning and other factors including physiological and cognitive capabilities - such as strength and capacity for spatial reasoning - as well as environmental factors such as the presence or absence of obstacles. This information will be used to design personalized auditory biofeedback tuned to convey information about movement kinematics and foot forces in a way that facilitates dynamic balance during walking. An initial set of experiments will test human perception and motor responses to sonified biofeedback of dynamic balance information. Additional experiments will test the ability of fall-prone older adults to use personalized biofeedback to improve dynamic balance during turns. This project advances the national health by developing a machine learning approach to the diagnosis of balance deficits in older adults as well as a novel sonified biofeedback approach to improving dynamic balance in that population. The project includes an education and outreach plan, including an innovative “Artist in Residence” program, that will expose underrepresented groups to cutting edge engineering and scientific research in a way that is engaging for engineers and young artists alike. This project tests the hypothesis that interactive sonified biofeedback can improve balance strategies used by fall-risk older adults during turning-while-walking. This project has three research objectives. In the first, the PI will conduct human subjects experiments to characterize person-specific relationships between dynamic balance strategies used during turns, physiological and cognitive capabilities, and environmental factors. Participants will walk and turn within the controlled setting of the research lab and outdoors while wearing technology that can sense and transmit movement kinematics and ground reaction forces in real-time. For the second objective, the PI will establish how sonification can be used to train specific balance strategies and the extent to which those strategies can be retained and used without dependency on concurrent biofeedback. Working in conjunction with a sound designer, the PI will develop soundscapes that sonify balance metrics during unconstrained body movements, evaluate their ability to modulate measures of stability in real-time during repeated training sessions, and test their ability to elicit long-lasting behavioral change. In the third objective, the PI will test the ability of machine learning models to diagnose person-specific balance deficits during turns compared to clinician diagnoses, and to design personalized biofeedback soundscapes that can mitigate clinician-diagnosed balance deficits. Here, the PI will use Factored Conditional Restricted Boltzman Machines to generate person-specific, turn-type specific, and deficit-specific models that will be able to generate personalized sonified biofeedback to improve balance during turns. This project will advance fundamental understanding of an intelligent machine can communicate intent or otherwise shape the behavior of its human user through physical interaction.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.

这项学院早期职业发展(Career)补助金将使用机器学习和可穿戴技术来识别容易摔倒的老年人的平衡和步态缺陷，并提供个性化的听觉生物反馈(“发声生物反馈”)，旨在改善行走时的动态平衡。转身与老年人反复跌倒有关，因为它会在平衡和改变方向之间造成机械冲突。老年人跌倒经常导致受伤，有时甚至死亡。根据一些说法，在任何一天中，边走边转身的步数可能占到步数的50%。边走边转身时使用的再培训平衡策略有可能降低跌倒的风险。这个项目将确定转弯过程中使用的特定人的平衡策略与其他因素之间的关系，包括生理和认知能力--如力量和空间推理能力--以及环境因素，如障碍的存在或不存在。这些信息将被用来设计个性化的听觉生物反馈，以传达关于运动、运动学和脚部力量的信息，以促进行走过程中的动态平衡。最初的一系列实验将测试人类对动态平衡信息的有声生物反馈的感知和运动反应。其他实验将测试容易摔倒的老年人在转弯时使用个性化生物反馈改善动态平衡的能力。该项目通过开发一种机器学习方法来诊断老年人的平衡缺陷，以及一种新的发声生物反馈方法来改善老年人的动态平衡，从而促进了国民健康。该项目包括一项教育和外展计划，其中包括一项创新的“艺术家驻场”计划，该计划将使未被充分代表的群体接触到尖端工程和科学研究，以吸引工程师和年轻艺术家。这个项目测试了一种假设，即交互式发声生物反馈可以改善有跌倒风险的老年人在走路时转身时使用的平衡策略。本项目有三个研究目标。首先，PI将进行人类受试者实验，以表征在转弯时使用的动态平衡策略、生理和认知能力以及环境因素之间的特定关系。参与者将在研究实验室和户外的受控环境中行走和转弯，同时佩戴可以实时感知和传输运动运动学和地面反作用力的技术。对于第二个目标，PI将确定如何使用发声来训练特定的平衡策略，以及在多大程度上可以保留和使用这些策略，而不依赖于同时进行的生物反馈。PI将与音响设计师合作开发音景，以在不受限制的身体运动中使平衡指标发声，评估他们在重复训练期间实时调整稳定性指标的能力，并测试他们引发长期行为变化的能力。在第三个目标中，PI将测试机器学习模型的能力，以与临床医生诊断相比，在轮流中诊断个人特定的平衡缺陷，并设计个性化的生物反馈音景，以减轻临床医生诊断的平衡缺陷。在这里，PI将使用因数分解的条件限制Boltzman机器来生成特定于人、特定于回合类型和特定于缺陷的模型，这些模型将能够生成个性化的发声生物反馈，以改善回合期间的平衡。该项目将促进对智能机器的基本理解，该机器可以通过物理交互来交流意图或以其他方式塑造人类用户的行为。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。