A Wearable Haptic Feedback System for Home-based Gait Training for Older Adults
A Wearable Haptic Feedback System for Home-based Gait Training for Older Adults
批准号:
10653458
负责人:
Babak Hejrati
金额:
$43.24万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-04-01 至 2026-03-31
关键词:
AcuteAddressAdultAgeAgingAlgorithmsArticular Range of MotionAttentionBiomechanicsCellular PhoneChronicCognitionCognitiveCommunitiesCoupledDataDisciplineElderlyEngineeringExerciseExhibitsFeedbackFosteringFutureGaitGait abnormalityGait speedHealthHomeHumanImpaired cognitionImpairmentIndependent LivingIndividualInstructionInterval trainingInterventionKnowledgeLearningLearning SkillLeftLegLengthLinear ModelsMaintenanceMethodsModificationMonitorMotorMotor SkillsMovementMusculoskeletal SystemNeurologicNeurosciencesOlder PopulationOutcomeParticipantPatternPerformancePhasePhysical therapyPositive ReinforcementsProductionPsychologyResearchSignal TransductionSpeedStructureSystemTactileTargeted ResearchTechnologyTestingThigh structureTimeTrainingVariantWalkingWorkcardiopulmonary systemdesignfall riskfear of fallinggait rehabilitationhaptic feedbackimprovedimproved mobilityinnovationmortality riskmotor controlmotor impairmentmotor learningmuscular systemneuroregulationnovelrecruitresponsesensorskillstelehealthwireless
中文摘要
项目摘要
美国的老年人口正在迅速增长。独立生活是在
成功老龄化的核心,独立的移动性是独立运作的关键。许多
社区居住的老年人,谁可以独立行走,表现出轻度至中度异常
步态模式,如果保持不治疗,将导致福尔斯的风险,害怕跌倒,丧失活动性,
和其他健康问题。随着年龄的增长,步态的许多早期变化反映了
运动控制受损这些运动控制缺陷并不能通过针对
肌肉和心肺系统。干预措施的重点是培训
需要感知系统来产生更稳定和有效的步态。知识
差距在于(1)训练的结构,以提高感知系统,和(2)合并
训练不引人注目地进入用户的日常步行锻炼,因为神经运动技能可以
通过定期培训提高。为了解决这些差距,首款可穿戴触觉反馈
已经创建了一种系统,该系统使用触觉反馈与基于无线智能手机的
技术,使独立和重复的运动学习驱动的步态训练。客观
这项建议的目的是测试生物力学驱动的触觉反馈的假设,
缺陷的基础生物力学变量通过运动学习练习和人类
适应,可以显着改善运动的神经控制,通过修改的
老年人的关键步态参数。触觉反馈旨在增加大腿峰伸展
从而改善作为两个关键步态参数的步幅长度和步态速度。
本建议的两个目的是为了实现上述目标。目标1:
我将测试两个算法的基础上错误反馈和积极的强化方法,以提供
触觉反馈,以增加大腿伸展。这一目标决定了哪些方法唤起更好
用户在培训阶段的反应。目标2将侧重于步态的保持
通过长期的实践与开发的系统和两种算法的改进
在稍后的日期当反馈不再存在时。这项工作将提供可翻译的研究
涉及多个学科的机会,如工程学,神经科学和心理学,
以及运动学习物理治疗,有可能影响许多人的生活,
个体由于这项研究的目标是用户的认知,以影响改变他们的身体能力
通过运动学习训练,以及提高认知能力的流动性的好处
在老年人中,它可以为解决老年人认知能力下降问题提供机会。
英文摘要
Project Summary
The population of older adults in the U.S. is growing very rapidly. Living independence is at the
core of successful aging, and independent mobility is critical to independent functioning. Many
community-dwelling older adults, who can walk independently, exhibit mild to moderate abnormal
gait patterns that if remaining untreated will lead to a risk of falls, fear of falling, loss of mobility,
and other health issues. Many of the early changes in gait seen with aging are a reflection of
impaired motor control. These motor control deficits are not ameliorated by interventions targeting
the muscular and cardiopulmonary systems. Interventions that focus on training the
perceptuomotor system are needed to generate a more stable and efficient gait. The knowledge
gaps lie in (1) the structure of the training to enhance the perceptuomotor system, and (2) merging
the training unobtrusively into users’ daily walking exercise because neuromotor skills can be
improved by regular training. To address these gaps, a first-of-its-kind wearable tactile feedback
system has been created that uses tactile feedback coupled with wireless smartphone-based
technology to enable independent and repeated motor learning-driven gait training. The objective
of this proposal is to test the hypothesis that biomechanics-driven tactile feedback, which targets
deficits in the underlying biomechanical variables via motor learning exercises and human
adaptation, can significantly improve the neural control of movement through modification of the
key gait parameters in older adults. The tactile feedback aims to increase the thigh peak extension
and, thereby, improve stride length and gait speed as two key gait parameters.
The two aims of this proposal are geared towards achieving the mentioned objective. In Aim 1, we
will test two algorithms based on error feedback and positive reinforcement methods to provide
tactile feedback to increase the thigh extension. This aim determines which methods evoke better
responses from users during the training phase. Aim 2 will focus on the retention of gait
improvements through longer-term practicing with the developed system and the two algorithms
at a later date when the feedback is no longer present. This work will provide translatable research
opportunities involving multiple disciplines such as engineering, neuroscience, and psychology,
as well as motor learning physical therapy with the potential to impact the lives of many
individuals. Since this research targets users' cognition to effect change their physical capabilities
through motor learning training, and the benefits of improving mobility for cognitive capabilities
in older adults, it can open up opportunities to address cognitive declines in older adults.
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