Friday, April 26, 2024
4/26/2024
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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