Award Abstract # 2125256
Automating At-Home Balance Training Using Wearable Sensors

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: REGENTS OF THE UNIVERSITY OF MICHIGAN
Initial Amendment Date: August 19, 2021
Latest Amendment Date: May 8, 2024
Award Number: 2125256
Award Instrument: Continuing Grant
Program Manager: Alexandra Medina-Borja
amedinab@nsf.gov
 (703)292-7557
CMMI
 Division of Civil, Mechanical, and Manufacturing Innovation
ENG
 Directorate for Engineering
Start Date: October 1, 2021
End Date: March 31, 2026 (Estimated)
Total Intended Award Amount: $849,445.00
Total Awarded Amount to Date: $881,445.00
Funds Obligated to Date: FY 2021 = $315,504.00
FY 2022 = $533,941.00

FY 2023 = $16,000.00

FY 2024 = $16,000.00
History of Investigator:
  • Kathleen Sienko (Principal Investigator)
    sienko@umich.edu
  • Leia Stirling (Co-Principal Investigator)
  • Jenna Wiens (Co-Principal Investigator)
  • Lauro Ojeda (Co-Principal Investigator)
  • Xun Huan (Co-Principal Investigator)
Recipient Sponsored Research Office: Regents of the University of Michigan - Ann Arbor
1109 GEDDES AVE STE 3300
ANN ARBOR
MI  US  48109-1015
(734)763-6438
Sponsor Congressional District: 06
Primary Place of Performance: Regents of the University of Michigan - Ann Arbor
3003 S. State St
Ann Arbor
MI  US  48109-1274
Primary Place of Performance
Congressional District:
06
Unique Entity Identifier (UEI): GNJ7BBP73WE9
Parent UEI:
NSF Program(s): M3X - Mind, Machine, and Motor
Primary Program Source: 01002425DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT

01002223DB NSF RESEARCH & RELATED ACTIVIT

01002324DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 9102, 9251, 060Z, 116E, 070E, 9178, 9231
Program Element Code(s): 058Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

Reductions in balance ability caused by aging and sensory disabilities have a negative impact on quality of life and long-term health. Poor balance increases the risk of falls, fear of falling, and sedentary lifestyles, which contribute to subsequent morbidity, mortality, and increased healthcare costs. Balance training designed to strengthen or restore the complex sensorimotor pathways that lead to successful balance can improve function in individuals at risk for falls; however, clinic-based sessions administered by physical therapists are limited by patient load and insurance constraints. Current home-based training is not as effective as it could be because patients cannot accurately assess their performance or effectively self-progress their training without expert guidance. This project will advance science and promote national health, prosperity, and welfare by developing and verifying wearable technology and data-driven models capable of 1) remotely assessing balance in users? homes; and 2) recommending balance exercises informed by models of observed clinical decision-making that adequately and safely challenge users based on their evolving balance abilities. This research is a first and necessary step in achieving the long-term goal of creating automated balance training technologies to complement, supplement, and increase access to clinic-quality care. The outcomes of this research have the potential to be adapted to a diverse population of Americans with a wide range of balance and gait impairments including sensory, neurological, and motor disorders. Additionally, students will be engaged through multiple curricular offerings including hands-on design course projects with broader community interactions.

This research takes the first step toward developing wearable technology and machine intelligence that will enable balance training programs that can be performed in the absence of real-time physical therapist (PT) guidance. The project will (1) identify important kinematic and visual information used by physical therapists to estimate underlying balance exercise ability and to inform clinical-decision making regarding balance exercise progression, and (2) assess the capabilities of adaptive machine learning models to simulate expert-informed balance exercise progression strategies that are responsive to the evolving needs of different individuals and groups. To achieve these objectives, eye movement tracking and patient kinematic measures will be collected in both live and asynchronous video-recorded formats to identify key aspects of information-gathering relevant to physical therapists? evaluations of adult balance capabilities. Additionally, Markov decision process modeling under a reinforcement learning framework will capture the dynamics of the physical therapist-patient co-adaptation of effective exercise progression policies. The models to be developed will capture the iterative, co-adaptive process of expert-patient interaction that evolves over the course of the training program, where the patient adapts their sensorimotor behavior due to the selected training and the physical therapist adapts the training based on patient progress. This work will result in the development of models that integrate heterogeneous clinical and biomechanical data and generate new approaches for modeling expert-patient interaction that are robust to patient differences and co-adaptive between the expert and patient over time. The development of models that characterize the dynamic process of physical therapist-patient interaction in a rehabilitative setting promises to inform future efforts to develop effective, scalable at-home balance training solutions for older adults and people with vestibular dysfunction. Such solutions would complement and/or supplement the current provision of balance training within clinical settings using adaptive wearable technology at home. Furthermore, the framework and technology that will be developed through this research may be adapted for use in other clinic-based training contexts (e.g., stroke recovery and post-surgical rehabilitation).

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Nigrelli, Emma and Carender, Wendy and Sienko, Kathleen H and Stirling, Leia "Eye Tracking Reveals Physical Therapist Decision Making While Evaluating Standing Balance Performance" IEEE Transactions on Neural Systems and Rehabilitation Engineering , v.33 , 2025 https://doi.org/10.1109/TNSRE.2025.3582571 Citation Details
Nigrelli, Emma and Stirling, Leia and Sienko, Kathleen "A preliminary characterization of physical therapist visual behaviors during standing balance tasks using eye tracking" , 2024 https://doi.org/10.1109/EMBC53108.2024.10782282 Citation Details

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