# Validation and application of wearable sensors for capturing kinematic responses to real-world losses of balance among balance-impaired older adults

> **NIH NIH R21** · VIRGINIA POLYTECHNIC INST AND ST UNIV · 2022 · $203,625

## Abstract

The broad objective of this application is to establish a methodology to measure kinematic (i.e.
bodily movement) responses to losses of balance (LOBs) in the real-world during daily life.
Falls are the leading cause of injuries and injury-related deaths among older adults, with tripping
and slipping being responsible for an estimated 60% of these falls. Numerous laboratory
studies have shown that these falls generally result from an age-related decline in balance
recovery responses to these LOBs. Unfortunately, technical challenges have limited our ability
to assess LOB responses outside the lab, and therefore has allowed a disconnect to persist
between lab studies of trips and slips and actual trips and slips in the real-world. We recently
developed a novel technique to capture the kinematics of real-world LOBs and their context
using wearable sensors and voice recorders. Aim 1 of this application will investigate the
feasibility of this technique for extended use by asking balance-impaired community-dwelling
older adults to wear the system daily during their waking hours for three weeks. Aim 2 will
involve a laboratory validation of this technique by inducing trips and slips among Aim 1
participants while measuring LOB response kinematics simultaneously with the wearable sensor
system and a gold-standard optoelectronic motion capture system. Aim 3 will use wearable
sensor data from Aims 1 and 2 to begin to explore differences between real-world and
laboratory LOB responses. The ability to capture detailed kinematics of LOB responses and
their context in the real-world would have a profound and fundamental impact on fall
prevention efforts. First, it would clarify any differences between laboratory and real-world
LOBs to enhance the generalizability of lab studies to the real-world. Second, it would
overcome well-known limitations of using memory recall when evaluating the frequency and
characteristics of real-world falls. Third, it would greatly enhance the critical evaluation of fall
prevention interventions and their mechanisms to maximize training benefits. Fourth, it would
assist researchers developing algorithms to automatically detect real-world LOBs among
individuals using wearable sensors.

## Key facts

- **NIH application ID:** 10526627
- **Project number:** 1R21AG075430-01A1
- **Recipient organization:** VIRGINIA POLYTECHNIC INST AND ST UNIV
- **Principal Investigator:** Neil Alexander
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $203,625
- **Award type:** 1
- **Project period:** 2022-09-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10526627

## Citation

> US National Institutes of Health, RePORTER application 10526627, Validation and application of wearable sensors for capturing kinematic responses to real-world losses of balance among balance-impaired older adults (1R21AG075430-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10526627. Licensed CC0.

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