# Development of TransKinect: A Clinically Robust System for Transfer Assessment

> **NIH VA I01** · VETERANS HEALTH ADMINISTRATION · 2021 · —

## Abstract

Improper transfer technique predisposes wheelchair users to developing upper arm pain and injuries. A
large body of research on the proper ways to transfer to avoid extraneous forces exists but these techniques
have not been well disseminated into clinics. In a recent study we found that up to 63% of Veterans who use
wheelchairs have many transfer skill deficits including improper arm positioning, use of handgrips, and head-
hips relationship. We propose to develop TransKinect, an automated transfer assessment system for clinical
settings that can help therapists and their patients to identify improper transfer motions and provide guidance
on how to improve their technique. The system will be based on the Transfer Assessment Instrument (TAI)
which is a valid and reliable scale used to assess the quality of transfer technique but requires new therapists
considerable time to learn and to make judgements on appropriate body positioning and mechanics.
TransKinect will eliminate the need for background knowledge and training on the TAI and provide objective
measurement of body and joint motions using a portable, low-cost markerless motion capture sensor. In prior
work, we developed machine learning classifiers for TransKinect that can differentiate proper from improper
techniques with an average accuracy of 94% using the sensor data recorded during a transfer. These classifiers
will be embedded in system software designed to `watch' a transfer, automatically compute the TAI score,
present the results in real-time and provide education and training recommendations to therapists and their
patients.
 The specific aims are: 1) to iteratively develop the TransKinect software platform involving input from an
expert panel, 2) to validate TransKinect for clinical use by comparing the TAI scores generated by the system
against those of expert therapists 3) to test the usability of TransKinect with a novice group of therapists and 4)
field test the system in a VA clinical setting to examine its utility, usability and effectiveness. TransKinect will be
iteratively refined using the data that is collected in each aim of the study.
 For Aim 1, a prototype of TransKinect will be developed and tested with field experts familiar with the
TAI. For Aim 2 three of the expert therapists and TransKinect will score the TAI after watching 30 Veterans
perform wheelchair transfers. We aim to achieve at least 80% of agreement between the two sets of scores
and will refine the software using data collected in this aim if necessary. For Aim 3, 10 novice therapists wlll
use TransKinect to assess a model patient and provide their feedback on the usability and perceived
usefulness of the system. We hypothesize that the therapists will find TransKinect easy to setup and use and
that it increases their awareness and understanding about transfer technique. In Aim 4 the TransKinect system
will be introduced into clinical practice at the Clement J. Zablocki VA Medical Center where we wi...

## Key facts

- **NIH application ID:** 10132737
- **Project number:** 5I01RX002794-03
- **Recipient organization:** VETERANS HEALTH ADMINISTRATION
- **Principal Investigator:** Alicia M. Koontz
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2021
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2019-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10132737

## Citation

> US National Institutes of Health, RePORTER application 10132737, Development of TransKinect: A Clinically Robust System for Transfer Assessment (5I01RX002794-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10132737. Licensed CC0.

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