Journal Article


Single sensor gait analysis to detect diabetic peripheral neuropathy: A proof of principle study

Abstract

This study explored the potential utility of gait analysis using a single sensor unit (inertial easurement unit [IMU]) as a simple tool to detect peripheral neuropathy in people with diabetes. Seventeen people (14 men) aged 63±9 years (mean±SD) with diabetic peripheral neuropathy performed a 10-m walk test instrumented with an IMU on the lower back. Compared to a reference healthy control data set (matched by gender, age, and body mass index) both spatiotemporal and gait control variables were different between groups, with walking speed, step time, and SDa (gait control parameter) demonstrating good discriminatory power (receiver operating characteristic area under the curve >0.8). These results provide a proof of principle of this relatively simple approach which, when applied in clinical practice, can detect a signal from those with known diabetes peripheral neuropathy. The technology has the potential to be used both routinely in the clinic and for tele-health applications. Further research should focus on investigating its efficacy as an early indicator of or effectiveness of the management of peripheral neuropathy. This could support the development of interventions to prevent complications such as foot ulceration or Charcot’s foot.

Attached files

Authors

Esser, Patrick
Collett, Johnny
Maynard, Kevin
Steins, Dax
Hillier, Angela
Buckingham, Jodie
Tan, Garry D.
King, Laurie
Dawes, Helen

Oxford Brookes departments

Faculty of Health and Life Sciences

Dates

Year of publication: 2018
Date of RADAR deposit: 2018-01-04


Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License


Related resources

This RADAR resource is the Version of Record of Single sensor gait analysis to detect diabetic peripheral neuropathy: A proof of principle study

Details

  • Owner: Joseph Ripp
  • Collection: Outputs
  • Version: 1 (show all)
  • Status: Live