Insole Pressure Sensors to Assess Post-Stroke Gait.

Gait analysis Insole pressure sensor Outcome measure Stroke

Journal

Annals of rehabilitation medicine
ISSN: 2234-0645
Titre abrégé: Ann Rehabil Med
Pays: Korea (South)
ID NLM: 101573065

Informations de publication

Date de publication:
11 Jan 2024
Historique:
received: 07 06 2023
accepted: 13 11 2023
medline: 11 1 2024
pubmed: 11 1 2024
entrez: 10 1 2024
Statut: aheadofprint

Résumé

To confirm that the simplified insole does not affect the gait speed and to identify objective sensor-based gait parameters that correlate strongly with existing clinical gait assessment scales. Ten participants with gait impairment due to hemiplegic stroke were enrolled in this study. Pairs of insoles with four pressure sensors on each side were manufactured and placed in each shoe. Data were extracted during the 10-Meter Walk Test. Several sensor-derived parameters (for example stance time, heel_on-to-toe_peak time, and toe_peak pressure) were calculated and correlated with gait speed and lower extremity Fugl-Meyer (F-M) score. The insole pressure sensor did not affect gait, as indicated by a strong correlation (ρ=0.988) and high agreement (ICC=0.924) between the gait speeds with and without the insole. The parameters that correlated most strongly with highest β coefficients against the clinical measures were stance time of the non-hemiplegic leg (β=-0.87 with F-M and β=-0.95 with gait speed) and heel_on-to-toe_peak time of the non-hemiplegic leg (β=-0.86 with F-M and -0.94 with gait speed). Stance time of the non-hemiparetic leg correlates most strongly with clinical measures and can be assessed using a non-obtrusive insole pressure sensor that does not affect gait function. These results suggest that an insole pressure sensor, which is applicable in a home environment, may be useful as a clinical endpoint in post-stroke gait therapy trials.

Identifiants

pubmed: 38200402
pii: arm.23064
doi: 10.5535/arm.23064
doi:

Types de publication

Journal Article

Langues

eng

Auteurs

Hyung Seok Nam (HS)

Department of Rehabilitation Medicine, Sheikh Khalifa Specialty Hospital, Ras al Khaimah, United Arab Emirates.
Department of Rehabilitation Medicine, Seoul National University Hospital, Seoul, Korea.
Wearable Health Lab, Department of Orthopaedic Surgery, Stanford University, Redwood City, CA, United States.

Caitlin Clancy (C)

Stanford Stroke Center, Stanford University, Palo Alto, CA, United States.

Matthew Smuck (M)

Wearable Health Lab, Department of Orthopaedic Surgery, Stanford University, Redwood City, CA, United States.

Maarten G Lansberg (MG)

Stanford Stroke Center, Stanford University, Palo Alto, CA, United States.

Classifications MeSH