Thigh-Worn Sensor for Measuring Initial and Final Contact During Gait in a Mobility Impaired Population: Validation Study

TL;DR

This study validates a sensor-based method for detecting gait events in stroke survivors, showing it works as well as lab equipment.

Contribution

The study introduces the use of the Teager-Kaiser energy operator for gait event detection in stroke survivors using thigh-worn sensors.

Findings

Stance duration measured by the AP4 sensor showed very good agreement with motion capture data.

The TKEO method provides accurate gait event detection comparable to laboratory systems in stroke survivors.

Results suggest potential for using wearable sensors in gait rehabilitation outside clinical settings.

Abstract

Adapting physical activity monitors to detect gait events (ie, at initial and final contact) has the potential to build a more personalized approach to gait rehabilitation after stroke. Meeting laboratory standards for detecting these events in impaired populations is challenging, without resorting to a multisensor solution. The Teager-Kaiser energy operator (TKEO) estimates the instantaneous energy of a signal; its enhanced sensitivity has successfully detected gait events from the acceleration signals of individuals with impaired mobility, but has not been applied to stroke.

This study aimed to test the criterion validity of TKEO gait event detection (and derived spatiotemporal metrics) using data from thigh mounted physical activity monitors compared with concurrent 3D motion capture in chronic survivors of stroke.

Participants with a history of stroke(n=13, mean age 59, SD 14 years), time since stroke (mean 1.5, SD 0.5 years), walking speed (mean 0.93ms−1 , SD 0.38 m/s) performed two 10m walks at their comfortable speed, while wearing two ActivPAL 4+ (AP4) sensors (anterior of both thighs) and LED cluster markers on the pelvis and ankles which were tracked by a motion capture system. The TKEO signal processing technique was then used to extract gait events (initial and final contact) and calculate stance durations which were compared with motion capture data.

There was very good agreement between the AP4 and motion capture data for stance duration (AP4 0.85s, motion capture system 0.88s, 95% CI of difference −0.07 to 0.13, intraclass correlation coefficient [3,1]=0.79).

The TKEO method for gait event detection using AP4 data provides stance time durations that are comparable with laboratory-based systems in a population with chronic stroke. Providing accurate stance time durations from wearable sensors could extend gait training out of clinical environments. Limitations include ecological and external validity. Future work should confirm findings with a larger sample of participants with a history of stroke.

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