Estimating real-world walking speed from a single wearable device: analytical pipeline, results and lessons learnt from the Mobilise-D technical validation study

Abstract

Background: Estimation of walking speed from wearable devices requires combining a set of algorithms in a single analytical pipeline. The aim of this study was to validate a pipeline for walking speed estimation and assess its performance across different factors (complexity, speed, and walking bout duration) to make recommendations on the use and validity of wearable devices for real-world mobility analysis. Methods: Participants with Parkinson's Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and for 2.5 hours in the real-world, using a wearable device worn on the lower back. Two pipelines for estimating WS were validated across 1298 detected walking bouts, against 1365 walking bouts detected by a multi-sensor reference system. Results: In the laboratory, the mean absolute error (MAE) and mean absolute relative error (MARE) for estimation of walking speed ranged from − 0.06 to 0.04 m/s and 2.1–14.4% respectively, with ICCs ranged between good (0.79) and excellent (0.91). The real-world MAE ranged from − 0.04 to 0.11, and MARE from 1.3–22.7%, where ICCs showed moderate (0.57) to good (0.88) agreement. Errors were lower for cohorts with no major gait impairments, for less complex gait tasks and when considering longer walking bouts. Conclusions: We demonstrated that the analytical pipelines estimated walking speed with good accuracy. Accuracy was dependent upon confounding factors, highlighting the importance of undertaking a robust technical validation of wearable device-derived walking speed before clinical application. Trial registration ISRCTN – 12246987.

Gaitmap - An Open Ecosystem for IMU-based Human Gait Analysis and Algorithm Benchmarking

“…2) Example 2 -Full Pipeline Validation: To evaluate the representative performance of entire gait analysis pipelines, we compare aggregated parameters over an entire gait test. These types of comparisons represent more closely the error ranges expected during actual usage of a gait analysis system and, hence, are an important addition to detailed validation of individual algorithms [58].…”

Gaitmap - An Open Ecosystem for IMU-based Human Gait Analysis and Algorithm Benchmarking

“…2) Example 2 -Full Pipeline Validation: To evaluate the representative performance of entire gait analysis pipelines, we compare aggregated parameters over an entire gait test. These types of comparisons represent more closely the error ranges expected during actual usage of a gait analysis system and, hence, are an important addition to detailed validation of individual algorithms [58].…”