Gait Analysis for Thigh-Worn Accelerometry A Data Processing Pipeline using Data-Driven Approaches

Thigh-worn accelerometry is becoming increasingly popular in large-scale cohort studies for quantifying movement behaviour. Gait characteristics are associated with various health conditions and can be used to predict fall risk, monitor disease progression and evaluate rehabilitation outcomes. However, accurate gait assessment typically requires controlled laboratory conditions, that may not reflect real-world mobility. In this context, data-driven algorithms and machine learning approaches hold promise for extracting accurate gait parameters from raw accelerometer data.

Objective

We developed and evaluated a machine learning-based processing pipeline that uses activity classification to detect walking sequences, estimate walking speed, and identify gait events from raw thigh-worn accelerometer data, enabling accurate assessment of free-living gait.

Methods

We integrated an existing activity classification algorithm into the pipeline and evaluated its performance in free-living conditions. Walking speed was estimated based on stride frequency and body height. We then trained a temporal convolutional network model to predict the probability of gait events (i.e. initial and final contact) in healthy adults walking at various speeds on different inclines. All three components of the data processing pipeline were evaluated externally using various independent datasets.

Results

The activity classification model achieved F1 scores ≥ 0.95 for walking in both adults and older adults. Walking speed was estimated with a mean absolute percentage error of 11.5%, and with a bias of 0.02 m/s. The gait event detection model demonstrated high accuracy, with a mean recall ≥ 0.94, precision ≥ 0.98, and mean absolute errors of 20 ms and 31 ms for initial and final contacts, respectively.

Conclusion

Accurate gait analysis in free-living conditions can be achieved by combining data-driven and machine learning approaches with thigh-worn accelerometer data. The developed pipeline can support the analysis of existing thigh-worn accelerometer datasets and enable continuous gait monitoring outside laboratory settings over several days. However, the developed methods for estimating walking speed and gait events require validation in a more diverse sample and in truly unrestricted free-living conditions.