Predicting gait events from tibial acceleration in rearfoot running: a structured machine learning approach

• Published in: arXiv.org 2020-12
• Main Authors: Robberechts, Pieter, Rud Derie, Pieter Van den Berghe, Gerlo, Joeri, De Clercq, Dirk, Segers, Veerle, Davis, Jesse
• Format: Article
• Language: English
• Subjects: Acceleration; Accelerometers; Flaw detection; Force plates; Gait; Heuristic; Heuristic methods; Identification methods; Machine learning; Motion perception; Plates (structural members); Wearable technology
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1910.13372

Gait event detection of the initial contact and toe off is essential for running gait analysis, allowing the derivation of parameters such as stance time. Heuristic-based methods exist to estimate these key gait events from tibial accelerometry. However, these methods are tailored to very specific acceleration profiles, which may offer complications when dealing with larger data sets and inherent biological variability. Therefore, this paper investigates whether a structured machine learning approach can achieve a more accurate prediction of running gait event timings from tibial accelerometry. Force-based event detection acted as the criterion measure in order to assess the accuracy, repeatability and sensitivity of the predicted gait events. A heuristic method and two structured machine learning methods were employed to derive initial contact, toe off and stance time from tibial acceleration signals. Both a structured perceptron model (median absolute error of stance time estimation: 10.00 \(\pm\) 8.73 ms) and a structured recurrent neural network model (median absolute error of stance time estimation: 6.50 \(\pm\) 5.74 ms) significantly outperformed the existing heuristic approach (median absolute error of stance time estimation: 11.25 \(\pm\) 9.52 ms) on data from 93 rearfoot runners. Thus, results indicate that a structured recurrent neural network machine learning model offers the most accurate and consistent estimation of the gait events and its derived stance time during level overground running. The machine learning methods seem less affected by intra- and inter-subject variation within the data, allowing for accurate and efficient automated data output during rearfoot overground running. Furthermore offering possibilities for real-time monitoring and biofeedback during prolonged measurements, even outside the laboratory.