A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts

Abstract

Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall ≥92%, precision ≥97%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (−0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases.

Gait Analysis in Neurorehabilitation: From Research to Clinical Practice

“…Indeed, some authors investigated the role of deep learning (DL) in the context of neurorehabilitation. DL techniques can be used to detect gait features in ND, as reported by Romijnders et al [75]. These authors validated a DL-based approach in a cohort of ND that was useful to extract data about stride-specific gait parameters from IMUs.…”

Automatic Gait Events Detection with Inertial Measurement Units: Healthy Subjects and Moderate to Severe Impaired Patients

“…Another issue to consider is the localization of wearable sensors for gait analysis [9,58]. Trojanello et al and Romijnders et al use a shank sensor, which may be more sensitive to the anterior-posterior acceleration movement during the static phase, for which the foot sensor does not provide any information [26,23]. This location may be more suitable for detecting gait events.…”

A deep-learning approach for automatically detecting gait-events based on foot-marker kinematics in children with cerebral palsy—Which markers work best for which gait patterns?

“…Still, in recent years, there have been new models that potentially reach better performance, such as applying the attention model in LSTM. Furthermore, several studies [35][36][37][38][39][40] have also been conducted on hybrid deep learning architecture in other fields. Thus, one could speculate that using such a new model could enhance performance, and might therefore be worth to evaluate in a future study.…”