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

Abstract

Background: Recently, the use of inertial measurement units (IMUs) in quantitative gait analysis has been widely developed in clinical practice. Numerous methods have been developed for the automatic detection of gait events (GEs). While many of them have achieved high levels of efficiency in healthy subjects, detecting GEs in highly degraded gait from moderate to severely impaired patients remains a challenge. In this paper, we aim to present a method for improving GE detection from IMU recordings in such cases. Methods: We recorded 10-meter gait IMU signals from 13 healthy subjects, 29 patients with multiple sclerosis, and 21 patients with post-stroke equino varus foot. An instrumented mat was used as the gold standard. Our method detects GEs from filtered free acceleration and gyration signals. Firstly, we use autocorrelation and pattern detection techniques to identify a reference stride pattern. Next, we apply multiparametric Dynamic Time Warping to annotate this pattern from a model stride, in order to detect all GEs in the signal. Results: We analyzed 16,819 GEs recorded from healthy subjects and achieved an F1-score of 100%, with a median absolute error of 8 ms (IQR [3-13] ms). In multiple sclerosis and equino varus foot cohorts, we analyzed 6067 and 8951 GEs, respectively, with F1-scores of 99.4% and 96.3%, and median absolute errors of 18 ms (IQR [8-39] ms) and 26 ms (IQR [12-50] ms). Conclusions: Our results are consistent with the state of the art for healthy subjects and demonstrate a good accuracy in GEs detection for pathological patients. Therefore, our proposed method provides an efficient way to detect GEs from IMU signals, even in degraded gaits. However, it should be evaluated in each cohort before being used to ensure its reliability.

Inertial Measurement Units to evaluate the efficacity of Equino Varus Foot surgery in post stroke hemiparetic patients

“…The interest of the semiogram lies in its close relationship with the clinical examination moreover this tool has also been made available online to any team wishing to use it. It can therefore be used widely and reproducibly [47]. All algorithms can be accessed and used online at https://www.ipol.im/.…”

“…We used the semiogram as described in [22], applicated with an automated gait events segmentation algorithm [21]. The calculation methods for each parameter were derived from the literature and are detailed in a previous paper [22].…”

“…The number of examinations obtained was 72 out of the 80 expected, as a number of patients were unable to perform a barefoot passage, and some were too tired after the rst passage. Automatic gait events detection has been realized in another study with a F1-score of 96.3%, with a recall of 96.7% and a precision of 96.0% compared to GaitRite [21]. We assess the correspondence between barefoot and "with shoes" tests during the same sessions to evaluate an Intra-session intraclass correlation coe cients (ICCs) for speed and the seven criteria of the semiogram as shown in Fig.…”

“…However, IMUs have never been used in post-operative evaluation in this population. The semiogram is an innovative tool based on IMUs including several mathematical and physical parameters divided into seven clinical characteristics, allowing a visual feedback on gait evaluation and evolution through a radar diagram [21,22]. It offers an instantaneous analysis of gait, which clinicians can conveniently interpret.…”

Innovative multidimensional gait evaluation using IMU in multiple sclerosis: introducing the semiogram

“…Gait detection was performed using an automated detection algorithm that was previously described ( 27 ). The algorithm combined pattern extraction and temporal dilation methods to detect gait events from the foot-level sensor data.…”