Calibration-Free Gait Assessment by Foot-Worn Inertial Sensors

Abstract

Walking is a central activity of daily life, and there is an increasing demand for objective measurement-based gait assessment. In contrast to stationary systems, wearable inertial measurement units (IMUs) have the potential to enable non-restrictive and accurate gait assessment in daily life. We propose a set of algorithms that uses the measurements of two foot-worn IMUs to determine major spatiotemporal gait parameters that are essential for clinical gait assessment: durations of five gait phases for each side as well as stride length, walking speed, and cadence. Compared to many existing methods, the proposed algorithms neither require magnetometers nor a precise mounting of the sensor or dedicated calibration movements. They are therefore suitable for unsupervised use by non-experts in indoor as well as outdoor environments. While previously proposed methods are rarely validated in pathological gait, we evaluate the accuracy of the proposed algorithms on a very broad dataset consisting of 215 trials and three different subject groups walking on a treadmill: healthy subjects (n = 39), walking at three different speeds, as well as orthopedic (n = 62) and neurological (n = 36) patients, walking at a self-selected speed. The results show a very strong correlation of all gait parameters (Pearson's r between 0.83 and 0.99, p < 0.01) between the IMU system and the reference system. The mean absolute difference (MAD) is 1.4 % for the gait phase durations, 1.7 cm for the stride length, 0.04 km/h for the walking speed, and 0.7 steps/min for the cadence. We show that the proposed methods achieve high accuracy not only for a large range of walking speeds but also in pathological gait as it occurs in orthopedic and neurological diseases. In contrast to all previous research, we present calibration-free methods for the estimation of gait phases and spatiotemporal parameters and validate them in a large number of patients with different pathologies. The proposed methods lay the foundation for ubiquitous unsupervised gait assessment in daily-life environments.

Validation of Non-Restrictive Inertial Gait Analysis of Individuals with Incomplete Spinal Cord Injury in Clinical Settings

“…Gait events and phases were identified using the calibration-free gait assessment method introduced by [19]. The technique used angular rates and acceleration signals from two IMUs placed on the shoes and automatically adapted its parameters to the participant's gait velocity.…”

“…It has been shown that the method can overcome the difficulty of identifying gait events in stroke patients' irregular gait patterns. Using the proposed method in [19], gait events-i.e., Initial Contacts (IC) and Final Contacts (FC)-were extracted from IMUs, which were placed on the shoes. The gait cycles began from the IC of one foot and lasted until the consecutive IC of the same foot.…”

“…To overcome the mentioned limitations, less-restrictive and calibration-free algorithms have been proposed recently for finding automatic sensor alignment [17], correcting relative heading offset [18], and determining spatiotemporal gait parameters [19]. The proposed methods have been tested and validated using 3D printed mechanical joints [17,18] and healthy participants.…”

“…Combining less-restrictive approaches [17][18][19] for plug-and-play inertial gait analysis; • Validating the temporal gait parameters and the estimated RoM of hip, knee, and ankle with an OMC reference system using the plug-in gait model [20].…”

Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders

“…With increasing algorithm complexity, required processing resources rise yielding high energy consumption and waiting times between subsequent recordings. Still, algorithm development is an active area of research tackling these issues (e.g., Marín et al, 2020;Laidig et al, 2021). Also, existing hardware issues, such as limited recording time by battery and storage capacity, and data loss during the wireless transfer from sensors to applications or cloud servers should be a trivial problem in the future.…”