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.

Background: Gait analysis constitutes an essential part of orthopedic rehabilitation assessment. Previous studies indicate that observational-based gait analysis lacks reliability and requires extensive clinical training. Therefore, gait analysis in the clinical practice heavily relies on technical aids. The aim of the present study is to develop a reliable gait analysis assessment tool that can accurately assess clinically relevant gait cycle parameters in daily clinical practice. Methods: In this pilot study, a new gait analysis and motion score (GAMS), comprising 10 observational and 5 technically measured (e.g. pressure plate) gait parameters, was developed. The parameters were dichotomously operationalized, reflecting pathological versus physiological manifestations of the parameters. The rating algorithm was administered by 12 raters using videotaped treadmill sessions of 10 orthopedic subjects ( n = 120 ratings). Inter-rater reliability was calculated using the intraclass correlation coefficient (ICC) and the percentage of rating agreement. Results: The mean (standard deviation (SD)) GAMS ratings ranged from 10.0 (1.1) to 21.5 (1.3) points. The overall GAMS ICC was 0.98 (95% confidence interval (CI) 0.96–1.00), whereas the ICC of observational parameters alone was 0.97 (95% CI 0.93–0.99). The mean (SD) percentage of rating agreement was 86.1% (3.3%). For the observational parameters, the mean (SD) rating agreement was 82.5% (4.5%). Conclusion: This new GAMS shows excellent overall inter-rater reliability for a continuum of functional gait statuses. The new score may be an appropriate clinical tool to objectively evaluate patients’ gait patterns. Furthermore, the GAMS may find application as a clinician-reported outcome measure in orthopedic rehabilitation. Further studies are required to verify the validity and accuracy of the new GAMS and its functionality in assessing clinical changes in gait patterns.