Precise estimation of step length constitutes a fundamental requirement in contemporary gait analysis, particularly for applications in healthcare monitoring, rehabilitation, and intelligent wearable systems. In contrast to conventional approaches, which often rely on wired, foot-mounted inertial sensors and computationally intensive deep learning architectures, this study presents a wireless, thigh-mounted Inertial Measurement Unit (IMU)-based framework for step length estimation, employing a supervised learning paradigm to enhance accuracy, portability, and practicality in natural walking environments. Using an MPU-6050 IMU interfaced with an ESP32 module, the proposed SLE framework establishes a comprehensive data acquisition pipeline to enable seamless wireless transmission and real-time gait recording. Moreover, noise attenuation via Butterworth filtering and statistical normalization was also applied to refine motion signals. Additionally, fourteen engineered gait features extracted from segmented step events are employed to train four supervised learning algorithms, namely artificial neural networks (ANN), sequential neural networks (SNN), k-Nearest Neighbour (k-NN), and support vector machine (SVM), by this SLE framework. Evaluation of the proposed SLE model under both normal and fast walking conditions using a leave-one-out cross-validation scheme demonstrates SNN’s superiority over the other considered supervised models, with an outstanding average accuracy of over 99.4% and a reasonably superior average accuracy of over 83.5% achieved in the wired and wireless environments, respectively, across diverse gaits under both walking modes. With a certain marginal performance degradation, the wireless configuration still underscores its robustness and exhibits its potential for real-time gait monitoring through resource-constrained devices.
Ghosh et al. (Mon,) studied this question.