Gait parameters derived from foot-worn inertial measurement units (IMUs) have been investigated extensively as a digital mobility marker for a variety of diseases. However, it remains poorly understood how the performance of algorithms for spatial gait parameters change when the placement of the sensors is varied. Only few studies investigated this influence, and they only tested physics based algorithms. Over the past years, machine learning and in particular neural networks have been proposed as an alternative method to estimate spatial gait parameters. It is an open question, how consistent these new methods are when the sensor is placed at different positions. Hence, we studied the differences in performance of two convolutional neural network architectures for stride length estimation across six sensor positions on the feet. We conducted an empirical study on a publicly available dataset consisting of 8,989 strides from 14 participants with stride length reference based on motion capture. Our results indicate that the performance of both network architectures are sensitive to changes in the sensor position. Further, we found a consistent relationship between the different sensor positions and the stride length error for both approaches and the physics-based algorithms studied in previous work. These findings motivate further investigations into the origins of the observed differences and the robustness of gait analysis systems in general, to ensure more reliable measurements under unsupervised real-world conditions in the future.

Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. Methods: This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. Conclusion: The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond.

Abstract: Gait is an indicator of a person’s health status and abnormal gait patterns are associated with a higher risk of falls, dementia, and mental health disorders. Wearable sensors facilitate long-term assessment of walking in the user’s home environment. Earables, wearable sensors that are worn at the ear, are gaining popularity for digital health assessments because they are unobtrusive and can easily be integrated into the user’s daily routine, for example, in hearing aids. A comprehensive gait analysis pipeline for an ear-worn accelerometer that includes spatial-temporal parameters is currently not existing. Therefore, we propose and compare three algorithmic approaches to estimate step length and gait speed based on ear-worn accelerometer data: a biomechanical model, feature-based machine learn- ing (ML) models, and a convolutional neural network. We evaluated their performance on a step and walking bout level and compared it with an optical motion capture system. The feature-based ML model achieved the best performance with a precision of 4.8 cm on a walking bout level. For gait speed, the machine learning approach achieved an absolute percentage error of 5.3% (± 3.9%). We find that the ML model is able to estimate step length and gait speed with clinically relevant precision. Furthermore, the model is insensitive to different age groups and sampling rates but sensitive to walking speed. To our knowledge, this work is the first contribution to estimating step length and gait speed using ear-worn accelerometers. Moreover, it lays the foundation for a comprehensive gait analysis framework for ear-worn sensors enabling continuous and long-term monitoring at home.