EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors

Abstract

Wearable sensors are able to monitor physical health in a home environment and detect changes in gait patterns over time. To ensure long-term user engagement, wearable sensors need to be seamlessly integrated into the user’s daily life, such as hearing aids or earbuds. Therefore, we present EarGait, an open-source Python toolbox for gait analysis using inertial sensors integrated into hearing aids. This work contributes a validation for gait event detection algorithms and the estimation of temporal parameters using ear-worn sensors. We perform a comparative analysis of two algorithms based on acceleration data and propose a modified version of one of the algorithms. We conducted a study with healthy young and elderly participants to record walking data using the hearing aid’s integrated sensors and an optical motion capture system as a reference. All algorithms were able to detect gait events (initial and terminal contacts), and the improved algorithm performed best, detecting 99.8% of initial contacts and obtaining a mean stride time error of 12 ± 32 ms. The existing algorithms faced challenges in determining the laterality of gait events. To address this limitation, we propose modifications that enhance the determination of the step laterality (ipsi- or contralateral), resulting in a 50% reduction in stride time error. Moreover, the improved version is shown to be robust to different study populations and sampling frequencies but is sensitive to walking speed. This work establishes a solid foundation for a comprehensive gait analysis system integrated into hearing aids that will facilitate continuous and long-term home monitoring.

Estimating Gait Events and Speed in the Real World with a Head-Worn IMU

“…An overview of the method performance in comparison with previous studies using head-mounted IMUs in structured conditions was reported in Table Va. The performance of ICs detection was comparable or better than those of previous studies -with only [44] achieving a slightly higher sensitivity. For gait speed estimation, the proposed method matched the performance of previous methods, with only [14] reporting slightly better values of correlation coefficient and relative MAE.…”

Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor

“…Follow-up studies are required to evaluate the reliability of the activity monitor in non-healthy clinical populations. Future applications should further expand the activity monitor to include activities like active and passive transportation, and efforts should be directed towards training algorithms for more detailed activity classification, such as characterizing stepto-step patterns during ambulatory activities [18]. This research lays the foundation for a comprehensive and versatile ear-centered monitoring system with potential applications in healthcare, sports, and beyond.…”

“…The head, housing crucial sensory peripheries for vision, audition, and balance, remains exceptionally stable during various movements [16,17], providing a reliable locus for low-noise identification and differentiation of different bodily activities. Additionally, the ear is a location where users in particular in the elderly population often already employ assistive devices like hearing aids or eyeglass frames that could be readily combined with a miniature motion sensor [18]. Finally, beyond motion, the ear is an ideal location for monitoring a person's physical and health status, as optical in-ear sensors can reliably capture vital signs such as heart rate, blood pressure, body temperature, and oxygen saturation [19].…”

Predicting Gait Parameters of Leg Movement with sEMG and Accelerometer Using CatBoost Machine Learning

“…sEMG in gait analysis proves beneficial for patients with neuromuscular conditions such as cerebral palsy, Parkinson's disease, and muscle dystrophy [8]. Several researchers have employed accelerometers (ACC) for the identification of gait parameters [9,10].…”