Step length and gait speed estimation using a hearing aid integrated
accelerometer: A comparison of different algorithms
Abstract
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.