Comparison of professional and everyday wearable technology at different body positions in terms of recording gait perturbations.
Lea FeldLena Schell-MajoorSandra HellmersJessica KoschateAndreas HeinTania ZieschangBirger KollmeierPublished in: PLOS digital health (2024)
Falls are a significant health problem in older people, so preventing them is essential. Since falls are often a consequence of improper reaction to gait disturbances, such as slips and trips, their detection is gaining attention in research. However there are no studies to date that investigated perturbation detection, using everyday wearable devices like hearing aids or smartphones at different body positions. Sixty-six study participants were perturbed on a split-belt treadmill while recording data with hearing aids, smartphones, and professional inertial measurement units (IMUs) at various positions (left/right ear, jacket pocket, shoulder bag, pants pocket, left/right foot, left/right wrist, lumbar, sternum). The data were visually inspected and median maximum cross-correlations were calculated for whole trials and different perturbation conditions. The results show that the hearing aids and IMUs perform equally in measuring acceleration data (correlation coefficient of 0.93 for the left hearing aid and 0.99 for the right hearing aid), which emphasizes the potential of utilizing sensors in hearing aids for head acceleration measurements. Additionally, the data implicate that measurement with a single hearing aid is sufficient and a second hearing aid provides no added value. Furthermore, the acceleration patterns were similar for the ear position, the jacket pocket position, and the lumbar (correlation coefficient of about 0.8) or sternal position (correlation coefficient of about 0.9). The correlations were found to be more or less independent of the type of perturbation. Data obtained from everyday wearable devices appears to represent the movements of the human body during perturbations similar to that of professional devices. The results suggest that IMUs in hearing aids and smartphones, placed at the trunk, could be well suited for an automatic detection of gait perturbations.
Keyphrases
- hearing loss
- electronic health record
- antiretroviral therapy
- big data
- healthcare
- heart rate
- loop mediated isothermal amplification
- endothelial cells
- magnetic resonance imaging
- magnetic resonance
- blood pressure
- machine learning
- diffusion weighted imaging
- cerebral palsy
- label free
- mental health
- deep learning
- high resolution
- optical coherence tomography
- artificial intelligence
- clinical evaluation
- induced pluripotent stem cells