Using Lower Limb Wearable Sensors to Identify Gait Modalities: A Machine-Learning-Based Approach.

ABSTRACT: Real-world gait analysis can aid in clinical assessments and influence related interventions, free from the restrictions of a laboratory setting. Using individual accelerometers, we aimed to use a simple machine learning method to quantify the performance of the discrimination between three self-selected cyclical locomotion types using accelerometers placed at frequently referenced attachment locations. Thirty-five participants walked along a 10 m walkway at three different speeds. Triaxial accelerometers were attached to the sacrum, thighs and shanks. Slabs of magnitude, three-second-long accelerometer data were transformed into two-dimensional Fourier spectra. Principal component analysis was undertaken for data reduction and feature selection, followed by discriminant function analysis

SUBMITTER: Hughes LD 

PROVIDER: S-EPMC10675053 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature