Estimation of ground reaction force waveforms during fixed pace running outside the laboratory.

In laboratory experiments, biomechanical data collections with wearable technologies and machine learning have been promising. Despite the development of lightweight portable sensors and algorithms for the identification of gait events and estimation of kinetic waveforms, machine learning models have yet to be used to full potential. We propose the use of a Long Short Term Memory network to map inertial data to ground reaction force data gathered in a semi-uncontrolled environment.

Estimation of gait events and kinetic waveforms with wearable sensors and machine learning when running in an unconstrained environment.

Wearable sensors and machine learning algorithms are becoming a viable alternative for biomechanical analysis outside of the laboratory. The purpose of this work was to estimate gait events from inertial measurement units (IMUs) and utilize machine learning for the estimation of ground reaction force (GRF) waveforms. Sixteen healthy runners were recruited for this study, with varied running experience.

Validation of Running Gait Event Detection Algorithms in a Semi-Uncontrolled Environment.

The development of lightweight portable sensors and algorithms for the identification of gait events at steady-state running speeds can be translated into the real-world environment. However, the output of these algorithms needs to be validated. The purpose of this study was to validate the identification of running gait events using data from Inertial Measurement Units (IMUs) in a semi-uncontrolled environment.

Feature Identification With a Heuristic Algorithm and an Unsupervised Machine Learning Algorithm for Prior Knowledge of Gait Events.

The purpose of this study was to compare a heuristic feature identification algorithm with output from the Beta Process Auto Regressive Hidden Markov Model (BP-AR-HMM) utilizing minimally sampled (≤ 100 Hz) human locomotion data for identification of gait events prior to their occurrence. Data were collected from 16 participants (21-64 years) using a single gyroscopic sensor in an inertial measurement unit on the dorsum of the foot, across multiple locomotion modes, including level ground walking and running (across speeds 0.8 m s - 3.

User Independent Estimations of Gait Events With Minimal Sensor Data.

Goal: The purpose of this study was to provide an initial examination of the utility of the Beta Process - Auto Regressive - Hidden Markov Model (BP-AR-HMM) for the prior identification of gait events. A secondary objective was to determine whether the output of the model could be used for classification and prediction of locomotion states.

Methods: In this study we utilized the output of the BP-AR-HMM to develop user-independent identification of gait events and gait classification from an idealized three-dimensional acceleration signal.