Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning.

Lim Lim,Park Park,Kim Kim

doi:10.3390/s20010130

Abstract

Recent studies have reported the application of artificial neural network (ANN) techniques on data of inertial measurement units (IMUs) to predict ground reaction forces (GRFs), which could serve as quantitative indicators of sports performance or rehabilitation. The number of IMUs and their measurement locations are often determined heuristically, and the rationale underlying the selection of these parameter values is not discussed. Using the dynamic relationship between the center of mass (CoM), the GRFs and joint kinetics, we propose the CoM as a single measurement location with which to predict the dynamic data of the lower limbs, using an ANN. Data from seven subjects walking on a treadmill at various speeds were collected from a single IMU worn near the sacrum. The data was segmented by step and numerically processed for integration. Six segment angles of the stance and swing leg, three joint torques, and two GRFs were estimated from the kinematics of the CoM measured from a single IMU sensor, with fair accuracy. These results indicate the importance of the CoM as a dynamic determinant of multi-segment kinetics during walking. The tradeoff between data quantity and wearable convenience can be solved by utilizing a machine learning algorithm based on the dynamic characteristics of human walking.

Highlights

Kinetic data of human motion, such as ground reaction forces (GRFs) and joint torques, serve as a quantitative indicator of sports performance or the effect of rehabilitation
We proposed a method for the prediction of six lower limb kinematics and five lower limb kinetics data points from a single inertial measurement units (IMUs) measurement using the artificial neural network (ANN), based on the biomechanical characteristics of the center of mass (CoM) and the GRF during human walking (Figure 1)
From the single IMU measurement at the lower back, the practical approximate of the CoM, eleven joint kinetics data points were predicted by the ANN over walking speeds ranging from 1.0 to

Summary

Introduction

Kinetic data of human motion, such as ground reaction forces (GRFs) and joint torques, serve as a quantitative indicator of sports performance or the effect of rehabilitation. GRFs and joint torques have served as indicators of injury risks and pain during running [1,2,3,4,5]. The motion analysis used for these studies is performed in a laboratory with accurate motion trackers and force transducers, which often impose spatial and temporal constraints on subjects. With the development of sensor technology, the market for wearable motion monitoring systems has rapidly grown. Wearable motion monitoring products such as Galaxy, Garmin, Fitbit, and Apple watches monitor the overall rough motion information, such as the steps, cadence, distance, etc

Methods

Results

Conclusion