A Deep Learning Approach to Prevent Problematic Movements of Industrial Workers Based on Inertial Sensors

Abstract

Nowadays, manufacturing industries still face difficulties applying traditional Work-related MusculoSkeletal Disorders (WMSDs) risk assessment methods due to the high effort required by a continuous data collection when using observational methods. An interesting solution is to adopt Inertial Measurement Units (IMUs) to automate the data collection, thus supporting occupational health professionals. In this paper, we propose a deep learning approach to predict human motion based on IMU data with the goal of preventing industrial worker problematic movements that can arise during repetitive actions. The proposed system includes an initial Madgwick filter to merge the raw inertial tri-axis sensor data into a single angle orientation time series. Then, a Machine Learning (ML) algorithm is trained with the obtained time series, allowing to build a forecasting model. The effectiveness of the developed system was validated by using an open-source dataset composed of different motions for the upper body collected in a laboratory environment, aiming to monitor the abduction/adduction angle of the arm. Firstly, distinct ML algorithms were compared for a single angle orientation time series prediction, including: three Long Short-Term Memory (LSTM) methods - a one layer, a stacked layer and a Sequence to Sequence (Seq2Seq) model; and three non deep learning methods - a Multiple Linear Regression, a Random Forest and a Support Vector Machine. The best results were provided by the Seq2Seq LSTM model, which was further evaluated for WMSD prevention by considering 11 human subject datasets and two evaluation procedures (single person and multiple person training and testing). Overall, interesting results were achieved, particularly for multiple person evaluation, where the proposed Seq2Seq LSTM has shown an excellent capability to anticipate problematic movements.