Learning from Acceleration Data to Differentiate the Posture, Dynamic and Static Work of the Back: An Experimental Setup

Abstract

Information on body posture, postural change, and dynamic and static work is essential in understanding biomechanical exposure and has many applications in ergonomics and healthcare. This study aimed at evaluating the possibility of using triaxial acceleration data to classify postures and to differentiate between dynamic and static work of the back in an experimental setup, based on a machine learning (ML) approach. A movement protocol was designed to cover the essential degrees of freedom of the back, and a subject wearing a triaxial accelerometer implemented this protocol. Impulses and oscillations from the signals were removed by median filtering, then the filtered dataset was fed into two ML algorithms, namely a multilayer perceptron with back propagation (MLPBNN) and a random forest (RF), with the aim of inferring the most suitable algorithm and architecture for detecting dynamic and static work, as well as for correctly classifying the postures of the back. Then, training and testing subsets were delimitated and used to evaluate the learning and generalization ability of the ML algorithms for the same classification problems. The results indicate that ML has a lot of potential in differentiating between dynamic and static work, depending on the type of algorithm and its architecture, and the data quantity and quality. In particular, MLPBNN can be used to better differentiate between dynamic and static work when tuned properly. In addition, static work and the associated postures were better learned and generalized by the MLPBNN, a fact that could provide the basis for cheap real-world offline applications with the aim of getting time-scaled postural profiling data by accounting for the static postures. Although it wasn’t the case in this study, on bigger datasets, the use of MLPBPNN may come at the expense of high computational costs in the training phase. The study also discusses the factors that may improve the classification performance in the testing phase and sets new directions of research.

Research on Vibration Comfort of Non-Motorized Lane Riding Based on Three-Axis Acceleration

“…During riding, it is assumed that the rider is seated, and lateral swaying of the vehicle body is disregarded. Additionally, considering the rider's strong self-regulatory ability, hand-transferred vibration is also overlooked [35][36][37][38]. Consequently, rider (3) Experimental Program Data collection was conducted from 11:00 to 13:00 on weekdays.…”

Research on Vibration Comfort of Non-motorized Lane Riding Based on Three Axis Acceleration

“…Mechanical vibration has a great impact on human health and comfort, and the degree of bumps in the vehicle is reflected by the smoothness of the ride, according to the evaluation standard of human body withstanding whole-body vibration given by the international standard ʺEvaluation Guidelines for Human Body Withstanding Whole-Body Vibrationʺ (ISO 2631-74). During riding, assuming that the human body is in a seated position and ignoring the side-to-side swaying of the vehicle body, and considering that the non-motorized drivers have strong self-regulation ability during riding, the hand-transferred vibration can also be ignored [25][26][27][28][29]. In summary, the riderʹs vibration is affected by the effect of acceleration from x (forward direction), y (lateral direction perpendicular to the forward direction), z (vertical direction) three dimensions; due to the human bodyʹs sensitivity to acceleration in different vibration directions, the contribution of acceleration in each direction to the human bodyʹs comfort is different, so the calculation of total weighted acceleration needs to be based on the degree of influence of acceleration of the three axes on the comfort, respectively [30][31][32].…”

Development of a Robust Machine Learning Model to Monitor the Operational Performance of Fixed-Post Multi-Blade Vertical Sawing Machines

“…Two machine learning algorithms were considered, namely the Artificial Neural Networks (hereafter, NN) and Random Forests (hereafter, RF). The choice was based on the popularity of these two machine learning techniques [4,[22][23][24]26,30,37] as well as on the capabilities and functionalities of the software used [34] to tune, train and test the models (Section 2.4). By the software used, NN models are implemented in the form of multilayer perceptrons with backpropagation [34,38].…”

“…A typical example is that of using accelerometer data loggers, which were found to be very sensitive to motion and vibration, making them very versatile in getting useful information in many disciplines. There are already many examples of studies using ac-celerometer data, which were implemented in forestry and other sectors with the aim of solving specific problems, mainly those related to operational activity recognition [4,[23][24][25][26][27][28][29][30], proving that acceleration data may be successfully used in many tasks. As a fact, electronics have increasingly been used in forestry [31] to find efficient solutions to the current challenges.…”