Worker Activity Recognition in Smart Manufacturing Using IMU and sEMG Signals with Convolutional Neural Networks

Human Activity Recognition for Production and Logistics—A Systematic Literature Review

“…This underlines the increasing significance and research interest in HAR for P+L. The application covers a wide range of sectors, such as manufacturing and assembling [58,86], warehousing [72,83,85], construction [90] and maintenance [11,14]. Warehousing, in particular order picking, is the only sector in logistics.…”

“…The remaining contributions from P+L utilise IMUs. The combination of Surface Electromyography (sEMG) and IMUs was proposed by Tao et al Using a single IMU attached to a hand or arm is proposed twice [58,86].…”

“…The authors of [52,75,79] proposed using the magnitude of the acceleration vector from the three components x, y, and z. The authors of [86] used the logarithm magnitude of a two-dimensional Discrete Fourier Transform of IMU signals. They proposed utilising this magnitude as an image input for a CNN.…”

“…As a remark, the authors of [87] did not explain their network in detail. Classification is usually performed using a softmax layer [83,86] or by means of attribute representation, a sigmoid function and a distance measurement, e.g., Euclidean, Cosine or Bray-Curtis distances [85]. The authors of [86] used spectrograms of inertial signals as image inputs for CNNs.…”

“…Classification is usually performed using a softmax layer [83,86] or by means of attribute representation, a sigmoid function and a distance measurement, e.g., Euclidean, Cosine or Bray-Curtis distances [85]. The authors of [86] used spectrograms of inertial signals as image inputs for CNNs. Tao et al [86] concatenated sEMG vectors, corresponding to muscles activation levels, to the output of the first fully-connected layer and input it to a softmax layer.…”