Software Open Access
Prediction of human activity and detection of subsequent actions is crucial for improving the interaction between humans and robots during collaborative operations. Deep-learning techniques are being applied to recognize human activities, including industrial applications. However, the lack of sufficient dataset in the industrial domain and complexities of some industrial activities such as screw driving, assembling small parts, and others affect the model development and testing of human activities. Recently, the InHard dataset (Industrial Human Activity Recognition Dataset) has been published to facilitate industrial human activity recognition for better human-robot collaboration, which still lacks extended evaluation. In this regard, we employ human activity recognition memory and sequential networks (HARNets) combining convolutional neural network (CNN) and long short-term memory (LSTM) techniques for evaluating InHard dataset in our setting.