Automation in Healthcare Services

The health care service industry (also known as a medical industry) is an industry that is comprised of the services related to the safeguarding or enhancement of patient health or provides services to treat patients with medicinal, protective, rehabilitative, and analgesic care. For the last two decades, it has been seen that there are drastic changes in healthcare services through automation, digitalization, technological innovation, and communication. Automation has made a revolutionary change in the healthcare industry and allowed for it to be more cost-effective for the industry to run day-to-day operations. Automation-driven health care activities are free from human fatigue and error, so they can help out to provide consistency, accuracy, and potentially lead to a reduction in patient complications, infections, and deaths. Besides, automation can help hospitals, professionals, and doctors for cost-reduction measures and increased efficiency as part of their monetary benefits.

Human Fatigue Evaluation by Face's Image Analysis Based upon Convolutional Neural Networks

This article presents solutions to the person's fatigue recognition problem by the face's image analysis based on convolutional neural networks. In the present paper, existing algorithms were considered. A new model's architecture was proposed and implemented. Resultant metrics of the model were evaluated.

Fatigue Detection for Human Aware Adaptation in Human-Robot Collaboration

Knowledge about human cognitive and physical state is a key factor in physical Human-robot collaboration (pHRC). Such information benefits the robot in planning an adaptive control strategy to prevent or mitigate human fatigue. In this paper, we present a method to detect upper limb muscle fatigue during pHRC using a low-cost myoelectric sensor. We used Riemann geometry to extract robust features from the time-series data and designed a classifier to detect the binary state of human fatigue i.e. fatigued vs not fatigued. We evaluated the method using a fine-motor coordination task for the human to guide an industrial robot along a virtual path for sometime followed by a muscle curl exercise until it induces fatigue in the muscles, and then repeat the robot experiment. We recruited nine participants for the study and recorded muscle activity from their dominant upper limb using the myoelectric sensor and used the data to develop a classifier. We compared the accuracy and robustness of the classifier against conventional time-domain and wavelet-based features and showed that Riemann geometry-based features yield higher classification accuracy (∼ 91%) compared to conventional features and require less computational effort. Such classifier can be used in real-time to develop a human-aware adaptation strategy to prevent fatigue.

Sustainable Human–Robot Collaboration Based on Human Intention Classification

Sustainable manufacturing plays a role in ensuring products’ economic characteristics and reducing energy and resource consumption by improving the well-being of human workers and communities and maintaining safety. Using robots is one way for manufacturers to increase their sustainable manufacturing practices. Nevertheless, there are limitations to directly replacing humans with robots due to work characteristics and practical conditions. Collaboration between robots and humans should accommodate human capabilities while reducing loads and ineffective human motions to prevent human fatigue and maximize overall performance. Moreover, there is a need to establish early and fast communication between humans and machines in human–robot collaboration to know the status of the human in the activity and make immediate adjustments for maximum performance. This study used a deep learning algorithm to classify muscular signals of human motions with accuracy of 88%. It indicates that the signal could be used as information for the robot to determine the human motion’s intention during the initial stage of the entire motion. This approach can increase not only the communication and efficiency of human–robot collaboration but also reduce human fatigue by the early detection of human motion patterns. To enhance human well-being, it is suggested that a human–robot collaboration assembly line adopt similar technologies for a sustainable human–robot collaboration workplace.