Objective: The study aimed to provide a comprehensive overview of the factors impacting technology adoption, to predict the acceptance of artificial intelligence (AI)-based technologies. Background: Although the acceptance of AI devices is usually defined by behavioural factors in theories of user acceptance, the effects of technical and human factors are often overlooked. However, research shows that user behaviour can vary depending on a system’s technical characteristics and differences in users. Method: A systematic review was conducted. A total of 85 peer-reviewed journal articles that met the inclusion criteria and provided information on the factors influencing the adoption of AI devices were selected for the analysis. Results: Research on the adoption of AI devices shows that users’ attitudes, trust and perceptions about the technology can be improved by increasing transparency, compatibility, and reliability, and simplifying tasks. Moreover, technological factors are also important for reducing issues related to human factors (e.g. distrust, scepticism, inexperience) and supporting users with lower intention to use and lower trust in AI-infused systems. Conclusion: As prior research has confirmed the interrelationship among factors with and without behaviour theories, this review suggests extending the technology acceptance model that integrates the factors studied in this review to define the acceptance of AI devices across different application areas. However, further research is needed to collect more data and validate the study’s findings. Application: A comprehensive overview of factors influencing the acceptance of AI devices could help researchers and practitioners evaluate user behaviour when adopting new technologies.

We examined the effects of posture, weight and frequency on trunk muscular activity and fatigue during repetitive dynamic lifting. Electromyographic (EMG) signals from eight primary trunk muscles were collected during 120 min for four different task conditions. The patterns of muscle recruitment and the levels of relative activation were analysed using the normalized EMG data. Median power frequency (MPF) shift patterns were analysed to examine muscular fatigue. The muscles in the dorsal part of the trunk were activated at the symmetric posture, while the muscles on the contralateral side to the workload were more strongly activated at the asymmetric posture. Decreasing trends of MPFs were found in some active muscles, and they were more pronounced for the asymmetric posture than for the symmetric posture. It was also seen that the muscles became fatigued faster for the light load-high frequency task than for the heavy load-low frequency task.

Facial emotion recognition (FER) systems are imperative in recent advanced artificial intelligence (AI) applications to realize better human–computer interactions. Most deep learning-based FER systems have issues with low accuracy and high resource requirements, especially when deployed on edge devices with limited computing resources and memory. To tackle these problems, a lightweight FER system, called Light-FER, is proposed in this paper, which is obtained from the Xception model through model compression. First, pruning is performed during the network training to remove the less important connections within the architecture of Xception. Second, the model is quantized to half-precision format, which could significantly reduce its memory consumption. Third, different deep learning compilers performing several advanced optimization techniques are benchmarked to further accelerate the inference speed of the FER system. Lastly, to experimentally demonstrate the objectives of the proposed system on edge devices, Light-FER is deployed on NVIDIA Jetson Nano.

The rapid development of deep-learning-based edge artificial intelligence applications and their data-driven nature has led to several research issues. One key issue is the collaboration of the edge and cloud to optimize such applications by increasing inference speed and reducing latency. Some researchers have focused on simulations that verify that a collaborative edge–cloud network would be optimal, but the real-world implementation is not considered. Most researchers focus on the accuracy of the detection and recognition algorithm but not the inference speed in actual deployment. Others have implemented such networks with minimal pressure on the cloud node, thus defeating the purpose of an edge–cloud collaboration. In this study, we propose a method to increase inference speed and reduce latency by implementing a real-time face recognition system in which all face detection tasks are handled on the edge device and by forwarding cropped face images that are significantly smaller than the whole video frame, while face recognition tasks are processed at the cloud. In this system, both devices communicate using the TCP/IP protocol of wireless communication. Our experiment is executed using a Jetson Nano GPU board and a PC as the cloud. This framework is studied in terms of the frame-per-second (FPS) rate. We further compare our framework using two scenarios in which face detection and recognition tasks are deployed on the (1) edge and (2) cloud. The experimental results show that combining the edge and cloud is an effective way to accelerate the inferencing process because the maximum FPS achieved by the edge–cloud deployment was 1.91× more than the cloud deployment and 8.5× more than the edge deployment.