Moreover, defects relating to the monitoring and supervision system were identified as important causes of accidents.
 
  The findings demonstrated that the qualitative approach could reveal additional latent aspects of safety influencing factors, which require consideration for the appropriate management of occupational safety. This study can guide the planning of preventive strategies for occupational accidents in the petroleum industry.
 The findings demonstrated that the qualitative approach could reveal additional latent aspects of safety influencing factors, which require consideration for the appropriate management of occupational safety. This study can guide the planning of preventive strategies for occupational accidents in the petroleum industry.
  The traditional meal assistance robots use human-computer interaction such as buttons, voice, and EEG. However, most of them rely on excellent programming technology for development, in parallelism with exhibiting inconvenient interaction or unsatisfactory recognition rates in most cases.
 
  To develop a convenient human-computer interaction mode with a high recognition rate, which allows users to make the robot show excellent adaptability in the new environment without programming ability.
 
  A visual interaction method based on deep learning was used to develop the feeding robot when the camera detects that the user's mouth is open for 2 seconds, the feeding command is turned on, and the feeding is temporarily conducted when the eyes are closed for 2 seconds. A programming method of learning from the demonstration, which is simple and has strong adaptability to different environments, was employed to generate a feeding trajectory.
 
  The user is able to eat independently through convenient visual interaction, and it only requires the caregiver to drag and teach the robotic arm once in the face of a new eating environment.
 The user is able to eat independently through convenient visual interaction, and it only requires the caregiver to drag and teach the robotic arm once in the face of a new eating environment.
  The advances in experimental psychology in the last decade have led to a greater understanding of cognitive bias, and the investigation of cognitive bias modifications as a therapeutic option. Whilst conventionally such interventions are delivered in a laboratory, technological advances are changing the potential modes of delivery of these interventions. Whereas mobile delivery of interventions might seem to increase accessibility and encourage compliance, this might not be the case for cognitive bias modification interventions. To reduce boredom, researchers have investigated whether gamification of the task could help reduce repetitiveness, and the diminished motivation that participants had over time. In a prior review of cognitive bias modification interventions, a collaboration between academics and developers was recommended to ensure that the developed product is evidence-based. With the increased recognition of the importance of participatory action research, participants could better help conventional intervention to meet their needs.
 
  The aim of this article was to describe the iterative steps in the conceptualization of the co-designed gamified cognitive bias modification intervention for individuals with opioid use disorders.
 
  A multidisciplinary team worked through the differences in the perspectives offered by healthcare professionals and patient participants, and jointly worked with a developer to conceptualize a new co-designed gamified attention bias modification intervention. The methods shared in this article could be considered and applied to future conceptualization of co-designed interventions.
 A multidisciplinary team worked through the differences in the perspectives offered by healthcare professionals and patient participants, and jointly worked with a developer to conceptualize a new co-designed gamified attention bias modification intervention. The methods shared in this article could be considered and applied to future conceptualization of co-designed interventions.
  The aging population brings the problem of healthcare and dyskinesia. The lack of mobility extremely affects stroke patient's activities of daily living (ADL) and decreases their quality of life. To assist these mobility-limited people, a robotic walker is designed to facilitate gait rehabilitation training.
 
  The aim of this paper is to present the implementation of a novel motion control method to assist disabled people based on their motion intention.
 
  The kinematic framework of the robotic walker is outlined. We propose an intention recognition algorithm based on the interactive force signal. A novel motion control method combined with T-S fuzzy controller and PD controller is proposed. The motion controller can recognize the intention of the user through the interactive force, which allows the user to move or turn around as usual, instead of using their hands to control the walker.
 
  Preliminary experiments with healthy individuals and simulated patients are carried out to verify the effectiveness of the algorithm. The results show that the proposed motion control approach can recognize the user's intention, is easy to control and has a higher precision than the traditional proportional-integral-derivative controller.
 
  The results show that users could achieve the task with acceptable error, which indicates the potential of the proposed control method for gait training.
 The results show that users could achieve the task with acceptable error, which indicates the potential of the proposed control method for gait training.
  People with severe neuromuscular disorders caused by an accident or congenital disease cannot normally interact with the physical environment. The intelligent robot technology offers the possibility to solve this problem. However, the robot can hardly carry out the task without understanding the subject's intention as it relays on speech or gestures. Brain-computer interface (BCI), a communication system that operates external devices by directly converting brain activity into digital signals, provides a solution for this.
 
  In this study, a noninvasive BCI-based humanoid robotic system was designed and implemented for home service.
 
  A humanoid robot that is equipped with multi-sensors navigates to the object placement area under the guidance of a specific symbol "Naomark", which has a unique ID, and then sends the information of the scanned object back to the user interface. Based on this information, the subject gives commands to the robot to grab the wanted object and give it to the subject.  https://www.selleckchem.com/products/tulmimetostat.html  To identify the subject's intention, the channel projection-based canonical correlation analysis (CP-CCA) method was utilized for the steady state visual evoked potential-based BCI system.