Brain-computer interface (BCI) technology shows potential for application to motor rehabilitation therapies that use neural plasticity to restore motor function and improve quality of life of stroke survivors. However, it is often difficult for BCI systems to provide the variety of control commands necessary for multi-task real-time control of soft robot naturally. In this study, a novel multimodal human-machine interface system (mHMI) is developed using combinations of electrooculography (EOG), electroencephalography (EEG), and electromyogram (EMG) to generate numerous control instructions. Moreover, we also explore subject acceptance of an affordable wearable soft robot to move basic hand actions during robot-assisted movement. Six healthy subjects separately perform left and right hand motor imagery, looking-left and looking-right eye movements, and different hand gestures in different modes to control a soft robot in a variety of actions. The results indicate that the number of mHMI control instructions is significantly greater than achievable with any individual mode. Furthermore, the mHMI can achieve an average classification accuracy of 93.83% with the average information transfer rate of 47.41 bits/min, which is entirely equivalent to a control speed of 17 actions per minute. The study is expected to construct a more user-friendly mHMI for real-time control of soft robot to help healthy or disabled persons perform basic hand movements in friendly and convenient way.
Operating a crane is a complex job, which requires not only the experience of the operator, but also sufficient and appropriate realtime support to conceive and react to the environment. To help the crane operator, crane pose estimation is necessary to predict potential collisions. Environment perception technologies are essential to update environment information. Location data of the components of the cranes should be used to calculate the pose of the crane that can be used for collision avoidance. This paper aims to investigate how to collect and efficiently process the location data in near real time using ultra wideband (UWB) technology for providing intelligent support to crane operators. First, the requirements of using UWB technology in construction sites to track crane movements are defined. Then, the details of the UWB system setting method are investigated to decide the location of sensors and the number and location of tags attached to different components of a crane. A location data processing method is proposed to improve data quality by filtering noisy data and filling in missing data in near real time. An outdoor test is presented to demonstrate the feasibility of applying the proposed approach. Location data of a crane boom are collected and processed in near real time. The results of the test show a good potential to calculate the poses of crane booms using UWB real-time location system (RTLS).
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