This paper presents a comparative study of two AR training systems for Humanitude dementia care. Humanitude is a multimodal comprehensive care methodology for patients with dementia and has attracted considerable attention as a gentle and effective care technique. To provide an effective training system for Humanitude, we developed HEARTS 1, which realizes simultaneous sensing and interaction by combining a real training entity and augmented reality technology. However, training experiments using HEARTS 1 indicated that HEARTS 1 had certain weaknesses that should be addressed, namely, the usability of the AR display or the impression of the CG model. This paper presents a new prototype called HEARTS 2 consisting of Microsoft HoloLens 2 as well as realistic and animated CG models of older women. Psychological experiments were conducted to identify the difference in the usability of the AR displays, such as the weight and the field of view, and the impression of the CG models. We also conducted questionnaire surveys for beginners and medical professionals and preliminary training experiments to evaluate the usefulness of HEARTS 2. Based on the psychological experiments and questionnaire surveys, we concluded that HEARTS 2 was more comfortable and suitable for a training system for dementia care. GRAPHICAL ABSTRACT

This study proposes a new software platform, called ROS2-TMS, for an informationally structured environment. An informationally structured environment is vital for developing intelligent service robots by embedding various sensors in the environment to enhance the sensing capability and intelligence of robots. Thus far, we have been developing a software platform, named ROS-TMS, for an informationally structured environment, which connects various sensors and robots using ROS architecture. In recent years, ROS2, a next-generation version of ROS, has been released. ROS2 has many advantages, such as enhanced security, QoS control, and support for various platforms. ROS2-TMS, a new version of ROS-TMS, is developed not only by porting existing modules in ROS-TMS, such as the control system for a communication robot, but also by adding useful functions utilizing new features in ROS2. For instance, we added a voice user interface to control robots and various devices in the environment, such as lights or a bed. In addition, we implemented a new task scheduler that provides a cancelation function to stop running tasks and improve the security of the platform.

The present paper introduces a near-future perception system called Previewed Reality. In a co-existence environment of a human and a robot, unexpected collisions between the human and the robot must be avoided to the extent possible. In many cases, the robot is controlled carefully so as not to collide with a human. However, it is almost impossible to perfectly predict human behavior in advance. On the other hand, if a user can determine the motion of a robot in advance, he/she can avoid a hazardous situation and exist safely with the robot. In order to ensure that a user perceives future events naturally, we developed a near-future perception system named Previewed Reality. Previewed Reality consists of an informationally structured environment, a VR display or an AR display, and a dynamics simulator. A number of sensors are embedded in an informationally structured environment, and information such as the position of furniture, objects, humans, and robots, is sensed and stored structurally in a database. Therefore, we can forecast possible subsequent events using a robot motion planner and a dynamics simulator and can synthesize virtual images from the viewpoint of the user, which will actually occur in the near future. The viewpoint of the user, which is the position and orientation of a VR display or an AR display, is also tracked by an optical tracking system in the informationally structured environment, or the SLAM technique on an AR display. The synthesized images are presented to the user by overlaying these images on a real scene using the VR display or the AR display. This system provides human-friendly communication between a human and a robotic system, and a human and a robot can coexist safely by intuitively showing the human possible hazardous situations in advance. GRAPHICAL ABSTRACT

This paper proposes a novel bolt type force sensor that measures forces applied to an object fixed by the sensor. This sensor consists of a iron bolt and four strain gauges and can be made inexpensively compared to a 6-axis force sensor. Due to the feature of the bolt type sensor, it can measure the force applied to the object without dedicated holds, extra jigs, gaps between holds and walls. Therefore, the sensor can be applied to existing climbing holds without changes of difficulty of climbing problems. In this paper, the structure of the sensor is introduced and its calibration method is discussed. Finally, a force measurement experiments are conducted, and it was confirmed that the sensor is able to achieve highly accurate force estimation in practical situations.

Understanding the interaction forces between climbers and climbing holds is important for motion analysis and performance evaluation in sport climbing. In particular, force measurement during speed climbing can provide valuable insights into explosive movements and athlete performance. However, many existing measurement systems require modifications to the climbing wall structure or sensors installed behind the wall, which limits their applicability to existing speed climbing facilities. This study proposes a wireless instrumented climbing hold for speed climbing that enables force-related measurement without modifying the wall structure. The proposed system integrates a six-axis force sensor, a microcomputer, a wireless communication module, and a battery inside the climbing hold. This self-contained configuration allows the hold to wirelessly transmit force and moment data during climbing while maintaining compatibility with standard speed climbing walls and competition environments. In addition, the system enables an estimation of the point of force application on the hold surface by combining measured force and moment data with the three-dimensional hold geometry. Experimental evaluations were conducted to verify the feasibility and performance of the system. External load tests using a digital force gauge confirmed that the embedded sensor can measure static loads and respond to rapidly changing loads with sufficient temporal responsiveness, and the estimated point of force application corresponded closely to the actual loading point. Furthermore, measurements on an actual speed climbing wall demonstrated that the proposed system can successfully capture interaction forces during climbing movements. These results indicate that the proposed system is a practical tool for force-based motion analysis in speed climbing.