Ryosuke Ichikari

Countries experiencing rapid aging face severe caregiver shortages, necessitating effective workforce optimization. Nursing care activity recognition (NCAR) offers a promising solution by automatically detecting caregiving tasks over time and visualizing workflows. Based on this information, NCAR can facilitate improvements in caregiving processes. This review systematically assessed 32 studies following PRISMA guidelines, analyzing dataset characteristics and recognition performance of algorithms in NCAR to identify factors influencing recognition accuracy. The review revealed significant challenges, including a lack of publicly available datasets, class imbalance, and decreased recognition accuracy in real-world environments. Additionally, sensor configuration and annotation quality were found to significantly impact recognition performance. The limited availability of field data restricts model generalization, highlighting the need for novel algorithms and efficient data collection methods. Expanding datasets, designing appropriate classes, and improving algorithms are critical steps for advancing NCAR research and practical applications. The findings of this study demonstrate the potential of NCAR in optimizing caregiving workflows and mitigating workforce shortages.

Hydraulic excavators are widely used in construction work owing to their versatility. However, the general operation of these excavators is complex and novice operators require extensive training to operate them. In this study, we propose a virtual reality (VR)-based training system with three types of visual augmentations using pre-recorded expert operations to support the skill acquirement for handling a hydraulic excavator. To evaluate the effectiveness of the proposed visual augmentations in terms of skill improvement, we compared the scores of the trainees before and after training including combinations of visual augmentations. The results indicated that the display of the lever movement significantly improved the trajectory of the bucket tip, while ghost animation and slow motion did not show significant effects. Furthermore, by showing the lever input and excavator movement of the expert in slow motion, the task completion time increased because of the aftereffect. Our findings not only provide a design guideline for VR-based excavator operation training but can also be applied to augmented reality (AR)/mixed reality (MR) support systems for supporting practical excavator operations.

Proceedings of the International Display Workshops Volume 28 (IDW '21),Search for presentations

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.

The performance of indoor localization methods is highly dependent on the situations in which they are used. Various competitions on indoor localization have been held for fairly comparing the existing indoor localization methods in shared and controlled testing environments. However, it is difficult to evaluate the practical performance in industrial scenarios through the existing competitions. This paper introduces two indoor localization competitions, which are named the “PDR Challenge in Warehouse Picking 2017” and “xDR Challenge for Warehouse Operations 2018” for tracking workers and vehicles in a warehouse scenario. For the PDR Challenge in Warehouse Picking 2017, we conducted a unique competition based on the data measured during the actual picking operation in an actual warehouse. We term the dead-reckoning of a vehicle as vehicle dead-reckoning (VDR), and the term “xDR” is derived from pedestrian dead-reckoning (PDR) plus VDR. As a sequel competition of the PDR Challenge in Warehouse Picking 2017, the xDR Challenge for Warehouse Operations 2018 was conducted as the world’s first competition that deals with tracking forklifts by VDR with smartphones. In the paper, first, we briefly summarize the existing competitions, and clarify the characteristics of our competitions by comparing them with other competitions. Our competitions have the unique capability of evaluating the practical performance in a warehouse by using the actual measured data as the test data and applying multi-faceted evaluation metrics. As a result, we successfully organize the competitions due to the many participants from many countries. As a conclusion of the paper, we summarize the findings of the competitions.