Search

In this paper, we address sequential mobility assistance for daily elderly care through physical human–robot interaction. The goal of this work is to develop a robotic assistive system to provide physical support in daily life such as movement transition, e.g. sit-to-stand and walking. Using a mobile human support robotic platform, we propose an unsupervised learning-based approach to providing desirable physical support through an adaptive impedance parameter selection strategy according to the recognized user's movement state in an online manner. Using a latent generative model with a long short-term memory-based variational autoencoder, we first estimate the probability of the user's current movement state based on the sensory information in a low dimensional latent space. Then, the desired impedance parameters are selected adaptively according to the estimated movement state. One of the benefits of such an unsupervised learning approach is that no labeling is necessary in the training phase. Furthermore, our proposed framework is capable of detecting possible novel states such as falling over based on the obtained latent space. In order to demonstrate the proof of concept of our proposed approach, we present the experimental results of performance evaluations of online movement state recognition as well as novel movement detection. [ABSTRACT FROM AUTHOR]

In recent years, many countries, including Japan, have rapidly aging populations, making the preservation of seniors' quality of life a significant concern. For elderly people with impaired physical abilities, support for toileting is one of the most important issues. This paper details the design, development, experimental assessment, and potential application of the gripper system, with a focus on the unique requirements and obstacles involved in aiding elderly or hemiplegic individuals in dressing and undressing trousers. The gripper we propose seeks to find the right balance between compliance and grasping forces, ensuring precise manipulation while maintaining a safe and compliant interaction with the users. The gripper's integration into a custom-built robotic manipulator system provides a comprehensive solution for assisting hemiplegic individuals in their dressing and undressing tasks. Experimental evaluations and comparisons with existing studies demonstrate the gripper's ability to successfully assist in both dressing and dressing of trousers in confined spaces with a high success rate. This research contributes to the advancement of assistive robotics, empowering elderly, and physically impaired individuals to maintain their independence and improve their quality of life. [ABSTRACT FROM AUTHOR]

In this study, we propose a scheme that enables a humanoid robot to perform cane-supported walking and to select the optimal cane usage depending on its condition. A reaction force is generated through the cane–ground interaction. Although the optimal cane–ground contact position leads to the most effective reaction force from the viewpoint of the walking capability, the cane position is constrained during such contact. Thus, different cane utilities can be achieved based on the priority assigned to the reaction force and cane position. In this light, we propose cyclic, leg-like, and preventive usages, each of which has different priorities. These different priorities lead to different utilities such as the expansion of the support polygon, load distribution, and posture recovery. The robot selects the most suitable of these three cane usages using a selection algorithm for locomotion that is combined with Q-learning (Us-SAL). Through simulations, the robot is made to learn the affinities between these cane usages and falling factors as Q-values and to accordingly select the optimal cane usage. As a result, the robot can perform the desired walking by selecting the optimal cane usage depending on its condition. We conducted walking experiments for each cane usage and found that each improved the walking stability for a suitable falling factor. Furthermore, we experimentally validated Us-SAL; the robot selected the optimal cane usage depending on its condition and walked in a complex environment without falling. [ABSTRACT FROM AUTHOR]