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Abstract Artificial intelligence (AI)-based robots have become popular in various fields, thereby increasing the demand for care robots. Such care robots recognize or estimate factors such as human states and then perform actions depending on the estimation results. If the humans cooperating with robots do not understand robot functions well, the t...
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Context 1
... Humans cannot stand without first leaning forward. People who need support for sit-to-stand actions, including elderly people, are assumed to be able to lean their upper body. However, they find it difficult to lift their bodies. Hence, a linear actuator is adopted to move the armrest vertically to support sit-to-stand actions, as shown in Fig. 2. The flowchart of the robot system is presented in Fig. ...
Citations
... By presenting the robot's actions, timing, and other information on a visual interface and through speakers, the robot will be theoretically easy to use and generate a sense of ease. The robot's actions and the user's next move are useful information that can be presented to the user [8], and audio presentation of the timing of the actions has proven to be effective [17]. Therefore, we designed the display to show the current state and next move of the user and robot on the screen, and set up the audio to announce the timing and the user's move. ...
... In addition, because the robot will stop and return to its original position when it detects an anomaly, the announcement of the stop, the announcement that "It will back up," and the countdown it executes before moving the armrest clearly declare the next action of the robot so that the user can respond to an anomaly with a sense of ease. In a previous study [17], presenting information via a speaker while the user is standing up was found to have a significant impact on the sense of ease and usability of the robot. However, experiments to support standing up, walking, and sitting down, including anomalies found during those movements, in combination with visual information presented on a display were not conducted. ...
As the population of modern society ages, accidents involving the elderly are increasing and there is a shortage of caregivers. Care robots are currently being developed, but they are not yet widely used. One of the reasons for this is that complex systems with numerous sensors are costly and require substantial maintenance. Another reason is that it is difficult for people to understand complex automatic systems, and physical contact with such systems can cause a sense of unease. For this reason, the authors have been researching a user state estimation method for care robots using a small number of sensors and an information presentation method that generates a sense of ease. The center of gravity (CoG) is an effective indicator of the state of the human body, but it generally requires many sensors to accurately determine. To solve this problem, we developed a method for calculating candidates for the CoG using a small number of sensors. In our previous studies, we validated the experimental method via off-line state estimation simulations on the measured data when the robot was moved without any state estimation. However, this technique was insufficient for validating real-time state estimation with an actual robot and for operating the robot based on the estimation. In addition, the care robot developed in previous studies was equipped with an interface to reduce the user's sense of unease, and it presented the estimated user status and timing of user movements on the screen and described them with audio. However, the system was limited to presenting information while providing standing support only, and did not communicate with the user while they were walking, sitting, or oriented abnormally. In this study, we prototyped an interface that presents information on all user movements, such as standing, walking, sitting, and abnormal states, and that links those movements to the care robot's response to the estimated user state. The effectiveness of the interface was validated through experiments using an actual robot with several participants. In all cases, the robot estimated the user's state, raised and lowered the armrest to allow the user to stand up and sit down, and stopped its motion and reverted to its original state when it detected an anomaly. The effectiveness of the interface was also confirmed by participant interviews. These results confirmed the effectiveness of the proposed method in estimating the user's state, providing support based on that state, and presenting information through the interface with a sense of ease.
... The diversity of explanations can be exemplified by the life story book, which Setchi et al. [44] suggest for reminiscence therapy by robots. Another example is verbal guidance for a sit-to-stand support system [46]. ...
The integration of artificial intelligence (AI) into human society mandates that their decision-making process is explicable to users, as exemplified in Asimov’s Three Laws of Robotics. Such human interpretability calls for explainable AI (XAI), of which this paper cites various models. However, the transaction between computable accuracy and human interpretability can be a trade-off, requiring answers to questions about the negotiable conditions and the degrees of AI prediction accuracy that may be sacrificed to enable user-interpretability. The extant research has focussed on technical issues, but it is also desirable to apply a branch of ethics to deal with the trade-off problem. This scholarly domain is labelled coarse ethics in this study, which discusses two issues vis-à-vis AI prediction as a type of evaluation. First, which formal conditions would allow trade-offs? The study posits two minimal requisites: adequately high coverage and order-preservation. The second issue concerns conditions that could justify the trade-off between computable accuracy and human interpretability, to which the study suggests two justification methods: impracticability and adjustment of perspective from machine-computable to human-interpretable. This study contributes by connecting ethics to autonomous systems for future regulation by formally assessing the adequacy of AI rationales.
... Hence, we conducted research based on the idea that the state of the robot user can be estimated based on the calculation of CoG candidates, provided that the candidates can be calculated within a certain narrow range, even if the exact CoG position is unknown [19]. The state estimation between sitting and standing and the leaning estimation method using the CoG candidates were proposed in [20] and [26]. However, these methods solely focus on sit-to-stand movements. ...
... Hence, leaning estimation is important to start moving the armrest. We adopt an estimation method that we previously proposed in [20], [26]. A few geometric features of the CoG candidates are set as the features of the SVM to estimate the user state. ...
... A negligible state estimation error exists; however, it is sufficiently small, such that the robot can be operated without any problem by adopting verbal guidance when it is operated. The effectiveness of verbal guidance is elucidated in [26]. VOLUME 4, 2016 This work is licensed under a Creative Commons Attribution 4.0 License. ...
With the aging of the population and the consequent severe shortage of caregivers, the demand for care robots to assist the elderly is increasing. However, care robots have yet to be widely adopted owing to cost constraints and anxiety issues due to several factors. For instance, care robots are required to have higher functionality than general care devices. It is important to provide both massive power and the appropriate support for the user’s state. However, this requires more sensors to obtain detailed information for user-state estimation and more actuators for physical support, increasing the cost and risk of failure. In a system that has many sensors and operates based on detailed data, the problem of user privacy also emerges. The risk of personal information leakage and the feeling of being monitored increase user discomfort. To support standing up and prevent falling during walking, care robots are required to apply power to the user according to the user state. The position of the center of gravity (CoG) has been used for such state estimation; however, many sensors are required to determine the accurate CoG position. To reduce the number of sensors required for user state estimation, we proposed a method for calculating CoG candidates, and validated the proposed method via experiments. Previous studies have focused solely on normal standing-up motion. However, in daily activities, standing up, walking, and sitting down are a set of motions. In addition, it is not always true that the care robot user can move normally; hence, anomaly detection is beneficial in care robots. Therefore, it is important to estimate the user state considering not only standing-up motion, but also walking and sitting down, as well as any anomaly that may occur during these motions. In this study, we develop an elderly support system that can assist in standing, walking, and sitting based on user state estimation. The CoG candidate calculation method is improved for walking and stand-to-sit movements, and an anomaly detection method using CoG candidates is also proposed. The care robot is designed to be user-driven and provide support for persons with insufficient strength based on state estimation. The experiments verify that the developed system can constantly monitor the user’s state and support a series of movements, such as standing up, walking, and sitting down, with a single robot.
... Many efforts have investigated using robots for assisting seniors to stand and walk on their own, which is a major goal of Humanitude (Martins et al., 2012;Cifuentes and Frizera, 2016;Geravand et al., 2017;Ferrari et al., 2020;Takeda et al., 2020). Robotic technologies do already exist that facilitate the standing or walking of PwDs by themselves. ...
... For example, Takeda et al. proposed a sit-to-stand support system that informed users by verbal guidance when assistance will begin. Healthy adult users felt comfortable when the start of the robot's movement was indicated by voice (Takeda et al., 2020), although they did not evaluate their system with healthy seniors or PwDs. Providing a feeling of comfort through multimodal information in standing and walking is critical for PwDs. ...
Due to cognitive and socio-emotional decline and mental diseases, senior citizens, especially people with dementia (PwD), struggle to interact smoothly with their caregivers. Therefore, various care techniques have been proposed to develop good relationships with seniors. Among them, Humanitude is one promising technique that provides caregivers with useful interaction skills to improve their relationships with PwD, from four perspectives: face-to-face interaction, verbal communication, touch interaction, and helping care receivers stand up (physical interaction). Regardless of advances in elderly care techniques, since current social robots interact with seniors in the same manner as they do with younger adults, they lack several important functions. For example, Humanitude emphasizes the importance of interaction at a relatively intimate distance to facilitate communication with seniors. Unfortunately, few studies have developed an interaction model for clinical care communication. In this paper, we discuss the current challenges to develop a social robot that can smoothly interact with PwDs and overview the interaction skills used in Humanitude as well as the existing technologies.
Nowadays, numerous countries are facing the challenge of aging population. Additionally, the number of people with reduced mobility due to physical illness is increasing. In response to this issue, robots used for walking assistance and sit-to-stand (STS) transition have been introduced in nursing to assist these individuals with walking. Given the shared characteristics of these robots, this paper collectively refers to them as Walking Support Robots (WSR). Additionally, service robots with assisting functions have been included in the scope of this review. WSR are a crucial element of modern nursing assistants and have received significant research attention. Unlike passive walkers that require much user’s strength to move, WSR can autonomously perceive the state of the user and environment, and select appropriate control strategies to assist the user in maintaining balance and movement. This paper offers a comprehensive review of recent literature on WSR, encompassing an analysis of structure design, perception methods, control strategies and safety & comfort features. In conclusion, it summarizes the key findings, current challenges and discusses potential future research directions in this field.
The importance of care robots is growing owing to the increasing numbers of the elderly and the shortage of caregivers. For robots to automatically assist the movements of the elderly users, the state of their pose should be estimated. Hence, we proposed a method for estimating the user’s state using candidate positions of the center of gravity of the robot user using a few sensors. In this method, sensor outputs in all states and movements of the user are collected in advance, and a state estimation and abnormality detection model created by machine learning using the relationship between the outputs and state is used as training data. However, it is difficult to collect training data pertaining to elderly people, before use, for this purpose. Therefore, a database of human actions is created for use as the training data. Rich databases of such actions performed sans robots already exist, but not where care robots are used. Conventional methods for simulating human body movements to estimate human states require actual data and expert coordination and do not address detailed state estimation for physical support. Therefore, this study proposed a method for generating training data using a human link model, which enables the creation of a model that can estimate the state of a robot user without requiring the elderly user to stand up and perform other robot-assisted actions before use. Training data is generated using a link model and candidate centers of gravity, a simple method by which the state of the care robot user can be estimated, and physical support for standing, walking, and sitting can be provided. The effectiveness of the state estimation using the link model generated training data is verified off-line using sensor data independently obtained from the actual movements of the robot and user and also through experiments using an actual care robot. The results validated the state estimation since the time error was sufficiently short (0.35-1.85s), and the experiment confirmed that the robot could realize assistive actions with 90% accuracy.
Care robots have yet to be widely adopted even though the demand for care robots is increasing with the aging of the population. Care robots are required to be small and simple for general household use as well as high functionality. We have developed a small robot that can assist the user in standing up, walking, and sitting down in daily life. By using the user’s center-of-gravity candidate, we were able to estimate the user’s condition and determine abnormalities with a small number of sensors. The experiments verify that the robot can switch the appropriate support according to the estimated state and prevent falls.
Gait rehabilitation training under robotic-assisted partial Body Weight Support (BWS) is a promising technique that helps patients who suffer from a traumatic or congenital brain injury like stroke or cerebral palsy to become more independent in daily life activities. In recent years, robotic BWS systems have been widely studied, where the BWS is provided by the robot while the user walks on devices fixed to the environment such as treadmills, thereby gaining functional benefits such as improved gait symmetry and increased walking speed. On the other hand, mobile BWS robots that allow conventional overground walking with well-designed control strategies have been less researched, limiting the widespread adoption of robotic rehabilitation because of the cost effectiveness of fixed robotic devices and poor portability. To address this problem, in our previous study, we developed a mobile BWS walker that allows for overground walking under variable levels of BWS. In this letter, we introduce a system architecture that integrates the walker with a pair of robotic shoes and discuss different system control strategies including static and dynamic BWS control. When walking under Static-BWS (SBWS), the subject walks while a constant portion of his/her weight is supported, which might cause an unnatural gait. Using a Dynamic-BWS (DBWS), the provided BWS can be adjusted according to human's gait events to provide a more natural gait. This letter describes the system architecture, as well as experiments with able subjects that demonstrate the effectiveness of both the SBWS and DBWS control algorithms in relieving part of the subjects' weight. Specifically, by synchronizing the BWS with gait events, users were able to walk more naturally, particularly under
