Abstract--The placement of grab bars for elderly users is based largely on ADA building codes and does not reflect the large differences in height, mobility, and muscle power between individual persons. The goal of this study is to see if there are any correlations between an elderly user's preferred handlebar pose and various demographic indicators, self-rated mobility for tasks requiring postural change, and biomechanical markers. For simplicity, we consider only the case where the handlebar is positioned directly in front of the user, as this confines the relevant body kinematics to a 2D sagittal plane. Previous eldercare devices have been constructed to position a handlebar in various poses in space. Our work augments these devices and adds to the body of knowledge by assessing how the handlebar should be positioned based on data on actual elderly people instead of simulations.

Loneliness and social isolation are serious and widespread problems among older people, affecting their physical and mental health, quality of life, and longevity. In this paper, we propose a ChatGPT-based conversational companion system for elderly people. The system is designed to provide companionship and help reduce feelings of loneliness and social isolation. The system was evaluated with a preliminary study. The results showed that the system was able to generate responses that were relevant to the created elderly personas. However, it is essential to acknowledge the limitations of ChatGPT, such as potential biases and misinformation, and to consider the ethical implications of using AI-based companionship for the elderly, including privacy concerns.

Age-related loss of mobility and increased risk of falling remain important obstacles toward facilitating aging-in-place. Many elderly people lack the coordination and strength necessary to perform common movements around their home, such as getting out of bed or stepping into a bathtub. The traditional solution has been to install grab bars on various surfaces; however, these are often not placed in optimal locations due to feasibility constraints in room layout. In this paper, we present a mobile robot that provides an older adult with a handle anywhere in space - "handle anywhere". The robot consists of an omnidirectional mobile base attached to a repositionable handle. We analyze the postural changes in four activities of daily living and determine, in each, the body pose that requires the maximal muscle effort. Using a simple model of the human body, we develop a methodology to optimally place the handle to provide the maximum support for the elderly person at the point of most effort. Our model is validated with experimental trials. We discuss how the robotic device could be used to enhance patient mobility and reduce the incidence of falls.

When people try to influence others to do something, they subconsciously adjust their speech to include appropriate emotional information. In order for a robot to influence people in the same way, the robot should be able to imitate the range of human emotions when speaking. To achieve this, we propose a speech synthesis method for imitating the emotional states in human speech. In contrast to previous methods, the advantage of our method is that it requires less manual effort to adjust the emotion of the synthesized speech. Our synthesizer receives an emotion vector to characterize the emotion of synthesized speech. The vector is automatically obtained from human utterances by using a speech emotion recognizer. We evaluated our method in a scenario when a robot tries to regulate an elderly person's circadian rhythm by speaking to the person using appropriate emotional states. For the target speech to imitate, we collected utterances from professional caregivers when they speak to elderly people at different times of the day. Then we conducted a subjective evaluation where the elderly participants listened to the speech samples generated by our method. The results showed that listening to the samples made the participants feel more active in the early morning and calmer in the middle of the night. This suggests that the robot may be able to adjust the participants' circadian rhythm and that the robot can potentially exert influence similarly to a person.

"He's my baby," she tells me over Zoom, holding up a puppy to the camera. I laugh and say, "Who's a good robot?" Lucky barks again, and the sound is convincing, as if it's coming from a real dog. He's got a tail that wags, eyes that open and close, and a head that turns to face you when you talk. Under his synthetic golden fur, he has sensors that respond to your touch and a heartbeat you can feel. LeRuzic, who lives in a rural area outside Albany, is fully aware that her pet is a robot. But ever since she got him in March, he's made her feel less lonely, she says.

To better interact with users, a social robot should understand the users' behavior, infer the intention, and respond appropriately. Machine learning is one way of implementing robot intelligence. It provides the ability to automatically learn and improve from experience instead of explicitly telling the robot what to do. Social skills can also be learned through watching human-human interaction videos. However, human-human interaction datasets are relatively scarce to learn interactions that occur in various situations. Moreover, we aim to use service robots in the elderly-care domain; however, there has been no interaction dataset collected for this domain. For this reason, we introduce a human-human interaction dataset for teaching non-verbal social behaviors to robots. It is the only interaction dataset that elderly people have participated in as performers. We recruited 100 elderly people and two college students to perform 10 interactions in an indoor environment. The entire dataset has 5,000 interaction samples, each of which contains depth maps, body indexes and 3D skeletal data that are captured with three Microsoft Kinect v2 cameras. In addition, we provide the joint angles of a humanoid NAO robot which are converted from the human behavior that robots need to learn. The dataset and useful python scripts are available for download at https://github.com/ai4r/AIR-Act2Act. It can be used to not only teach social skills to robots but also benchmark action recognition algorithms.

I don't know how self-driving car technology ranks on a difficulty scale. Perhaps it's not as difficult as rocket science, but it still must be very hard. Add to that the challenge of programming a self-driving car to make moral decisions. Take for example the MIT Media Lab experiment called "The Moral Machine," which was "designed to test how we view…moral problems in light of the emergence of self-driving cars." If a self-driving car were in a'moral bind' in which it would have to hit either an elderly person, a child or a pet to avoid the others, what should it do?

Not many people know that Isaac Asimov didn't originally write his three laws of robotics for I, Robot. They actually first appeared in "Runaround", the 1942 short story*. Robots mustn't do harm, he said, or allow others to come to harm through inaction. They must obey orders given by humans unless they violate the first law. And the robot must protect itself, so long as it doesn't contravene laws one and two.

Low birth rates and higher life expectancies mean that those over 65 years old now will account for 28.7 % of Europe's population by 2080, according to Eurostat, the EU's statistics arm. It means the age-dependency ratio – the proportion of the elderly compared with the number of workers – will almost double from 28.8 % in 2015 to 51 % in 2080, straining healthcare systems and national budgets. Yet there's hope marching over the horizon, in the form of robots. The creators of one humanoid robot under development for the elderly say it can understand people's actions and learn new behaviours in response, even though it is devoid of arms. Robots can be programmed to understand an elderly person's preferences and habits to detect changes in behaviour: for example if a yoga devotee misses a class, it will ask why, while if an elderly person falls it will automatically alert caregivers or emergency services.

Humanoid robots and advances in information technology are increasingly being used by nursing homes in a bid to give elderly people a better quality of life. In one example, a nursing home in Natori, Miyagi Prefecture, has enlisted the help of the Telenoid robot via which people can communicate remotely with an elderly person using a microphone and camera. "Grandma, what color flowers do you like?" the Telenoid robot asked a resident in her 80s at the Urayasu elderly nursing home last month. "Pink cherry blossoms," the woman responded with a smile. Telenoid, which weighs 2.7 kg and is 50 cm tall, was developed by Osaka University professor Hiroshi Ishiguro and his associates.