Large Language Models (LLMs) are gaining popularity in the field of robotics. However, LLM-based robots are limited to simple, repetitive motions due to the poor integration between language models, robots, and the environment. This letter proposes a novel approach to enhance the performance of LLM-based autonomous manipulation through Human-Robot Collaboration (HRC). The approach involves using a prompted GPT-4 language model to decompose high-level language commands into sequences of motions that can be executed by the robot. The system also employs a YOLO-based perception algorithm, providing visual cues to the LLM, which aids in planning feasible motions within the specific environment. Additionally, an HRC method is proposed by combining teleoperation and Dynamic Movement Primitives (DMP), allowing the LLM-based robot to learn from human guidance. Real-world experiments have been conducted using the Toyota Human Support Robot for manipulation tasks. The outcomes indicate that tasks requiring complex trajectory planning and reasoning over environments can be efficiently accomplished through the incorporation of human demonstrations.

Background: Interventions and care that can evoke positive emotions and reduce apathy or agitation are important for people with dementia. In recent years, socially assistive robots used for better dementia care have been found to be feasible. However, the immediate responses of people with dementia when they are given multiple sensory modalities from socially assistive robots have not yet been sufficiently elucidated. Objective: This study aimed to quantitatively examine the immediate emotional responses of people with dementia to stimuli presented by socially assistive robots using facial expression analysis in order to determine whether they elicited positive emotions. Methods: This pilot study adopted a single-arm interventional design. Socially assistive robots were presented to nursing home residents in a three-step procedure: (1) the robot was placed in front of participants (visual stimulus), (2) the robot was manipulated to produce sound (visual and auditory stimuli), and (3) participants held the robot in their hands (visual, auditory, and tactile stimuli). Expression intensity values for “happy,” “sad,” “angry,” “surprised,” “scared,” and “disgusted” were calculated continuously using facial expression analysis with FaceReader. Additionally, self-reported feelings were assessed using a 5-point Likert scale. In addition to the comparison between the subjective and objective emotional assessments, expression intensity values were compared across the aforementioned 3 stimuli patterns within each session. Finally, the expression intensity value for “happy” was compared between the different types of robots. Results: A total of 29 participants (mean age 88.7, SD 6.2 years; n=27 female; Japanese version of Mini-Mental State Examination mean score 18.2, SD 5.1) were recruited. The expression intensity value for “happy” was the largest in both the subjective and objective assessments and increased significantly when all sensory modalities (visual, auditory, and tactile) were presented (median expression intensity 0.21, IQR 0.09-0.35) compared to the other 2 patterns (visual alone: median expression intensity 0.10, IQR 0.03-0.22; P<.001; visual and auditory: median expression intensity 0.10, IQR 0.04-0.23; P<.001). The comparison of different types of robots revealed a significant increase when all stimuli were presented by doll-type and animal-type robots, but not humanoid-type robots. Conclusions: By quantifying the emotional responses of people with dementia, this study highlighted that socially assistive robots may be more effective in eliciting positive emotions when multiple sensory stimuli, including tactile stimuli, are involved. More studies, including randomized controlled trials, are required to further explore the effectiveness of using socially assistive robots in dementia care.

Background: The worldwide aging trend requires conceptually new prevention, care, and innovative living solutions to support human-based care using smart technology, and this concerns the whole world. Enabling access to active and healthy aging through personalized digital coaching services like physical activity coaching, cognitive training, emotional well-being, and social connection for older adults in real life could offer valuable advantages to both individuals and societies. A starting point might be the analysis of the perspectives of different professionals (eg, geriatricians) on such technologies. The perspectives of experts in the sector may allow the individualization of areas of improvement of clinical interventions, supporting the positive perspective pointed out by the intrinsic capacity framework. Objective: The overall aim of this study was to explore the cross-national perspectives and experiences of different professionals in the field of intrinsic capacity, and how it can be supported by eHealth interventions. To our knowledge, this is the first study to explore geriatric care providers’ perspectives about technology-based interventions to support intrinsic capacity. Methods: A survey involving 20 geriatricians or clinical experts in the fields of intrinsic capacity and active and healthy aging was conducted in Italy, France, Germany, and Japan between August and September 2021. Results: The qualitative findings pointed out relevant domains for eHealth interventions and provided examples for successful practices that support subjective well-being under the intrinsic capacity framework (the benefits offered by personalized interventions, especially by promoting health literacy but avoiding intrusiveness). Moreover, eHealth interventions could be used as a bridge that facilitates and enables social engagement; an instrument that facilitates communication between doctors and patients; and a tool to enrich the monitoring actions of medical staff. Conclusions: There is an unexplored and significant role for such geriatric perspectives to help the development process and evaluate the evidence-based results on the effectiveness of technologies for older people. This is possible only when clinicians collaborate with data scientists, engineers, and developers in order to match the complex daily needs of older adults.

Digital technologies ranging from biosensors to virtual reality have revolutionized the healthcare landscape by offering innovations that hold great promise in addressing the challenges posed by rapidly aging populations. To optimize healthcare experiences for older people, it is crucial to understand their user experience (UX) with digital health technologies. This systematic review, covering articles published from 2013 to 2023, aimed to explore frequently used questionnaires for assessing digital healthcare UX among older people. The inclusion criteria were original studies assessing UX in digital health for individuals aged ≥65 years. Of 184 articles identified, 17 were selected after rigorous screening. The questionnaires used included the System Usability Scale (SUS), the User Experience Questionnaire (UEQ), and the Post-Study System Usability Questionnaire. Customized questionnaires based on models such as the Technology Acceptance Model and the Almere model were developed in some studies. Owing to its simplicity and effectiveness in assessing digital health UX among older people, the SUS emerged as the go-to tool (52.9%). Combining the SUS with the UEQ provided comprehensive insights into UX. Specialized questionnaires were also used, but further research is needed to validate and adapt these tools for diverse cultural contexts and evolving technologies.

The long-term use of transfer support robots in nursing facilities is an important option for improving the efficiency of care work. The “Resyone” transfer support robot is a combination of an electric care bed and a wheelchair, and the wheelchair half of the bed can be detached at the touch of a button. The purpose of this study was to investigate how the long-term use of Resyone would improve the performance of transfer assists, such as reducing the need for multiple caregivers. One Resyone was installed in a nursing facility in Japan and 17 caregivers used it for more than 11 months. The time and number of caregivers required for each transfer assist were surveyed for three 1-week periods: 1 week before (Phase 1) and at 3 weeks (Phase 2) and 11 months (Phase 3) after the introduction of Resyone. In Phase 1, approximately 60% of all transfer assists were performed by two caregivers, but in Phase 2, this was reduced to approximately 20%, and finally, in Phase 3, all transfer assists were performed by a single caregiver. These results suggested that the long-term use of Resyone was associated with improved work efficiency in transfer assistance in a nursing facility.

We examined whether adding robot-supported balance exercises to cardiac rehabilitation improves the ability to balance in older adults with cardiovascular disease (CVD). We conducted a prospective study in 52 older adults who had been hospitalized for worsening CVD. Once weekly for four months, for a total of sixteen sessions as outpatients, the subjects used a Balance Exercise Assist Robot (BEAR) to perform balance exercises and an ergometer for aerobic exercises. Participants’ mean age was 76.9 ± 6.8 years (range, 65–95 years), and their mean brain natriuretic protein level was 164.0 ± 190.0 pg/mL. After the intervention, participants showed significant improvements in gait speed (before, 1.06 ± 0.33 m/s; after, 1.23 ± 0.30 m/s; p < 0.001), Short Physical Performance Battery score (before, 10.02 ± 2.25; after, 10.88 ± 1.79; p ˂ 0.001), timed up-and-go (before, 11.11 ± 5.07 s; after, 9.45 ± 3.45 s; p ˂ 0.001), and knee extension (before, 26.97 ± 11.78 kgf; after, 30.13 ± 13.04 kgf; p = 0.001). Cardiac rehabilitation including exercises using BEAR improved physical functioning and the ability to balance in older adults with CVD. Frail and prefrail patients improved, whereas robust ones did not change.