Clio Yuen Man Cheng

Sarcopenia is a progressive condition characterized by age-related losses in muscle mass and strength, and irreversible in its advanced stages. While sarcopenia negatively impacts daily living, accurately, quickly and economically assessing its effects can be challenging due to individual variability in activity levels. This study introduced a novel approach for assessing the risk of sarcopenia-prone using machine learning and wearable IoT (Internet of Things) sensors. A total of 53 community-dwelling older adults aged 65+ underwent gait analysis using dual sensors. Nineteen gait features were extracted from each cycle and used to train classification algorithms to categorize participants as healthy, risk level 1, risk level 2, or risk level 3 for sarcopenia. Binary classification of healthy vs. sarcopenic-prone achieved 97.41% accuracy on average, while four-class classification averaged 94.67%. Notably, the research discovered worsening gait symmetry with increasing sarcopenia-prone severity. These results indicate IoT sensor-assessed gait may serve as a sensitive indicator for daily sarcopenia-prone screening. Accurate assessment of sarcopenia-prone individuals can be achieved through only a 4-meter walking test, significantly reducing the burden for older adults. This approach offers a cost-effective, convenient, and accurate method for early sarcopenia risk detection and intervention, potentially improving quality of life for older adults. This system could also aid in creating widely applicable monitoring products for assessing sarcopenia risk, supporting IoT, and thereby enabling early identification and intervention for individuals at risk of this condition.

This study aimed to develop a faster and simpler user-centered approach for sarcopenia identification and management using a novel balance sensor system and wearable robots. The study design was a cross-sectional study. The research was conducted based on a community-based study in Hong Kong. A total of 144 community-dwelling older adults were included. Sarcopenia was defined according to the guidelines published by the Asian Working Group for Sarcopenia 2019. Appendicular skeletal muscle mass was calculated using the Lee equation. Among the 46 features extracted from the balance sensor system, 15 displayed a sensitivity >0.8 through a machine-learning approach. The area under the receiver operating characteristics curve of the logistic model in discriminating sarcopenia was 0.68. This study demonstrated that a novel balance sensor system proved useful in sarcopenia identification in older adults. Furthermore, the balance sensor data were valuable in informing the development of wearable robots for sarcopenia management.

Background: This study examined the social well-being of single older adults through the companionship of a social robot, LOVOT (Love+Robot; Groove X). It is designed as a companion for older adults, providing love and affection through verbal and physical interaction. We investigated older adults’ perceptions of the technology and how they benefitted from interacting with LOVOT, to guide the future development of social robots. Objective: This study aimed to use a phenomenological research design to understand the participants’ experiences of companionship provided by the social robot. Our research focused on (1) examining the social well-being of single older adults through the companionship of social robots and (2) understanding the perceptions of single older adults when interacting with social robots. Given the prevalence of technology use to support aging, understanding single older adults’ social well-being and their perceptions of social robots is essential to guide future research on and design of social robots. Methods: A total of 5 single women, aged 60 to 75 years, participated in the study. The participants interacted independently with the robot for a week in their own homes and then participated in a poststudy interview to share their experiences. Results: In total, 4 main themes emerged from the participants’ interactions with LOVOT, such as caring for a social robot, comforting presence of the social robot, meaningful connections with the social robot, and preference for LOVOT over pets. Conclusions: The results indicate that single older adults can obtain psychosocial support by interacting with LOVOT. LOVOT is easily accepted as a companion and makes single older adults feel like they have a greater sense of purpose and someone to connect with. This study suggests that social robots can provide companionship to older adults who live alone. Social robots can help alleviate loneliness by allowing single older adults to form social connections with robots as companions. These findings are particularly important given the rapid aging of the population and the increasing number of single-person households in Singapore.

BACKGROUND This study examined the social well-being of single older adults through the companionship of a social robot, LOVOT (Love+Robot; Groove X). It is designed as a companion for older adults, providing love and affection through verbal and physical interaction. We investigated older adults’ perceptions of the technology and how they benefitted from interacting with LOVOT, to guide the future development of social robots. OBJECTIVE This study aimed to use a phenomenological research design to understand the participants’ experiences of companionship provided by the social robot. Our research focused on (1) examining the social well-being of single older adults through the companionship of social robots and (2) understanding the perceptions of single older adults when interacting with social robots. Given the prevalence of technology use to support aging, understanding single older adults’ social well-being and their perceptions of social robots is essential to guide future research on and design of social robots. METHODS A total of 5 single women, aged 60 to 75 years, participated in the study. The participants interacted independently with the robot for a week in their own homes and then participated in a poststudy interview to share their experiences. RESULTS In total, 4 main themes emerged from the participants’ interactions with LOVOT, such as caring for a social robot, comforting presence of the social robot, meaningful connections with the social robot, and preference for LOVOT over pets. CONCLUSIONS The results indicate that single older adults can obtain psychosocial support by interacting with LOVOT. LOVOT is easily accepted as a companion and makes single older adults feel like they have a greater sense of purpose and someone to connect with. This study suggests that social robots can provide companionship to older adults who live alone. Social robots can help alleviate loneliness by allowing single older adults to form social connections with robots as companions. These findings are particularly important given the rapid aging of the population and the increasing number of single-person households in Singapore.

Background: Given the rapidly aging nature of our global population, policy makers around the world are now emphatically promoting active aging. To address the psychosocial needs of older persons and support active aging, researchers are exploring the use of assistive technologies, specifically social robots as companions. However, there is limited evidence on the efficacy of social robots in promoting active aging for older people in the Hong Kong and Singapore contexts. Objective: This study presents the protocol of a study that investigates the acceptance and quality of interaction between a Japanese social robot, LOVOT, and single older adults in Hong Kong and Singapore. Methods: We used a baseline assessment to measure the primary outcome, participants’ acceptance of technology, and a sense of loneliness, namely, the participants’ differences in responses to LOVOT before and following their interaction with the social robot in this multimethod study design. The baseline assessment consisted of the Qualtrics survey, which measures senior technology acceptance, loneliness, older people’s quality of life, subjective happiness, cultural values, willingness to pay, and demographic characteristics, along with the LOVOT’s sociability and system usability. In the study, participants interacted with LOVOT in 3 sessions before being surveyed to measure the older people’s acceptance and attitudes toward LOVOT. A pre–social robot intervention also occurred in the first session. The study was conducted in both Hong Kong and Singapore. A total of 15 single older adults (ie, individuals who live alone) from Hong Kong and another 15 from Singapore were recruited. Participants were 60-75 years of age, lived by themselves, and had no known cognitive or mental issues. Results: The study began recruiting in March 2022, and recruitment was completed at the end of October 2022. Data collection and data set construction were completed at the end of January 2023. Analysis of the data is currently being conducted, and we plan to publish the results by mid-2023. Conclusions: At an individual level, the study will clarify if LOVOT influences single older adults’ psychosocial well-being by reducing their loneliness. At a community level, the study’s findings will illustrate whether LOVOT can provide increased social connectedness while decreasing individual loneliness. Last, this study’s conclusions can inform policy makers to provide social robots to older people to improve their quality of life. Findings can also inform gerontechnology developers on which aspects and cultural considerations to take into account for future inventions. International Registered Report Identifier (IRRID): DERR1-10.2196/48618

In this ageing society, sarcopenia as a geriatric condition that can have significant negative impacts on an individual’s quality of life. Sarcopenia is a kind of aged syndrome associated with loss of muscle mass and function, which may lead to falls, fractures, gait disorders or even mortality. There are multiple ways to diagnose sarcopenia, such as using Magnetic resonance imaging (MRI), Dual-energy X-ray absorptiometry (DEXA) and Bioelectrical impedance analysis (BIA) etc. to calculate muscle mass; using handgrip or sit-to-stand to measure muscle strength; using short physical performance battery (SPPB), gait, and 5-time sit-to-stand to evaluate physical performance. In this work, we use two μIMUs worn on subjects to record their sit-to-stand motion, and then used several machine learning models to diagnose the severity of sarcopenia of the subjects. We recruited 53 elderly subjects in total for this work. The youngest subject is 65 years old and the oldest is 84 years old. Their average age is 70 years old. Among these 53 subjects, there are 12 healthy ones and 41 sarcopenia patients with different severity. The subject is instructed to do the single sit-to-stand (STS) three times, and two μIMUs attached to the subject’s waist and thigh transfer the data to a computer by Bluetooth. We separated the STS motion process into 4 phases based on the angle and angular velocity, extracted a total of 510 features for motion analytics. These features were futher analyzed by sequential feature selection with 5 different machine learning models (SVM, KNN, decision tree, LDA, and multilayer perceptron). With our proposed methodology, all 53 subjects could be classified as healthy or having sarcopenia with risk level 1, 2, or 3. The best accuracy to distinguish the healthy or sarcopenia subjects is 98.32%, and the best results to distinguish sarcopenia risk levels from 0 (healthy) to 3 (most severe) is 90.44%.

BACKGROUND Given the rapidly ageing nature of our global population, policy makers around the world are now more actively promoting active ageing. Researchers have begun exploring the use of assistive technologies in the form of social robots as companions for older persons to address their psychosocial needs, a key component of active ageing. However, there has been limited evidence on the efficacy of such social robots, especially in the Asian context. OBJECTIVE This article elucidates the protocol of a study to investigate the acceptance and quality of interaction between a Japanese social robot, LOVOT, and elderly singletons in Hong Kong and Singapore. METHODS In this multimethod study design, 15 elderly singletons from Hong Kong and another 15 from Singapore were recruited: participants were 60-75 years of age, living alone, and had no known cognitive or mental issues. The primary outcome is the participants’ differences in responses to and acceptance of LOVOT prior to and following their interaction with the social robot, as outlined in the baseline assessment: the Qualtrics survey measured the senior technology acceptance (Chen & Lou, 2020), loneliness (Russell, 1996), older people’s quality of life (Bowling et al., 2013 & Herdman et al., 2011), subjective happiness (Lyubomirsky & Lepper, 1999), cultural values (Triandis & Gelfand, 1998), willingness to pay, and demographic characteristics, along with the LOVOT’s sociability (Heerink et al., 2010) and system usability (Brooke, 1996). During the first session, participants were interviewed about their opinions on gerontechnology, social robots, and pets. Following the interview, participants were given 15 minutes to freely interact with LOVOT. This interaction was succeeded by a Qualtrics survey and interview to measure the elders’ acceptance and attitudes toward LOVOT. The interaction, post-interaction survey and interview, was repeated for two subsequent sessions for a total of three interactions with LOVOT. The study was conducted in both Hong Kong and Singapore. RESULTS The study began recruiting in March 2022 and recruitment completed at the end of Oct 2022. Data collection and dataset construction were completed at end of Jan 2023. A total of 30 participants successfully completed the longitudinal study. We are planning to publish the results by mid-2023. The interventional protocol is available in English and Traditional Chinese and will be available upon request from the corresponding author. CONCLUSIONS This article outlines the protocol of a pioneer study investigating the efficacy of the social robot, LOVOT, as companions for older people in Hong Kong and Singapore. This study introduces innovative solutions to improving the psychosocial health and engagement of older people. This protocol can be adapted to explore the efficacy of these assistive technologies in other elder care settings as well. CLINICALTRIAL The research protocol abides by all institutional ethical standards in human subject research and has been approved by the Human Research Ethics Committee at The University of Hong Kong (HREC Reference Number: EA220116).

BACKGROUND Given the rapidly aging nature of our global population, policy makers around the world are now emphatically promoting active aging. To address the psychosocial needs of older persons and support active aging, researchers are exploring the use of assistive technologies, specifically social robots as companions. However, there is limited evidence on the efficacy of social robots in promoting active aging for older people in the Hong Kong and Singapore contexts. OBJECTIVE This study presents the protocol of a study that investigates the acceptance and quality of interaction between a Japanese social robot, LOVOT, and single older adults in Hong Kong and Singapore. METHODS We used a baseline assessment to measure the primary outcome, participants’ acceptance of technology, and a sense of loneliness, namely, the participants’ differences in responses to LOVOT before and following their interaction with the social robot in this multimethod study design. The baseline assessment consisted of the Qualtrics survey, which measures senior technology acceptance, loneliness, older people’s quality of life, subjective happiness, cultural values, willingness to pay, and demographic characteristics, along with the LOVOT’s sociability and system usability. In the study, participants interacted with LOVOT in 3 sessions before being surveyed to measure the older people’s acceptance and attitudes toward LOVOT. A pre–social robot intervention also occurred in the first session. The study was conducted in both Hong Kong and Singapore. A total of 15 single older adults (ie, individuals who live alone) from Hong Kong and another 15 from Singapore were recruited. Participants were 60-75 years of age, lived by themselves, and had no known cognitive or mental issues. RESULTS The study began recruiting in March 2022, and recruitment was completed at the end of October 2022. Data collection and data set construction were completed at the end of January 2023. Analysis of the data is currently being conducted, and we plan to publish the results by mid-2023. CONCLUSIONS At an individual level, the study will clarify if LOVOT influences single older adults’ psychosocial well-being by reducing their loneliness. At a community level, the study’s findings will illustrate whether LOVOT can provide increased social connectedness while decreasing individual loneliness. Last, this study’s conclusions can inform policy makers to provide social robots to older people to improve their quality of life. Findings can also inform gerontechnology developers on which aspects and cultural considerations to take into account for future inventions. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/48618