Care of the Elderly and Robots in Healthcare: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Jessie Wu and Version 1 by Andrea Antonio Cantone.

The use of robots in elderly care represents a dynamic field of study aimed at meeting the growing demand for home-based health care services. This article examines the aapplication of robots in elderly home care and is examined and contributes to the literature by introducing a comprehensive and functional architecture within the realm of the Internet of Robotic Things (IoRT). This architecture amalgamates robots, sensors, and Artificial Intelligence (AI) to monitor the health status of the elderly. This study presented a four-actor system comprising a stationary humanoid robot, elderly individuals, medical personnel, and caregivers. This system enables continuous monitoring of the physical and emotional well-being of the elderly through specific sensors that measure vital signs, with real-time updates relayed to physicians and assistants, thereby ensuring timely and appropriate care. Our research endeavors to develop a fully integrated architecture that seamlessly integrates robots, sensors, and AI, enabling comprehensive care for elderly individuals in the comfort of their homes, thus reducing their reliance on institutional hospitalization. In particular, the methodology used was based on a user-centered approach involving geriatricians from the outset. This has been of fundamental importance in assessing their receptivity to the adoption of an intelligent information system, and above all, in understanding the issues most relevant to the elderly. The humanoid robot is specifically designed for close interaction with the elderly, capturing vital signs, emotional states, and cognitive conditions while providing assistance in daily routines and alerting family members and physicians to anomalies. Furthermore, communication was facilitated through an external Telegram bot. To predict the health status of the elderly, a machine learning model based on the Modified Early Warning Score (MEWS), a medical scoring scale, was developed. Five key lessons emerged from the study, showing how the system presented can provide valuable support to physicians, caregivers, and older people. 

  • socially assistive robotics
  • elderly
  • artificial intelligence
  • home-based healthcare
  • IoRT

1. Introduction

The implementation of robots in elderly home care is explored and provides a valuable addition to the existing literature by presenting a comprehensive and operational framework within the Internet of Robotic Things (IoRT) domain, which is generally finding wide use in various sectors [1,2][1][2]. This framework integrates robots, sensors, and Artificial Intelligence (AI) to effectively monitor the health conditions of the elderly. In recent years, people have been facing a number of challenges affecting mental health in which technology is providing support, such as ADHD [3[3][4],4], dyslexia [5], and other disorders. The elderly population is characterized by vulnerability, with the aging demographic showing an increased prevalence of chronic diseases, physical impairments, mental disorders, and other co-morbidities [6,7,8][6][7][8]. Several factors influence this scenario, including:
  • Social considerations: often, adult children are forced to leave their parents’ home in search of employment, leaving their elderly parents isolated without support [9,[910]][10].
  • Insufficient knowledge and awareness of risk factors: there is a lack of knowledge and understanding of the determinants that contribute to adverse health outcomes in elderly population [9].
  • Dietary and nutritional requirements: given that nutritional deficiencies are significant risk factors for age-related chronic diseases, addressing the current predicament of inadequate nutrition requires the implementation of nutritional interventions that promote healthy lifestyles [11,12][11][12].
  • Prescription requirements: Older people tend to rely on an extensive list of medications to be taken at specific intervals. This increases the complexity associated with appropriate medication intake, administration, and adherence [13].
  • Psycho-emotional concerns: isolation, mental strain, and challenges associated with time management are notable psychological and emotional issues facing older people [14].
There are several types of robots, but in ourthe study, weresearchers focus specifically on a class of robots known as social robots. In particular, these are a category of robots designed to interact and communicate with humans in social environments and in the smart home [15,16,17,18][15][16][17][18]. They are equipped with AI and have face, voice, and gesture recognition capabilities, enabling them to understand and respond to human interactions in a natural and engaging way. These robots can be used in a variety of social contexts, such as hospitals, schools, care homes, autism support, rehabilitation therapy, and support in work environments, such as customer service and in the education sector. One of their main goals is to enhance the social experience of individuals by providing emotional support, entertainment, and enjoyable social interactions. One of the most promising applications of social robots is in elderly care. These robots can provide companionship, monitor the well-being of the elderly, and even help with daily tasks, such as meal preparation and medication management. Studies have shown that interaction with social robots can have a positive impact on the emotional and mental state of the elderly, reducing loneliness and social isolation [19,20,21,22][19][20][21][22].

2. Monitoring of Elderly Health or the Utilization of Robots and Sensors in Healthcare

In the context of elderly care, it is very important to consider the ethical factor regarding the perception of healthcare robots for the elderly. In fact, according to a study by Boch et al. [24][23], the introduction of healthcare robots raises various ethical concerns and opportunities that require a holistic analysis from the perspective of both AI ethics and bioethics. An ethical approach to design can help prioritise the well-being and safety of users, avoid harm, respect user autonomy, promote equity in healthcare, and ensure transparency and accountability in decision-making processes. Boch et al. emphasise the importance of ensuring that systems developed in the healthcare sector respect AI ethical principles such as beneficence, non-maleficence, autonomy, justice, and accountability, bringing together the perspectives of AI ethics and bioethics. The article acknowledges that there will be specific ethical requirements within the subfield of AI, which may vary depending on the domain, culture, and users involved.
Mintrom et al. [25][24] consider the development of autonomous robots in public places and the associated policy implications. They highlight how rapid technological advances are making robots more efficient and autonomous, opening up new possibilities for interaction with people in public spaces. However, they also highlight the risk that robots may influence public spaces and social interactions in undesirable ways. The article draws attention to the paucity of public policy discussions on this issue and calls for more exploration by researchers and policy experts.
A range of technologies are currently being used in the care of the elderly, including the use of medical and environmental sensors. According to a systematic review by Alboksmaty et al. [26][25], there is limited evidence on the impact of the use of environmental sensors in healthcare for older people. It has been shown that environmental sensor technologies can lead to cost savings, but further research is needed to assess the impact on health outcomes.
The research by Aminosharieh et al. [27][26] developed and tested a sensor system for smart chairs with 40 participants. The system is innovative and versatile due to the small number of sensors and optimised design. It transmits data via Wi-Fi without initialisation or cables and offers 30 hours of continuous operation. It can be used to identify emotions and behaviour.
One such technology is remote monitoring systems, which allow real-time tracking of vital signs and other critical health information, such as blood pressure, heart rate, oxygen saturation, and blood glucose levels [28][27]. These systems provide continuous, instantaneous monitoring of health status, enabling rapid diagnosis and intervention. They have the potential to minimise hospital admissions and frequent doctor visits, thereby increasing convenience for the elderly. However, the main limitations of these systems are their cost and the need for a robust Internet connection. Moreover, some elderly may find the use of continuous monitoring devices inconvenient [29][28].
In the field of preventive measures and health monitoring for the elderly, wearable devices such as smart watches, bracelets, or necklaces are often used to monitor physical activity, sleep patterns, and various parameters [30][29]. These devices provide continuous monitoring of daily activities and vital signs, encouraging a healthy and active lifestyle. Some devices also incorporate alarm features to address emergencies or falls [31][30]. Conversely, some elderly may not immediately comprehend the output provided by such sensors, necessitating professional evaluation to accurately interpret the data collected [32][31].
Telehealth is another technology used in this area, enabling the elderly to communicate with healthcare professionals or caregivers via video calls or online platforms [33][32]. This facilitates access to medical advice, rehabilitative therapies, or emotional support [34][33]. Telehealth allows direct and immediate communication with healthcare providers or caregivers, thereby reducing the need for physical travel. It can provide remote emotional support and medical advice [34][33]. However, some elderly may prefer human interaction and find it challenging to engage in virtual communication [35][34].
Home automation technology offers solutions to improve safety and make daily activities easier for the elderly. For instance, voice commands or smartphones can be employed to control lighting, temperature, or window shutters. Systems can also be implemented to detect open doors or windows, gas leaks, or flooding [36][35]. This type of technology promotes a safe and comfortable environment for the elderly, and streamlines their daily tasks. It promotes a sense of independence and autonomy while reducing the risk of domestic accidents [37][36]. However, configuring and installing home automation systems can be costly, and some elderly may find it difficult to adapt to using complex technology or may prefer a less automated environment [38][37].
Assistive robots are another potential technological solution, capable of assisting the elderly with various daily activities, including meal preparation, medication management, lifting or transferring, as well as providing companionship and social interaction [39][38]. These robots increase independence and overall quality of life, by providing companionship and emotional support. Some robots are even designed to provide personalised care and emotional support [40][39]. It is important to consider the significant costs associated with purchasing and maintaining robots, as well as the potential discomfort some elderly may feel when interacting with robots or their preference for human assistance [38][37].
In addition, many mobile applications and software are specifically designed for the elderly and their caregivers, including medication reminders, chronic disease management, physical activity monitoring, cognitive games, and online including services [41][40]. These applications are easy to install on smartphones or tablets. However, older adults may have difficult using apps independently due to their limited familiarity with technology or cognitive limitations [42][41].
Based on the review of the existing literature, ouresearchers' study has identified specific gaps and limitations in the current body of knowledge. These gaps highlight areas for further investigation and provide a compelling rationale for ourthe research. For instance, ourresearchers' analysis revealed that a prominent challenge associated with wearable devices is the understanding of the output generated by such sensors by the elderly population. In response, ourresearchers' proposed system aims to address this gap by focusing not on enabling the elderly individuals themselves to understand their health status, but rather on providing caregivers with comprehensive situational awareness of the health status of the elderly individuals under their care.
Furthermore, the review of the literature has shown that a subset of older adults express a preference for direct interactions with healthcare professionals or caregivers, as opposed to virtual communication methods. In light of this finding, researchers propose the use of a humanoid robot as opposed to a conventional device. By incorporating a humanoid robot into the care paradigm, older people can cultivate a higher level of trust in the device [43,44,45,46,47,48][42][43][44][45][46][47]. This trust-building process is paramount in facilitating effective communication and engagement between the elder and the robot, ultimately improving the overall care experience.
A study by Gasteiger et al. [49][48] investigated the experiences and perceptions of elderly people who had the robot textbfBomy in their homes for a week. The results showed a positive acceptance of the robot and its value as a daily care assistant, to the extent that the participants perceived Bomy as a companion. Daily care robots, including the one under consideration, have thus shown promising potential in caring for the elderly, particularly in providing medication reminders and monitoring health and well-being. According to Gasteiger et al, the future design and development of daily care robots should focus on quiet, friendly, and customised technology to meet different daily and healthcare needs, such as measuring vital signs.
A study by Kolstad et al. [50][49] investigated the use of robots in three different Japanese care facilities to assess their impact on care and to collect positive and negative experiences reported by the elderly and the staff involved. The interviews focused on communication robots. The results of the interviews suggested that all types of robots studied had a positive impact on the mental health and well-being of patients.

References

  1. Battistoni, P.; Cantone, A.A.; Martino, G.; Passamano, V.; Romano, M.; Sebillo, M.; Vitiello, G. A Cyber-Physical System for Wildfire Detection and Firefighting. Future Internet 2023, 15, 237.
  2. Gregorio, M.D.; Romano, M.; Sebillo, M.; Vitiello, G.; Vozella, A. Improving human ground control performance in unmanned aerial systems. Future Internet 2021, 13, 188.
  3. Frolli, A.; Ricci, M.; Cavallaro, A.; Rizzo, S.; Di Carmine, F. Virtual reality improves learning in children with ADHD. In Proceedings of the EDULEARN21 Proceedings, Online Conference, 5–6 July 2021; pp. 9229–9236.
  4. Frolli, A.; Savarese, G.; Di Carmine, F.; Bosco, A.; Saviano, E.; Rega, A.; Carotenuto, M.; Ricci, M.C. Children on the autism spectrum and the use of virtual reality for supporting social skills. Children 2022, 9, 181.
  5. Di Gregorio, M.; Romano, M.; Sebillo, M.; Vitiello, G. Dyslexeasy-App to Improve Readability through the Extracted Summary for Dyslexic Users. In Proceedings of the CCNC, Las Vegas, NV, USA, 8–11 January 2022; pp. 1–6.
  6. Boutayeb, A. The double burden of communicable and non-communicable diseases in developing countries. Trans. R. Soc. Trop. Med. Hyg. 2006, 100, 191–199. Available online: http://xxx.lanl.gov/abs/https://academic.oup.com/trstmh/article-pdf/100/3/191/5314802/100-3-191.pdf (accessed on 20 July 2023).
  7. Khosravi, P. Investigating the effectiveness of technologies applied to assist seniors: A systematic literature review. Int. J. Med. Inform. 2016, 85 1, 17–26.
  8. Wu, H. Informal care and health behaviors among elderly people with chronic diseases. J. Health Popul. Nutr. 2017, 36, 1–8.
  9. Song, Y.; Ma, W.; Yi, X.; Wang, S.; Sun, X.; Tian, J.; Wang, S.; Zheng, C.; Zhang, B.; Xu, Z.; et al. Chronic Diseases Knowledge and Related Factors among the Elderly in Jinan, China. PLoS ONE 2013, 8, e68599.
  10. Cheung, G. Financial difficulty and biopsychosocial predictors of loneliness: A cross-sectional study of community dwelling older adults. Arch. Gerontol. Geriatr. 2019, 85, 103935.
  11. Kaur, D.; Rasane, P.; Singh, J.; Kaur, S.; Kumar, V.; Mahato, D.K.; Dey, A.; Dhawan, K.; Kumar, S. Nutritional Interventions for Elderly and Considerations for the Development of Geriatric Foods. Curr. Aging Sci. 2019, 12, 15–27.
  12. Liu, Z. Dietary Micronutrients Intake Status among Chinese Elderly People Living at Home: Data from CNNHS 2010–2012. Nutrients 2019, 11, 1787.
  13. Van Maanen, A.C.D.; Wilting, I.; Jansen, P.A.F. Prescribing medicines to older people—How to consider the impact of ageing on human organ and body functions. Br. J. Clin. Pharmacol. 2019, 86, 1921–1930.
  14. Brodsky, J.; Habib, J.; Hirschfeld, M.J. Key Policy Issues in Long-Term Care; World Health Organization: Geneva, Switzerland, 2003.
  15. Carolis, B.D. Simulating empathic behavior in a social assistive robot. Multimed. Tools Appl. 2017, 76, 5073–5094.
  16. Amato, F.; Di Gregorio, M.; Monaco, C.; Sebillo, M.; Tortora, G.; Vitiello, G. Socially Assistive Robotics combined with Artificial Intelligence for ADHD. In Proceedings of the 2021 IEEE 18th Annual Consumer Communications & Networking Conference(CCNC), Las Vegas, NV, USA, 9–12 January 2021; pp. 1–6.
  17. Amato, F.; Di Gregorio, M.; Monaco, C.; Sebillo, M.; Tortora, G.; Vitiello, G. The Therapeutic Use of Humanoid Robots for Behavioral Disorders. In Proceedings of the AVI ’20, International Conference on Advanced Visual Interfaces, Salerno, Italy, 28 September–2 October 2020.
  18. Hu, R.; Kabouteh, A.; Pawlitza, K.; Güttler, J.; Linner, T.; Bock, T. Developing Personalized Intelligent Interior Units to Promote Activity and Customized Healthcare for Aging Society. In Proceedings of the 36th International Symposium on Automation and Robotics in Construction (ISARC), Banff, AB, Canada, 21–24 May 2019; Al-Hussein, M., Ed.; pp. 234–241.
  19. Fong, T.; Nourbakhsh, I.; Dautenhahn, K. A Survey of Socially Interactive Robots. Robot. Auton. Syst. 2003, 42, 143–166.
  20. Abdi, J. Scoping review on the use of socially assistive robot technology in elderly care. BMJ Open 2018, 8, e018815.
  21. Pu, L. The Effectiveness of Social Robots for Older Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Studies. Gerontologist 2018, 59, e37–e51.
  22. Sinnema, L. The Attitude of Elderly and Young Adults Towards a Humanoid Robot as a Facilitator for Social Interaction. In Proceedings of the Social Robotics: 11th International Conference, ICSR 2019, Madrid, Spain, 26–29 November 2019; pp. 24–33.
  23. Boch, A.; Ryan, S.; Kriebitz, A.; Amugongo, L.M.; Lütge, C. Beyond the Metal Flesh: Understanding the Intersection between Bio- and AI Ethics for Robotics in Healthcare. Robotics 2023, 12, 110.
  24. Mintrom, M.; Sumartojo, S.; Kulic, D.; Tian, L.; Carreno, P.; Allen, A. Robots in public spaces: Implications for policy design. Policy Des. Pract. 2021, 5, 123–139.
  25. Alboksmaty, A.; Solomons, N.; Gul, S.; Neves, A.; Aylin, P. Remote patient monitoring at home using ambient sensors: A systematic review. Eur. J. Public Health 2022, 32, ckac130.065. Available online: http://xxx.lanl.gov/abs/https://academic.oup.com/eurpub/article-pdf/32/Supplement_3/ckac130.065/48592155/ckac130.065.pdf (accessed on 20 July 2023).
  26. Aminosharieh Najafi, T.; Abramo, A.; Kyamakya, K.; Affanni, A. Development of a Smart Chair Sensors System and Classification of Sitting Postures with Deep Learning Algorithms. Sensors 2022, 22, 5585.
  27. Evans, J.; Papadopoulos, A.; Silvers, C.; Charness, N.; Boot, W.; Schlachta-Fairchild, L.; Crump, C.; Martinez, M.; Ent, C. Remote Health Monitoring for Older Adults and Those with Heart Failure: Adherence and System Usability. Telemed. J. e-Health Off. J. Am. Telemed. Assoc. 2015, 22, 480–488.
  28. Fujioka, J.K.; Budhwani, S.; Thomas-Jacques, T.; De Vera, K.; Challa, P.; Fuller, K.; Hogeveen, S.; Gordon, D.; Shahid, S.; Seto, E.; et al. Challenges and Strategies for Promoting Health Equity in Virtual Care: Protocol for a Scoping Review of Reviews. JMIR Res. Protoc. 2020, 9, e22847.
  29. Stavropoulos, T.; Papastergiou, A.; Mpaltadoros, L.; Nikolopoulos, S.; Kompatsiaris, I. IoT Wearable Sensors and Devices in Elderly Care: A Literature Review. Sensors 2020, 20, 2826.
  30. Abidoye, A. Using Wearable Sensors for Remote Healthcare Monitoring System. J. Sens. Technol. 2011, 1, 22–28.
  31. Schall, M.; Sesek, R.; Cavuoto, L. Barriers to the Adoption of Wearable Sensors in the Workplace: A Survey of Occupational Safety and Health Professionals. Hum. Factors 2018, 60, 18720817753907.
  32. Botsis, T.; Demiris, G.; Pedersen, S.; Hartvigsen, G. Home telecare technologies for the elderly. J. Telemed. Telecare 2008, 14, 333–337.
  33. Van den Berg, N.; Schumann, M.; Kraft, K.; Hoffmann, W. Telemedicine and Telecare for Older Patients—A Systematic Review. Maturitas 2012, 73, 94–114.
  34. Zaman, S.B.; Khan, R.; Evans, R.; Thrift, A.; Maddison, R.; Shariful Islam, S.M. Exploring barriers to, and enablers of, the adoption of information and communication technology for the care of older adults with chronic diseases: A scoping review. JMIR Aging 2020, 5, e25251.
  35. López, N.; Ponce, S.; Piccinini, D.; Berenguer, E.; Roberti, M. From Hospital to Home Care: Creating a Domotic Environment for Elderly and Disabled People. IEEE Pulse 2016, 7, 38–41.
  36. Martin, S.; Kelly, G.; Kernohan, W.; Mccreight, B.; Nugent, C. Smart Home technologies for Health and Social Care Support. Cochrane Database Syst. Rev. 2008, 8, CD006412.
  37. Liu, L.; Stroulia, E.; Nikolaidis, I.; Cruz, A.; Ríos-Rincón, A.M. Smart homes and home health monitoring technologies for older adults: A systematic review. Int. J. Med. Inform. 2016, 91, 44–59.
  38. Iancu, I.; Iancu, B. Elderly in the Digital Era. Theoretical Perspectives on Assistive Technologies. Technologies 2017, 5, 60.
  39. Zsiga, K.; Edelmayer, G.; Rumeau, P.; Péter, O.; Tóth, A.; Fazekas, G. Home care robot for socially supporting the elderly: Focus group studies in three European countries to screen user attitudes and requirements. Int. J. Rehabil. Res. 2013, 36, 375–378.
  40. Jose, F.; Livaldo, Mahardhika, M.; Sukiman, N.; Spits Warnars, H.L.H.; Ramadhan, A.; Herry Utomo, W. Mobile Application for Elderly Care. In Proceedings of the 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India, 23–25 February 2022; pp. 968–974.
  41. Portenhauser, A.; Terhorst, Y.; Schultchen, D.; Sander, L.; Denkinger, M.; Stach, M.; Waldherr, N.; Dallmeier, D.; Baumeister, H.; Messner, E. Mobile Apps for Older Adults: Systematic Search and Evaluation Within Online Stores. JMIR Aging 2021, 4, e23313.
  42. Sasama, R. An Experiment for Motivating Elderly People with Robot Guided Interaction. In Universal Access in Human-Computer Interaction. Users Diversity; Stephanidis, C., Ed.; Springer: Berlin/Heidelberg, Germany, 2011.
  43. Görer, B. An autonomous robotic exercise tutor for elderly people. Auton. Robot. 2017, 41, 657–678.
  44. Tanaka, M. Effect of a human-type communication robot on cognitive function in elderly women living alone. Med. Sci. Monit. Int. Med. J. Exp. Clin. Res. 2012, 18, CR550–CR557.
  45. Piasek, J. Acceptance and Long-Term Use of a Social Robot by Elderly Users in a Domestic Environment. In Proceedings of the 2018 11th International Conference on Human System Interaction (HSI), Gdansk, Poland, 4–6 July 2018.
  46. Sakairi, K. Research of robot-assisted activity for the elderly with senile dementia in a group home. In Proceedings of the SICE 2004 Annual Conference, Sapporo, Japan, 4–6 August 2004.
  47. Wada, K. Robot assisted activity for elderly people and nurses at a day service center. In Proceedings of the 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), Washington, DC, USA, 11–15 May 2002.
  48. Gasteiger, N. Older adults’ experiences and perceptions of living with Bomy, an assistive dailycare robot: A qualitative study. Assist. Technol. 2021, 34, 487–497.
  49. Kolstad, M.; Yamaguchi, N.; Babic, A.; Nishihara, Y. Integrating Socially Assistive Robots into Japanese Nursing Care. Stud. Health Technol. Inform. 2020, 270, 1323–1324.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback