A Conversational Robot for Older Adults with Alzheimer’s Disease

Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review

BACKGROUND

The most common form of dementia, Alzheimer's Disease (AD), is challenging for both those affected as well as for their care providers, and caregivers. Socially assistive robots (SARs) offer promising supportive care to assist in the complex management associated with AD.

OBJECTIVES

To conduct a scoping review of published articles that proposed, discussed, developed or tested SAR for interacting with AD patients.

METHODS

We performed a scoping review informed by the methodological framework of Arksey and O'Malley and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist for reporting the results. At the identification stage, an information specialist performed a comprehensive search of 8 electronic databases from the date of inception until January 2022 in eight bibliographic databases. The inclusion criteria were all populations who recive or provide care for AD, all interventions using SAR for AD and our outcomes of inteerst were any outcome related to AD patients or care providers or caregivers. All study types published in the English language were included.

RESULTS

After deduplication, 1251 articles were screened. Titles and abstracts screening resulted to 252 articles. Full-text review retained 125 included articles, with 72 focusing on daily life support, 46 on cognitive therapy, and 7 on cognitive assessment.

CONCLUSION

We conducted a comprehensive scoping review emphasizing on the interaction of SAR with AD patients, with a specific focus on daily life support, cognitive assessment, and cognitive therapy. We discussed our findings' pertinence relative to specific populations, interventions, and outcomes of human-SAR interaction on users and identified current knowledge gaps in SARs for AD patients.

Robot-based solution for helping Alzheimer patients

Alzheimer's is a progressive and debilitating neurological disorder characterized by cognitive decline, memory loss, and impaired daily functioning. It is an irreversible brain disease that destroys memory, thinking, and the ability to carry out daily activities. It poses significant challenges for patients and healthcare providers. Modern societies are trying to enhance the quality of people's lives, including Alzheimer's patients. In this study, we explored the potential of social robots to provide emotional support, improve cognitive function, and facilitate communication among Alzheimer's patients. This was achieved by initiating conversations on various topics such as family, relationships, and daily activities. This paper contributes to the literature by introducing a novel and well-organized framework for building an Alzheimer's care robot. Further, this study enriches the literature by introducing the Alzheimer Care Companion Robot (ACCR), designed to identify Alzheimer's patients. The ACCR initiates conversations in the native Arab-Kuwaiti dialect, displaying relevant memories through images and videos on its screen to assist in memory recall based on the individuals' life experiences. The proposed ACCR consists of 271 conversations belonging to three main categories: active, proactive, and graphical user interface (GUI) dialogs comprising 112 dialogs, 109 dialogs, and 50 dialogs for active, proactive, and GUI, respectively. The experimental result illustrated the success of the proposed solution.

AI-based assessments of speech and language impairments in dementia

Recent advancements in the artificial intelligence (AI) domain have revolutionized the early detection of cognitive impairments associated with dementia. This has motivated clinicians to use AI-powered dementia detection systems, particularly systems developed based on individuals' and patients' speech and language, for a quick and accurate identification of patients with dementia. This paper reviews articles about developing assessment tools using machine learning and deep learning algorithms trained by vocal and textual datasets.

Robots in Assisted Living Facilities: Scoping Review

BACKGROUND

Various technological interventions have been proposed and studied to address the growing demand for care of residents in assisted living facilities, in which a preexisting shortage of professional caregivers has been exacerbated by the COVID-19 pandemic. Care robots are one such intervention with the potential to improve both the care of older adults and the work life of their professional caregivers. However, concerns about efficacy, ethics, and best practices in the applications of robotic technologies in care settings remain.

OBJECTIVE

This scoping review aimed to examine the literature on robots used in assisted living facilities and identify gaps in the literature to guide future research.

METHODS

On February 12, 2022, following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) protocol, we searched PubMed, CINAHL Plus with Full Text, PsycINFO, IEEE Xplore digital library, and ACM Digital Library using predetermined search terms. Publications were included if they were written in English and focused on the use of robotics in assisted living facilities. Publications were excluded if they did not provide peer-reviewed empirical data, focused on user needs, or developed an instrument to study human-robot interaction. The study findings were then summarized, coded, and analyzed using the Patterns, Advances, Gaps, Evidence for practice, and Research recommendations framework.

RESULTS

The final sample included 73 publications from 69 unique studies on the use of robots in assisted living facilities. The findings of studies on older adults were mixed, with some studies suggesting positive impacts of robots, some expressing concerns about robots and barriers to their use, and others being inconclusive. Although many therapeutic benefits of care robots have been identified, methodological limitations have weakened the internal and external validity of the findings of these studies. Few studies (18/69, 26%) considered the context of care: most studies (48/69, 70%) collected data only on recipients of care, 15 studies collected data on staff, and 3 studies collected data on relatives or visitors. Theory-driven, longitudinal, and large sample size study designs were rare. Across the authors' disciplines, a lack of consistency in methodological quality and reporting makes it difficult to synthesize and assess research on care robotics.

CONCLUSIONS

The findings of this study call for more systematic research on the feasibility and efficacy of robots in assisted living facilities. In particular, there is a dearth of research on how robots may change geriatric care and the work environment within assisted living facilities. To maximize the benefits and minimize the consequences for older adults and caregivers, future research will require interdisciplinary collaboration among health sciences, computer science, and engineering as well as agreement on methodological standards.

Robots and Robotics in Nursing

Technological advancements have led to the use of robots as prospective partners to complement understaffing and deliver effective care to patients. This article discusses relevant concepts on robots from the perspective of nursing theories and robotics in nursing and examines the distinctions between human beings and healthcare robots as partners and robot development examples and challenges. Robotics in nursing is an interdisciplinary discipline that studies methodologies, technologies, and ethics for developing robots that support and collaborate with physicians, nurses, and other healthcare workers in practice. Robotics in nursing is geared toward learning the knowledge of robots for better nursing care, and for this purpose, it is also to propose the necessary robots and develop them in collaboration with engineers. Two points were highlighted regarding the use of robots in health care practice: issues of replacing humans because of human resource understaffing and concerns about robot capabilities to engage in nursing practice grounded in caring science. This article stresses that technology and artificial intelligence are useful and practical for patients. However, further research is required that considers what robotics in nursing means and the use of robotics in nursing.

The use of technology for social interaction by people with dementia: A scoping review

People with dementia (PwD) are at risk of experiencing loneliness, which is associated with physical and mental health difficulties [1]. Technology is a possible tool to increase social connection and reduce loneliness. This scoping review aims to examine the current evidence regarding the use of technology to reduce loneliness in PwD. A scoping review was carried out. Medline, PsychINFO, Embase, CINAHL, Cochrane database, NHS Evidence, Trials register, Open Grey, ACM Digital Library and IEEE Xplore were searched in April 2021. A sensitive search strategy was constructed using combinations of free text and thesaurus terms to retrieve articles about dementia, technology and social-interaction. Pre-defined inclusion and exclusion criteria were used. Paper quality was assessed using the Mixed Methods Appraisal Tool (MMAT) and results reported according to PRISMA guidelines [2,3]. 73 papers were identified publishing the results of 69 studies. Technological interventions included robots, tablets/computers and other forms of technology. Methodologies were varied and limited synthesis was possible. There is some evidence that technology is a beneficial intervention to reduce loneliness. Important considerations include personalisation and the context of the intervention. The current evidence is limited and variable; future research is warranted including studies with specific loneliness outcome measures, studies focusing on PwD living alone, and technology as part of intervention programmes.

A simulated experiment to explore robotic dialogue strategies for people with dementia

Introduction

Persons with dementia (PwDs) often show symptoms of repetitive questioning, which brings great burdens on caregivers. Conversational robots hold promise of helping cope with PwDs’ repetitive behavior. This paper develops an adaptive conversation strategy to answer PwDs’ repetitive questions, follow up with new questions to distract PwDs from repetitive behavior, and stimulate their conversation and cognition.

Methods

We propose a general reinforcement learning model to interact with PwDs with repetitive questioning. Q-learning is exploited to learn adaptive conversation strategy (from the perspectives of rate and difficulty level of follow-up questions) for four simulated PwDs. A demonstration is presented using a humanoid robot.

Results

The designed Q-learning model performs better than random action selection model. The RL-based conversation strategy is adaptive to PwDs with different cognitive capabilities and engagement levels. In the demonstration, the robot can answer a user’s repetitive questions and further come up with a follow-up question to engage the user in continuous conversations.

Conclusions

The designed Q-learning model demonstrates noteworthy effectiveness in adaptive action selection. This may provide some insights towards developing conversational social robots to cope with repetitive questioning by PwDs and increase their quality of life.

Socially assistive robots for people with dementia: Systematic review and meta-analysis of feasibility, acceptability and the effect on cognition, neuropsychiatric symptoms and quality of life

BACKGROUND

There is increasing interest in using robots to support dementia care but little consensus on the evidence for their use. The aim of the study is to review evidence about feasibility, acceptability and clinical effectiveness of socially assistive robots used for people with dementia.

METHOD

We conducted a systematic review and meta-analysis. We searched MEDLINE, EMBASE, PsychINFO, CINHAL, IEEE Xplore Digital Library, and EI Engineering Village from inception to 04 - 02-2022 - included primary studies assessing feasibility, acceptability, or effectiveness of socially assistive robots for people with dementia. Two independent reviewers screened studies for eligibility, and assessed quality. Narrative synthesis prioritized higher quality studies, and random-effect meta-analyses compared robots with usual care (UC) or active control (AC) immediately after the intervention (short-term; ST) or long-term (LT) on cognition, neuropsychiatric symptoms, and quality of life.

FINDINGS

66 studies and four categories of robots were eligible: Companion robots (Pet and humanoid companion robots), telepresence communication robots, homecare assistive robots and multifunctional robots. PARO (companion robot seal) was feasible and acceptable but limited by its weight, cost, and sound. On meta-analysis, PARO had no ST or LT compared to UC or AC over 5-12 weeks on agitation (ST vs UC, 4 trials, 153 participants: pooled standardized mean difference (SMD) 0.25; - 0.57 to 0.06; LT vs UC; 2 trials, 77 participants, SMD = -0.24; - 0.94, 0.46), cognition (ST vs UC, 3 trials, 128 participants: SMD= 0.03; -0.32, 0.38), overall neuropsychiatric symptoms (ST vs UC, 3 trials, 169 participants: SMD= -0.01; -0.32, 0.29; ST vs AC, 2 trials, 145 participants: SMD =0.02, -0.71, 0.85), apathy (ST vs AC, 2 trials, 81 participants: SMD= 0.14; 0.29, 0.58), depression (ST vs UC, 4 trials, 181 participants; SMD= 0.08; -0.52, 0.69; LT vs UC: 2 trials, 77 participants: SMD =0.01; -0.75, 0.77), anxiety (ST vs UC: 2 trials, 104 participants, SMD= 0.24; -0.85, 1.33) and quality of life (ST vs UC, 2 trials, 127 participants: SMD=-0.05; -0.52, 0.42; ST vs AC: 2 trials, 159 participants, SMD =-0.36, -0.76, 0.05). Robotic animals, humanoid companion robots, telepresence robots and multifunctional robots were feasible and acceptable. However, humanoid companion robots have speech recognition problems, and telepresence robots and multifunctional robots were often difficult to use. There was mixed evidence about the feasibility of homecare robots. There was little evidence on any of these robots' effectiveness.

CONCLUSION

Although robots were generally feasible and acceptable, there is no clear evidence that people with dementia derive benefit from robots for cognition, neuropsychiatric symptoms, or quality of life. We recommend that future research should use high quality designs to establish evidence of effectiveness.

Introducing CARESSER: A framework for in situ learning robot social assistance from expert knowledge and demonstrations

Socially assistive robots have the potential to augment and enhance therapist's effectiveness in repetitive tasks such as cognitive therapies. However, their contribution has generally been limited as domain experts have not been fully involved in the entire pipeline of the design process as well as in the automatisation of the robots' behaviour. In this article, we present aCtive leARning agEnt aSsiStive bEhaviouR (CARESSER), a novel framework that actively learns robotic assistive behaviour by leveraging the therapist's expertise (knowledge-driven approach) and their demonstrations (data-driven approach). By exploiting that hybrid approach, the presented method enables in situ fast learning, in a fully autonomous fashion, of personalised patient-specific policies. With the purpose of evaluating our framework, we conducted two user studies in a daily care centre in which older adults affected by mild dementia and mild cognitive impairment (N = 22) were requested to solve cognitive exercises with the support of a therapist and later on of a robot endowed with CARESSER. Results showed that: (i) the robot managed to keep the patients' performance stable during the sessions even more so than the therapist; (ii) the assistance offered by the robot during the sessions eventually matched the therapist's preferences. We conclude that CARESSER, with its stakeholder-centric design, can pave the way to new AI approaches that learn by leveraging human-human interactions along with human expertise, which has the benefits of speeding up the learning process, eliminating the need for the design of complex reward functions, and finally avoiding undesired states.

Preserving Narrative Identity for Dementia Patients: Embodiment, Active Environments, and Distributed Memory

One goal of this paper is to argue that autobiographical memories are extended and distributed across embodied brains and environmental resources. This is important because such distributed memories play a constitutive role in our narrative identity. So, some of the building blocks of our narrative identity are not brain-bound but extended and distributed. Recognising the distributed nature of memory and narrative identity, invites us to find treatments and strategies focusing on the environment in which dementia patients are situated. A second goal of this paper is to suggest various of such strategies, including lifelogging technologies such as SenseCams, life story books, multimedia biographies, memory boxes, ambient intelligence systems, and virtual reality applications. Such technologies allow dementia patients to remember their personal past in a way that wouldn’t be possible by merely relying on their biological memory, in that way aiding in preserving their narrative identity and positively contributing to their sense of well-being.

An analysis of design recommendations for socially assistive robot helpers for effective human-robot interactions in senior care

As the global population ages, there is an increase in demand for assistive technologies that can alleviate the stresses on healthcare systems. The growing field of socially assistive robotics (SARs) offers unique solutions that are interactive, engaging, and adaptable to different users’ needs. Crucial to having positive human-robot interaction (HRI) experiences in senior care settings is the overall design of the robot, considering the unique challenges and opportunities that come with novice users. This paper presents a novel study that explores the effect of SAR design on HRI in senior care through a results-oriented analysis of the literature. We provide key design recommendations to ensure inclusion for a diverse set of users. Open challenges of considering user preferences during design, creating adaptive behaviors, and developing intelligent autonomy are discussed in detail. SAR features of appearance and interaction mode along with SAR frameworks for perception and intelligence are explored to evaluate individual developments using metrics such as trust, acceptance, and intent to use. Drawing from a diverse set of features, SAR frameworks, and HRI studies, the discussion highlights robot characteristics of greatest influence in promoting wellbeing and aging-in-place of older adults and generates design recommendations that are important for future development.

Natural Language Processing as an Emerging Tool to Detect Late-Life Depression

Late-life depression (LLD) is a major public health concern. Despite the availability of effective treatments for depression, barriers to screening and diagnosis still exist. The use of current standardized depression assessments can lead to underdiagnosis or misdiagnosis due to subjective symptom reporting and the distinct cognitive, psychomotor, and somatic features of LLD. To overcome these limitations, there has been a growing interest in the development of objective measures of depression using artificial intelligence (AI) technologies such as natural language processing (NLP). NLP approaches focus on the analysis of acoustic and linguistic aspects of human language derived from text and speech and can be integrated with machine learning approaches to classify depression and its severity. In this review, we will provide rationale for the use of NLP methods to study depression using speech, summarize previous research using NLP in LLD, compare findings to younger adults with depression and older adults with other clinical conditions, and discuss future directions including the use of complementary AI strategies to fully capture the spectrum of LLD.

Robotic Telemedicine for Mental Health: A Multimodal Approach to Improve Human-Robot Engagement

COVID-19 has severely impacted mental health in vulnerable demographics, in particular older adults, who face unprecedented isolation. Consequences, while globally severe, are acutely pronounced in low- and middle-income countries (LMICs) confronting pronounced gaps in resources and clinician accessibility. Social robots are well-recognized for their potential to support mental health, yet user compliance (i.e., trust) demands seamless affective human-robot interactions; natural 'human-like' conversations are required in simple, inexpensive, deployable platforms. We present the design, development, and pilot testing of a multimodal robotic framework fusing verbal (contextual speech) and nonverbal (facial expressions) social cues, aimed to improve engagement in human-robot interaction and ultimately facilitate mental health telemedicine during and beyond the COVID-19 pandemic. We report the design optimization of a hybrid face robot, which combines digital facial expressions based on mathematical affect space mapping with static 3D facial features. We further introduce a contextual virtual assistant with integrated cloud-based AI coupled to the robot's facial representation of emotions, such that the robot adapts its emotional response to users' speech in real-time. Experiments with healthy participants demonstrate emotion recognition exceeding 90% for happy, tired, sad, angry, surprised and stern/disgusted robotic emotions. When separated, stern and disgusted are occasionally transposed (70%+ accuracy overall) but are easily distinguishable from other emotions. A qualitative user experience analysis indicates overall enthusiastic and engaging reception to human-robot multimodal interaction with the new framework. The robot has been modified to enable clinical telemedicine for cognitive engagement with older adults and people with dementia (PwD) in LMICs. The mechanically simple and low-cost social robot has been deployed in pilot tests to support older individuals and PwD at the Schizophrenia Research Foundation (SCARF) in Chennai, India. A procedure for deployment addressing challenges in cultural acceptance, end-user acclimatization and resource allocation is further introduced. Results indicate strong promise to stimulate human-robot psychosocial interaction through the hybrid-face robotic system. Future work is targeting deployment for telemedicine to mitigate the mental health impact of COVID-19 on older adults and PwD in both LMICs and higher income regions.