A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound.

Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging, concentrating on studies that compare self-supervised pretraining to fully supervised learning for diagnostic tasks such as classification and segmentation. The most pertinent finding is that self-supervised pretraining generally improves downstream task performance compared to full supervision, most prominently when unlabelled examples greatly outnumber labelled examples. Based on the aggregate evidence, recommendations are provided for practitioners considering using self-supervised learning. Motivated by limitations identified in current research, directions and practices for future study are suggested, such as integrating clinical knowledge with theoretically justified self-supervised learning methods, evaluating on public datasets, growing the modest body of evidence for ultrasound, and characterizing the impact of self-supervised pretraining on generalization.

Disruption of Social Orders in Societal Transitions as Affective Control of Uncertainty.

Bayesian affect control theory is a model of affect-driven social interaction under conditions of uncertainty. In this paper, we investigate how the operationalization of uncertainty in the model can be related to the disruption of social orders-societal pressures to adapt to ongoing environmental and technological change. First, we study the theoretical tradeoffs between three kinds of uncertainty as groups navigate external problems: validity (the predictability of the environment, including of other agents), coherence (the predictability of interpersonal affective dynamics), and dependence (the predictability of affective meanings). Second, we discuss how these uncertainty tradeoffs are related to contemporary political conflict and polarization in the context of societal transitions. To illustrate the potential of our model to analyze the socio-emotional consequences of uncertainty, we present a simulation of diverging individual affective meanings of occupational identities under uncertainty in a climate change mitigation scenario based on events in Germany. Finally, we sketch a possible research agenda to substantiate the novel, but yet mostly conjectural, ideas put forward in this paper.

How Cultural Meanings of Occupations in the U.S. Changed During the Covid-19 Pandemic.

Social research highlights the stability of cultural beliefs, broadly arguing that population-level changes are uncommon and mostly explained by cohort replacement rather than individual-level change. We find evidence suggesting that cultural change may also occur rapidly in response to an economically and socially transformative period. Using data collected just before and after the outbreak of Covid-19 in the U.S., we explore whether cultural beliefs about essential and non-essential occupations are dynamic in the face of an exogenous social and economic shock. Using a sample of respondents whose characteristics match the U.S. Census on sex, age, and race/ethnicity, we fielded surveys measuring cultural beliefs about 85 essential and non-essential occupations using the evaluation, potency, and activity (EPA) dimensions from the Affect Control Theory paradigm. We expected that EPA ratings of essential work identities would increase due to positive media coverage of essential occupations as indispensable and often selfless roles in the pandemic, while EPA ratings of non-essential identities would decline. Our findings show patterns that are both clear and inconsistent with our predictions. For both essential and non-essential occupations, almost all statistically significant changes in mean evaluation and potency were negative; activity showed relatively little change. Changes in evaluation scores were more negative for non-essential occupations than essential occupations. Results suggest that pervasive and persistent exogenous events are worth investigating as potential sources of episodic cultural belief change.

Towards emotionally aligned social robots for dementia: perspectives of care partners and persons with dementia.

BACKGROUND: Persons living with dementia and their care partners place a high value on aging in place and maintaining independence. Socially assistive robots - embodied characters or pets that provide companionship and aid through social interaction - are a promising tool to support these goals. There is a growing commercial market for these devices, with functions including medication reminders, conversation, pet-like behaviours, and even the collection of health data. While potential users generally report positive feelings towards social robots, persons with dementia have been under-included in design and development, leading to a disconnect between robot functions and the real-world needs and desires of end-users. Furthermore, a key element of social and emotional connectedness in human relationships is emotional alignment - a state where all partners have congruent emotional understandings of a situation. Strong emotional alignment between users and robots will be necessary for social robots to provide meaningful companionship, but a computational model of how to achieve this has been absent from the field. To this end, we propose and test Affect Control Theory (ACT) as a framework to improve emotional alignment between older adults and social robotics. METHOD: Using a Canadian online survey, we introduced respondents to three exemplar social robots with older adult-specific functionalities and evaluated their responses around features, emotions, and ethics using standardized and novel measures (n=171 older adults, n=28 care partners, and n=7 persons living with dementia). RESULT: Overall, participants responded positively to the robots. High priority uses included companionship, interaction, and safety. Reasoning around robot use was pragmatic; curiosity and entertainment were motivators to use, while a perceived lack of need and the mechanical appearance of the robots were detractors. Realistic, cute, and cuddly robots were preferred while artificial-looking, creepy, and toy-like robots were disliked. Most importantly, our evidence supported ACT as a viable model of human-robot emotional alignment. CONCLUSION: This work supports the development of emotionally sophisticated, evidence-based, and user-centered social robotics with older adult- and dementia-specific functionality.

Equality and Freedom as Uncertainty in Groups.

In this paper, I investigate a connection between a common characterisation of freedom and how uncertainty is managed in a Bayesian hierarchical model. To do this, I consider a distributed factorization of a group's optimization of free energy, in which each agent is attempting to align with the group and with its own model. I show how this can lead to equilibria for groups, defined by the capacity of the model being used, essentially how many different datasets it can handle. In particular, I show that there is a "sweet spot" in the capacity of a normal model in each agent's decentralized optimization, and that this "sweet spot" corresponds to minimal free energy for the group. At the sweet spot, an agent can predict what the group will do and the group is not surprised by the agent. However, there is an asymmetry. A higher capacity model for an agent makes it harder for the individual to learn, as there are more parameters. Simultaneously, a higher capacity model for the group, implemented as a higher capacity model for each member agent, makes it easier for a group to integrate a new member. To optimize for a group of agents then requires one to make a trade-off in capacity, as each individual agent seeks to decrease capacity, but there is pressure from the group to increase capacity of all members. This pressure exists because as individual agent's capacities are reduced, so too are their abilities to model other agents, and thereby to establish pro-social behavioural patterns. I then consider a basic two-level (dual process) Bayesian model of social reasoning and a set of three parameters of capacity that are required to implement such a model. Considering these three capacities as dependent elements in a free energy minimization for a group leads to a "sweet surface" in a three-dimensional space defining the triplet of parameters that each agent must use should they hope to minimize free energy as a group. Finally, I relate these three parameters to three notions of freedom and equality in human social organization, and postulate a correspondence between freedom and model capacity. That is, models with higher capacity, have more freedom as they can interact with more datasets.

Technology and Dementia: The Future is Now.

BACKGROUND: Technology has multiple potential applications to dementia from diagnosis and assessment to care delivery and supporting ageing in place. OBJECTIVES: To summarise key areas of technology development in dementia and identify future directions and implications. METHOD: Members of the US Alzheimer's Association Technology Professional Interest Area involved in delivering the annual pre-conference summarised existing knowledge on current and future technology developments in dementia. RESULTS: The main domains of technology development are as follows: (i) diagnosis, assessment and monitoring, (ii) maintenance of functioning, (iii) leisure and activity, (iv) caregiving and management. CONCLUSIONS: The pace of technology development requires urgent policy, funding and practice change, away from a narrow medical approach, to a holistic model that facilitates future risk reduction and prevention strategies, enables earlier detection and supports implementation at scale for a meaningful and fulfilling life with dementia.

Ethical adoption: A new imperative in the development of technology for dementia.

INTRODUCTION: Technology interventions are showing promise to assist persons with dementia and their carers. However, low adoption rates for these technologies and ethical considerations have impeded the realization of their full potential. METHODS: Building on recent evidence and an iterative framework development process, we propose the concept of "ethical adoption": the deep integration of ethical principles into the design, development, deployment, and usage of technology. RESULTS: Ethical adoption is founded on five pillars, supported by empirical evidence: (1) inclusive participatory design; (2) emotional alignment; (3) adoption modelling; (4) ethical standards assessment; and (5) education and training. To close the gap between adoption research, ethics and practice, we propose a set of 18 practical recommendations based on these ethical adoption pillars. DISCUSSION: Through the implementation of these recommendations, researchers and technology developers alike will benefit from evidence-informed guidance to ensure their solution is adopted in a way that maximizes the benefits to people with dementia and their carers while minimizing possible harm.

Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia.

Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials.

Information and communication technology solutions for outdoor navigation in dementia.

INTRODUCTION: Information and communication technology (ICT) is potentially mature enough to empower outdoor and social activities in dementia. However, actual ICT-based devices have limited functionality and impact, mainly limited to safety. What is an ideal operational framework to enhance this field to support outdoor and social activities? METHODS: Review of literature and cross-disciplinary expert discussion. RESULTS: A situation-aware ICT requires a flexible fine-tuning by stakeholders of system usability and complexity of function, and of user safety and autonomy. It should operate by artificial intelligence/machine learning and should reflect harmonized stakeholder values, social context, and user residual cognitive functions. ICT services should be proposed at the prodromal stage of dementia and should be carefully validated within the life space of users in terms of quality of life, social activities, and costs. DISCUSSION: The operational framework has the potential to produce ICT and services with high clinical impact but requires substantial investment.

User Perception of Smart Home Surveillance Among Adults Aged 50 Years and Older: Scoping Review.

BACKGROUND: Smart home technology (SHT) can be useful for aging in place or health-related purposes. However, surveillance studies have highlighted ethical issues with SHTs, including user privacy, security, and autonomy. OBJECTIVE: As digital technology is most often designed for younger adults, this review summarizes perceptions of SHTs among users aged 50 years and older to explore their understanding of privacy, the purpose of data collection, risks and benefits, and safety. METHODS: Through an integrative review, we explored community-dwelling adults' (aged 50 years and older) perceptions of SHTs based on research questions under 4 nonmutually exclusive themes: privacy, the purpose of data collection, risk and benefits, and safety. We searched 1860 titles and abstracts from Ovid MEDLINE, Ovid Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials, Scopus, Web of Science Core Collection, and IEEE Xplore or IET Electronic Library, resulting in 15 included studies. RESULTS: The 15 studies explored user perception of smart speakers, motion sensors, or home monitoring systems. A total of 13 (87%) studies discussed user privacy concerns regarding data collection and access. A total of 4 (27%) studies explored user knowledge of data collection purposes, 7 (47%) studies featured risk-related concerns such as data breaches and third-party misuse alongside benefits such as convenience, and 9 (60%) studies reported user enthusiasm about the potential for home safety. CONCLUSIONS: Due to the growing size of aging populations and advances in technological capabilities, regulators and designers should focus on user concerns by supporting higher levels of agency regarding data collection, use, and disclosure and by bolstering organizational accountability. This way, relevant privacy regulation and SHT design can better support user safety while diminishing potential risks to privacy, security, autonomy, or discriminatory outcomes.

Engaging older adults with dementia in creative occupations using artificially intelligent assistive technology.

Engagement in creative occupations has been shown to promote well-being for older adults with dementia. Providing access to such occupations is often difficult, as successful participation requires face-time with a person who is knowledgeable in facilitating engagement as well as access to any required resources, such as an arts studio. In response, a computer-based device, the Engaging Platform for Art Development (ePAD), was created to with the aim of enabling more independent access to art creation, ePAD is a an artificially intelligent touch-screen device that estimates a client's level of engagement and provides prompts to encourage engagement if the client becomes disengaged. ePAD is customizable such that an art therapist can choose themes and tools that they feel reflect their client's needs and preferences. This article presents a mixed-methods study that evaluated ePAD's usability by six older adult (with mild-to-moderate dementia) and art therapist dyads. Usability measures suggest that all participants found ePAD engaging but did not find prompts effective. Future development of ePAD includes improving the prompts, implementing the recommendations made by participants in this research, and long-term testing in more naturalistic art therapy contexts.

VIPCare: Understanding the support needed to create affective interactions between new caregivers and residents with dementia.

INTRODUCTION: In this paper, we study the support needed by professional caregivers of those with dementia, and present a first step toward development of VIPCare, a novel application with the goal of assisting new caregivers at care-centres in interacting with residents with dementia. METHODS: A mixed-methods study including two questionnaires, two focus groups, and seven co-design sessions with 17 professional caregivers was conducted to (a) understand caregivers' challenges/approaches used to reduce negative interactions with persons with dementia, (b) identify the existing gaps in supporting information for improving such interactions, and (c) co-design the user interface of an application that aims to help improve interactions between a new professional caregiver and persons with dementia. A pre-questionnaire assessed knowledge of smartphones and attitude toward technology. A post-questionnaire provided an initial evaluation of the designed user interface. RESULTS: Focus groups emphasized the importance of role-playing learned through trial and error. The layout/content of the application was then designed in four iterative paper-prototyping sessions with professional caregivers. An iOS/Android-based application was developed accordingly and was modified/improved in three iterative sessions. The initial results supported efficiency of VIPCare and suggested a low task load index. CONCLUSIONS: We presented a first step toward understanding caregiver needs and developing an application that can help reduce negative interactions between professional caregivers and those with dementia.

User perspectives on emotionally aligned social robots for older adults and persons living with dementia.

Introduction: Socially assistive robots are devices designed to aid users through social interaction and companionship. Social robotics promise to support cognitive health and aging in place for older adults with and without dementia, as well as their care partners. However, while new and more advanced social robots are entering the commercial market, there are still major barriers to their adoption, including a lack of emotional alignment between users and their robots. Affect Control Theory (ACT) is a framework that allows for the computational modeling of emotional alignment between two partners. Methods: We conducted a Canadian online survey capturing attitudes, emotions, and perspectives surrounding pet-like robots among older adults (n = 171), care partners (n = 28), and persons living with dementia (n = 7). Results: We demonstrate the potential of ACT to model the emotional relationship between older adult users and three exemplar robots. We also capture a rich description of participants' robot attitudes through the lens of the Technology Acceptance Model, as well as the most important ethical concerns around social robot use. Conclusions: Findings from this work will support the development of emotionally aligned, user-centered robots for older adults, care partners, and people living with dementia.

The development of an adaptive upper-limb stroke rehabilitation robotic system.

BACKGROUND: Stroke is the primary cause of adult disability. To support this large population in recovery, robotic technologies are being developed to assist in the delivery of rehabilitation. This paper presents an automated system for a rehabilitation robotic device that guides stroke patients through an upper-limb reaching task. The system uses a decision theoretic model (a partially observable Markov decision process, or POMDP) as its primary engine for decision making. The POMDP allows the system to automatically modify exercise parameters to account for the specific needs and abilities of different individuals, and to use these parameters to take appropriate decisions about stroke rehabilitation exercises. METHODS: The performance of the system was evaluated by comparing the decisions made by the system with those of a human therapist. A single patient participant was paired up with a therapist participant for the duration of the study, for a total of six sessions. Each session was an hour long and occurred three times a week for two weeks. During each session, three steps were followed: (A) after the system made a decision, the therapist either agreed or disagreed with the decision made; (B) the researcher had the device execute the decision made by the therapist; (C) the patient then performed the reaching exercise. These parts were repeated in the order of A-B-C until the end of the session. Qualitative and quantitative question were asked at the end of each session and at the completion of the study for both participants. RESULTS: Overall, the therapist agreed with the system decisions approximately 65% of the time. In general, the therapist thought the system decisions were believable and could envision this system being used in both a clinical and home setting. The patient was satisfied with the system and would use this system as his/her primary method of rehabilitation. CONCLUSIONS: The data collected in this study can only be used to provide insight into the performance of the system since the sample size was limited. The next stage for this project is to test the system with a larger sample size to obtain significant results.

The COACH prompting system to assist older adults with dementia through handwashing: an efficacy study.

BACKGROUND: Many older adults with dementia require constant assistance from a caregiver when completing activities of daily living (ADL). This study examines the efficacy of a computerized device intended to assist people with dementia through ADL, while reducing caregiver burden. The device, called COACH, uses artificial intelligence to autonomously guide an older adult with dementia through the ADL using audio and/or audio-video prompts. METHODS: Six older adults with moderate-to-severe dementia participated in this study. Handwashing was chosen as the target ADL. A single subject research design was used with two alternating baseline (COACH not used) and intervention (COACH used) phases. The data were analyzed to investigate the impact of COACH on the participants' independence and caregiver burden as well as COACH's overall performance for the activity of handwashing. RESULTS: Participants with moderate-level dementia were able to complete an average of 11% more handwashing steps independently and required 60% fewer interactions with a human caregiver when COACH was in use. Four of the participants achieved complete or very close to complete independence. Interestingly, participants' MMSE scores did not appear to robustly coincide with handwashing performance and/or responsiveness to COACH; other idiosyncrasies of each individual seem to play a stronger role. While the majority (78%) of COACH's actions were considered clinically correct, areas for improvement were identified. CONCLUSION: The COACH system shows promise as a tool to help support older adults with moderate-levels of dementia and their caregivers. These findings reinforce the need for flexibility and dynamic personalization in devices designed to assist older adults with dementia. After addressing identified improvements, the authors plan to run clinical trials with a sample of community-dwelling older adults and caregivers.

An intelligent powered wheelchair to enable mobility of cognitively impaired older adults: an anticollision system.

Older adults with cognitive impairments are generally prohibited from using powered wheelchairs, because of the high risk of collisions with people and objects. This paper describes and presents the preliminary results of a system that uses an infrared sensor to provide anticollision and a prompting system for a powered wheelchair that helps guide the user safely past obstacles. Trials with the prototyped system detected collisions and stopped the chair in 95% of trials with an object and generated no false alarms.

Value-directed human behavior analysis from video using partially observable Markov decision processes.

This paper presents a method for learning decision theoretic models of human behaviors from video data. Our system learns relationships between the movements of a person, the context in which they are acting, and a utility function. This learning makes explicit that the meaning of a behavior to an observer is contained in its relationship to actions and outcomes. An agent wishing to capitalize on these relationships must learn to distinguish the behaviors according to how they help the agent to maximize utility. The model we use is a partially observable Markov decision process, or POMDP. The video observations are integrated into the POMDP using a dynamic Bayesian network that creates spatial and temporal abstractions amenable to decision making at the high level. The parameters of the model are learned from training data using an a posteriori constrained optimization technique based on the expectation-maximization algorithm. The system automatically discovers classes of behaviors and determines which are important for choosing actions that optimize over the utility of possible outcomes. This type of learning obviates the need for labeled data from expert knowledge about which behaviors are significant and removes bias about what behaviors may be useful to recognize in a particular situation. We show results in three interactions: a single player imitation game, a gestural robotic control problem, and a card game played by two people.

Review of fall detection techniques: A data availability perspective.

A fall is an abnormal activity that occurs rarely; however, missing to identify falls can have serious health and safety implications on an individual. Due to the rarity of occurrence of falls, there may be insufficient or no training data available for them. Therefore, standard supervised machine learning methods may not be directly applied to handle this problem. In this paper, we present a taxonomy for the study of fall detection from the perspective of availability of fall data. The proposed taxonomy is independent of the type of sensors used and specific feature extraction/selection methods. The taxonomy identifies different categories of classification methods for the study of fall detection based on the availability of their data during training the classifiers. Then, we present a comprehensive literature review within those categories and identify the approach of treating a fall as an abnormal activity to be a plausible research direction. We conclude our paper by discussing several open research problems in the field and pointers for future research.

Qualitative study of affective identities in dementia patients for the design of cognitive assistive technologies.

Our overall aim is to develop an emotionally intelligent cognitive assistant (ICA) to help older adults with Alzheimer's disease (AD) to complete activities of daily living more independently. For improved adoption, such a system should take into account how individuals feel about who they are. This paper investigates different affective identities found in older care home residents with AD, leading to a computational characterization of these aspects and, thus, tailored prompts to each specific individual's identity in a way that potentially ensures smoother and more effective uptake and response. We report on a set of qualitative interviews with 12 older adult care home residents and caregivers. The interview covered life domains (family, origin, occupation, etc.), and feelings related to the ICA. All interviews were transcribed and analyzed to extract a set of affective identities, coded according to the social-psychological principles of affect control theory (ACT). Preliminary results show that a set of identities can be extracted for each participant (e.g. father, husband). Furthermore, our results provide support for the proposition that, while identities grounded in memories fade as a person loses their memory, habitual aspects of identity that reflect the overall "persona" may persist longer, even without situational context.

A planning system based on Markov decision processes to guide people with dementia through activities of daily living.

Older adults with dementia often cannot remember how to complete activities of daily living and require a caregiver to aid them through the steps involved. The use of a computerized guidance system could potentially reduce the reliance on a caregiver. This paper examines the design and preliminary evaluation of a planning system that uses Markov decision processes (MDPs) to determine when and how to provide prompts to a user with dementia for guidance through the activity of handwashing. Results from the study suggest that MDPs can be applied effectively to this type of guidance problem. Considerations for the development of future guidance systems are presented.