Design and Development of the eBear: A Socially Assistive Robot for Elderly People with Depression

There has been tremendous progress in the field of robotics in the past decade and especially developing humanoid robots with social abilities that can assist human at a socio-emotional level. The objective of this thesis is to develop and study a perceptive and expressive animal-like robot equipped with artificial intelligence in assisting the elderly people with depression. We investigated how social robots can become companions of elderly individuals with depression and improve their mood and increase their happiness and well-being. The robotic platform built in this thesis is a bear-like robot called the eBear. The eBear can show facial expression and head gesture, can understand user\u27s emotion using audio-video sensory inputs and machine learning, can speak and show relatively accurate visual speech, and make dialog with users. the eBear can respond to their questions by querying the Internet, and even encourage them to physically be more active and even perform simple physical exercises. Besides building the robot, the eBear was used in running a pilot study in which seven elderly people with mild to severe depression interacted with the eBear for about 45 minutes three times a week over one month. The results of the study show that interacting with the eBear can increase happiness and mood of these human users as measured by Face Scale, and Geriatric Depression Scale (GDS) score systems. In addition, using Almere Model, it was concluded that the acceptance of the social agent increased over the study period. Videos of the users interaction with the eBear was analyzed and eye gaze, and facial expressions were manually annotated to better understand the behavior changes of users with the eBear. Results of these analyses as well as the exit surveys completed by the users at the end of the study demonstrate that a social robot such as the eBear can be an effective companion for the elderly people and can be a new approach for depression treatment

The Impact of Emotion Focused Features on SVM and MLR Models for Depression Detection

Major depressive disorder (MDD) is a common mental health diagnosis with estimates upwards of 25% of the United States population remain undiagnosed. Psychomotor symptoms of MDD impacts speed of control of the vocal tract, glottal source features and the rhythm of speech. Speech enables people to perceive the emotion of the speaker and MDD decreases the mood magnitudes expressed by an individual. This study asks the questions: “if high level features deigned to combine acoustic features related to emotion detection are added to glottal source features and mean response time in support vector machines and multivariate logistic regression models, would that improve the recall of the MDD class?” To answer this question, a literature review goes through common features in MDD detection, especially features related to emotion recognition. Using feature transformation, emotion recognition composite features are produced and added to glottal source features for model evaluation

Improving face perception and quality of life in age-related macular degeneration

The ability to see faces is essential for successful social interactions and good quality of life. Age-related macular degeneration (AMD) is a progressive eye condition that damages central vision required to see faces clearly. This thesis aims to investigate potential means to improve quality of life in AMD, via a two-pronged approach. The first prong examines the importance of face recognition difficulties, using a qualitative study of the effects of poor face perception in AMD on social interactions and quality of life. Previous studies of the impact of AMD on quality of life have focussed on domains including reading, driving, and self-care. Paper 1 of the thesis presents the first in-depth study of the quality-of-life impacts arising specifically from poor face perception. Results showed that, across all levels of vision loss (still driving through legally blind), AMD patients experience everyday problems with recognising who people are (face identity) and their emotions (facial expressions). These result in difficulties in social interactions, fear of offending others (e.g., appearing to ignore them deliberately), misinterpreting how others are feeling, and missing out in social situations. Patients also reported others did not understand their vision loss, and worried about appearing a fraud. These outcomes often contributed to social withdrawal and reduced confidence and quality of life. Paper 1 uses the study findings to develop new community resources (Faces and Social Life in AMD information sheet, conversation-starter, brochure for low-vision clinics), intended to improve patient and community understanding of how AMD affects face perception, and to provide practical tips for improving social interactions. The second prong focusses on improving face perception in AMD patients via image enhancement. The broad idea here is that, potentially, face images can be displayed to patients on screens or smart glasses after being digitally altered in ways that make them easier for patients to see and interpret. The specific image enhancement tested here is caricaturing, which involved exaggerating the shape information in the face image away from the average face (for face identity) or a neutral expression (for face expression). Paper 2 demonstrates that caricaturing can improve perception of identity in AMD; this benefit was observed for all eyes tested with mild vision loss, and half of eyes tested with moderate-to-severe vision loss. Paper 3 demonstrated that caricaturing can improve perception of facial expression in AMD, particularly for low-intensity expressions that are poorly recognised in their natural form, again across a wide range of vision loss. Overall, this thesis demonstrates that poor face perception in AMD is an important contributor to patients’ reduced quality of life. With the aim of enhancing quality of life, I have developed resources to improve community understanding, plus demonstrated that caricaturing provides a useful image enhancement method in AMD. Future research should focus on: further evaluation of the helpfulness of the community resources (to patients, carers and orthoptists); testing whether combining image enhancement methods (e.g., caricaturing plus contrast manipulations) can further improve face perception; and engineering advances needed to implement accurate caricaturing for patients in real-time

Development of Technologies for the Detection of (Cyber)Bullying Actions: The BullyBuster Project

Bullying and cyberbullying are harmful social phenomena that involve the intentional, repeated use of power to intimidate or harm others. The ramifications of these actions are felt not just at the individual level but also pervasively throughout society, necessitating immediate attention and practical solutions. The BullyBuster project pioneers a multi-disciplinary approach, integrating artificial intelligence (AI) techniques with psychological models to comprehensively understand and combat these issues. In particular, employing AI in the project allows the automatic identification of potentially harmful content by analyzing linguistic patterns and behaviors in various data sources, including photos and videos. This timely detection enables alerts to relevant authorities or moderators, allowing for rapid interventions and potential harm mitigation. This paper, a culmination of previous research and advancements, details the potential for significantly enhancing cyberbullying detection and prevention by focusing on the system’s design and the novel application of AI classifiers within an integrated framework. Our primary aim is to evaluate the feasibility and applicability of such a framework in a real-world application context. The proposed approach is shown to tackle the pervasive issue of cyberbullying effectively

Applications of Artificial Intelligence in Healthcare

Now in these days, artificial intelligence (AI) is playing a major role in healthcare. It has many applications in diagnosis, robotic surgeries, and research, powered by the growing availability of healthcare facts and brisk improvement of analytical techniques. AI is launched in such a way that it has similar knowledge as a human but is more efficient. A robot has the same expertise as a surgeon; even if it takes a longer time for surgery, its sutures, precision, and uniformity are far better than the surgeon, leading to fewer chances of failure. To make all these things possible, AI needs some sets of algorithms. In Artificial Intelligence, there are two key categories: machine learning (ML) and natural language processing (NPL), both of which are necessary to achieve practically any aim in healthcare. The goal of this study is to keep track of current advancements in science, understand technological availability, recognize the enormous power of AI in healthcare, and encourage scientists to use AI in their related fields of research. Discoveries and advancements will continue to push the AI frontier and expand the scope of its applications, with rapid developments expected in the future

Adjustment processes in chronic aphasia after stroke: Exploring multiple perspectives in the context of a community-based intervention

Background: The impact of chronic aphasia following stroke on quality of life (QOL) is widely acknowledged, with improved QOL recognised as an important outcome in aphasia recovery and supported by emerging quantitative measures. One of the key constructs recognised as contributing to QOL in other chronic conditions is psychosocial adjustment, the mechanisms of which are little understood for the person with aphasia. Aims: This study addressed adjustment processes in aphasia by exploring multiple perspectives from people engaged in the Communication Hub for Aphasia in North Tyneside (CHANT), a two-year community intervention for long-term aphasia. The study aimed to explore the adjustment process over time in people with aphasia using thematic analysis of personal narratives derived from a combination of sources: semi-structured interviews with reflections on experiences, quantitative measures of change in QOL and self-assessments of change. Methods & Procedures: Three people with mild or moderate chronic aphasia and three people without aphasia involved in CHANT were recruited (a carer, a volunteer, and a local government employee) to participate in semi-structured interviews at two- to three-month intervals over a 12-month period. A total of 28 semi-structured interviews were transcribed and analysed thematically by a small team using NVivo8 software. Narrative data were interpreted within the broader context of QOL measures and self-assessments of living with aphasia (Mumby & Whitworth, 2012).Outcomes & Results: Changes over time that reflected evidence of psychosocial adjustment from the multiple perspectives of the participants covered five core themes: Intervention type, Effectiveness, Barriers, Facilitators, and QOL. A model is proposed to encapsulate the barriers and facilitators that impacted on the process of adjustment and contributed to QOL for individuals involved in the intervention. This model is consistent with the domains from other classifications based on the International Classification of Functioning, Disability and Health (ICF; World Health Organization, 2001), viewing adjustment as a progression towards “wholeness”. The processes involved in personal (and specifically, emotional) adjustment to aphasia are explored, including three stages in rationalisation—Looking back, Looking around, and Looking forward—and the process of transforming negative emotional reactions into positive outcomes. Conclusions: The processes of adjustment in chronic aphasia are complex and vary both over time and according to individual perspectives and circumstances. This preliminary longitudinal study identified commonalities in participants engaged in long-term intervention over 12 months, enabling models of adjustment to be proposed for further exploration and evaluation

Identifying the supportive care needs of men and women affected by chemotherapy-induced alopecia? A systematic review.

Purpose: To systematically evaluate evidence regarding the unmet supportive care needs of men and women affected by chemotherapy-induced alopecia (CIA) to inform clinical practice guidelines. Methods: We performed a review of CINAHL, MEDLINE, PsychINFO, Scopus, the Cochrane Library (CCRT and CDSR) controlled trial databases and clinicaltrials.gov from January 1990 to June 2019 according to the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) statement. Twenty-seven publications were selected for inclusion in this analysis. Results: Included reports used qualitative (ten) and quantitative (17) studies. Across these studies men and women reported the major impact that CIA had on their psychological well-being, quality of life and body image. Hair loss had a negative impact irrespective of gender, which resulted in feelings of vulnerability and visibility of being a “cancer patient”. Men and women described negative feelings, often similar, related to CIA with a range of unmet supportive care needs. Conclusions: Some patients are not well-prepared for alopecia due to a lack of information and resources to reduce the psychological burden associated with CIA. Hair loss will affect each patient and their family differently, therefore, intervention and support must be tailored at an individual level of need to optimise psychological and physical well-being and recovery. Implications for Cancer Survivors: People affected by CIA may experience a range of unmet supportive care needs, and oncology doctors and nurses are urged to use these findings in their everyday consultations to ensure effective, person-centred care and timely intervention to minimise the sequalae associated with CIA