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.
This feasibility and pilot study aimed to develop and field-test a 14-session virtual Cognitive Stimulation Therapy (vCST) programme for people living with dementia, developed as a result of services moving online during the COVID-19 pandemic. Methods: The vCST protocol was developed using the existing group CST manual, through stakeholder consultation with people living with dementia, caregivers, CST group facilitators and dementia service managers. This protocol was then field-tested with 10 groups of people living with dementia in the Brazil, China (Hong Kong), India, Ireland and the UK, and feedback on the protocol was gathered from 14 facilitators. Results: Field testing in five countries indicated acceptability to group facilitators and participants. Feedback from these groups was used to refine the developed protocol. The final vCST protocol is proposed, including session materials for delivery of CST over videoconferencing and a framework for offering CST virtually in global settings. Conclusion: vCST is a feasible online intervention for many people living with dementia. We recommend that it is offered to those unable to access traditional in-person CST for health reasons, lack of transport or COVID-19 restrictions. Further research is needed to explore if participant outcomes are comparable to in-person CST groups.
The anticipated introduction of disease-modifying agents for the treatment of Alzheimer’s disease (AD) highlights the need for its early and accurate detection. This article provides an overview of the objective statistical voxel-based image processing and analyses technology that make early detection of AD with 18F-fluorodeoxyglucose (FDG)-PET possible. Our report demonstrates that the comparison of a single FDG-PET scan with a group of control scans provides an objective statistical map that is useful for the detection of early stages of AD, augmenting visual inspection of the PET image itself. The need for early detection of AD, together with the power of voxel-based statistical analyses, provides an impetus for agencies to re-evaluate FDG-PET as an approved methodology for the early diagnosis of AD. The expected approval of disease-modifying agents for the treatment of AD places more emphasis on the need for earlier diagnosis of this common and devastating disorder.
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