Designing ethical social robots - A longitudinal field study with older adults

Emotional deception and emotional attachment are regarded as ethical concerns in human robot interaction. Considering these concerns is essential, particularly as little is known about longitudinal effects of interactions with social robots. We ran a longitudinal user study with older adults in two retirement villages, where people interacted with a robot in a didactic setting for eight sessions over a period of four weeks. The robot would show either non-emotive or emotive behavior during these interactions in order to investigate emotional deception. Questionnaires were given to investigate participants’ acceptance of the robot, perception of the social interactions with the robot and attachment to the robot. Results show that the robot’s behavior did not seem to influence participants’ acceptance of the robot, perception of the interaction or attachment to the robot. Time did not appear to influence participants’ level of attachment to the robot, which ranged from low to medium. The perceived ease of using the robot significantly increased over time. These findings indicate that a robot showing emotions (and perhaps resulting in users being deceived) in a didactic setting may not by default negatively influence participants’ acceptance and perception of the robot, and that older adults may not become distressed if the robot would break or be taken away from them, as attachment to the robot in this didactic setting was not high. However, more research is required as there may be other factors influencing these ethical concerns, and support through other measurements than questionnaires are required to be able to draw conclusions regarding these concerns

EM-COGLOAD: An investigation into age and cognitive load detection using eye tracking and deep learning

Alzheimer's Disease is the most prevalent neurodegenerative disease, and is a leading cause of disability among the elderly. Eye movement behaviour demonstrates potential as a non-invasive biomarker for Alzheimer's Disease, with changes detectable at an early stage after initial onset. This paper introduces a new publicly available dataset: EM-COGLOAD. A dual-task paradigm was used to create effects of declined cognitive performance in 75 healthy adults as they carried out visual tracking tasks. Their eye movement was recorded, and time series classification of the extracted eye movement traces was explored using a range of deep learning techniques. The results of this showed that convolutional neural networks were able to achieve an accuracy of 87.5% when distinguishing between eye movement under low and high cognitive load, and 76% when distinguishing between the oldest and youngest age groups

Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data

High sensitivity methods such as next generation sequencing and polymerase chain reaction (PCR) are adversely impacted by organismal and DNA contaminants. Current methods for detecting contaminants in microbial materials (genomic DNA and cultures) are not sensitive enough and require either a known or culturable contaminant. Whole genome sequencing (WGS) is a promising approach for detecting contaminants due to its sensitivity and lack of need for a priori assumptions about the contaminant. Prior to applying WGS, we must first understand its limitations for detecting contaminants and potential for false positives. Herein we demonstrate and characterize a WGS-based approach to detect organismal contaminants using an existing metagenomic taxonomic classification algorithm. Simulated WGS datasets from ten genera as individuals and binary mixtures of eight organisms at varying ratios were analyzed to evaluate the role of contaminant concentration and taxonomy on detection. For the individual genomes the false positive contaminants reported depended on the genus, with Staphylococcus, Escherichia, and Shigella having the highest proportion of false positives. For nearly all binary mixtures the contaminant was detected in the in-silico datasets at the equivalent of 1 in 1,000 cells, though F. tularensis was not detected in any of the simulated contaminant mixtures and Y. pestis was only detected at the equivalent of one in 10 cells. Once a WGS method for detecting contaminants is characterized, it can be applied to evaluate microbial material purity, in efforts to ensure that contaminants are characterized in microbial materials used to validate pathogen detection assays, generate genome assemblies for database submission, and benchmark sequencing methods

Neurocognitive function following out-of-hospital cardiac arrest: A systematic review

Objectives: The primary aim of this review was to investigate neurocognitive outcomes following out-of-hospital cardiac arrest (OHCA). Specifically, the focus was on identifying the different neurocognitive domains that are assessed, the measures used, and the level of, and criteria for, impairment. Design and review methods: A systematic review of the literature from 2006 to 2021 was completed using Medline, Cinahl and Psychinfo. Criteria for inclusion were studies with participants over the age of 18, OHCA and at least one neurocognitive function measure. Qualitative and case studies were excluded. Reviewers assessed criteria and risk of bias using a modified version of Downs and Black. Results: Forty-three studies were identified. Most studies had a low risk of bias (n = 31) or moderate risk of bias (n = 11) and one had a high risk; however, only six reported effect sizes or power analyses. Multiple measures of neurocognitive outcomes were used (>50) and level of impairment criteria varied considerably. Memory impairments were frequently found and were also more likely to be impaired followed by executive function and processing speed. Discussion: This review highlights the heterogeneity of measures and approaches used to assess neurocognitive outcomes following OHCA as well as the need to improve risk of bias concerning generalizability. Improved understanding of the approaches used for assessment and the subsequent findings will facilitate a standardized evaluation of neurocognitive outcomes following OHCA