Aug 21, 2021
“Humanoid Robot” Healthcare Field, August 2021 — summary from PMC, Europe PMC and PubMed
PMC — summary generated by Brevi Assistant
Social Assistive Robotics is progressively being made use of in treatment settings to give psychosocial support and interventions for the senior with cognitive impairments. Reports show that the elderly tend to shed passions in these social robots after the initial exhilaration as the uniqueness wears and the tedious knowledge becomes all too acquainted. We analysed the performance of human — humanoid interactions between our robot and 14 elderly over 29 sessions. We detected emotions connected with cognitively impaired elderly throughout these communications. In aspect of the all-natural language processing area, previous studies have generally examined sound signals and given relevant responses. The proposed chatbot model incorporates photo info with audio signals and gives appropriate responses to the individual. A VGG16 model is also selected for a feature extractor for the image details. The experimental outcomes show that the integrative innovation of noise and photo information, which are obtained by the image sensing unit and sound sensor in a companion robot, is valuable for the chatbot design suggested in this study. History: Mild cognitive problems is a phase coming before dementia, and early intervention is vital. This research checked out whether multi-domain cognitive training programs, specifically robot-assisted training, performed 12 times, two times a week for 6 weeks can boost cognitive function and clinical depression decline in community-dwelling older adults with mild cognitive disability. The cognitive training team was randomly divided right into 2 groups, 45 that received traditional cognitive training and 45 who received robot-assisted cognitive training. Conclusions: A 6-week robot-assisted, multi-domain cognitive training program can enhance the effectiveness of global cognitive function and depression throughout cognitive tasks in older adults with MCI, which is connected with renovations in memory and executive function.
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Europe PMC — summary generated by Brevi Assistant
Humans interpret and forecast others’ habits by referring them beliefs or intentions, or to put it simply, by embracing the willful position. Since artificial agents are progressively inhabiting our everyday environments, the concern arises whether human beings would use the human-model to comprehend the actions of these new social agents. Results showed that indeed human beings are a lot more prone to embrace the deliberate stance after having interacted with a more socially available and human-like robot, while no inflection of the adoption of the intentional position arised towards a mechanistic robot. Social Assistive Robotics is increasingly being utilized in care setups to provide psychosocial assistance and interventions for the elderly with cognitive problems. This paper provides our study facilitating conversations between a social humanoid robot, Nadine, and cognitively damaged elderly at a nursing home. We found feelings connected with cognitively impaired elderly throughout these communications. In aspect of the natural language processing area, previous researches have generally evaluated audio signals and offered relevant responses. The suggested chatbot design includes photo info with sound signals and gives ideal responses to the customer. The experimental results demonstrate that the integrative modern technology of audio and image info, which are gotten by the picture sensor and audio sensor in a companion robot, is valuable for the chatbot design proposed in this research. Background Mild cognitive problems is a phase preceding mental deterioration, and very early intervention is critical. This research study examined whether multi-domain cognitive training programs, particularly robot-assisted training, conducted 12 times, twice a week for 6 weeks can improve cognitive function and clinical depression decline in community-dwelling older grownups with moderate cognitive problems. The cognitive training group was randomly separated into two teams, 45 that got conventional cognitive training and 45 who got robot-assisted cognitive training.
Please keep in mind that the text is machine-generated by the Brevi Technologies’ Natural language Generation model, and we do not bear any responsibility. The text above has not been edited and/or modified in any way.
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PubMed — summary generated by Brevi Assistant
Social Assistive Robotics is significantly being made use of in care setups to offer psychosocial support and interventions for the senior with cognitive disabilities. This paper offers our research study assisting in conversations between a social humanoid robot, Nadine, and cognitively impaired senior at an assisted living facility. We evaluated the effectiveness of human-humanoid interactions between our robot and 14 elderly over 29 sessions. Using humanoid robotics within a restorative function, that is, assisting individuals with social problems, is an emerging field, but it continues to be unexplored in regards to concentration training. To effortlessly incorporate humanoid robots into concentration games, an examination right into the effects of human robot interactive proxemics on concentration-training games is specifically vital. Speculative outcomes revealed that a 2 m range and left-front orientation for a human and a robot are ideal for human robot interactive concentration training. In aspect of the all-natural language processing area, previous researches have typically examined noise signals and provided relevant responses. The suggested chatbot design incorporates picture info with sound signals and gives proper responses to the customer. The experimental outcomes show that the integrative technology of sound and image info, which are acquired by the picture sensor and audio sensor in a companion robot, is helpful for the chatbot design recommended in this research study.
Please keep in mind that the text is machine-generated by the Brevi Technologies’ Natural language Generation model, and we do not bear any responsibility. The text above has not been edited and/or modified in any way.
Source texts:
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