ArticleSensors (Basel, Switzerland)2019
PHAROS 2.0-A PHysical Assistant RObot System Improved.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Mapping Digital Nudges and Recommender Systems for Obesity Prevention: Scoping Review.Interactive journal of medical research · 2026Review
- Robots for Elderly Care: Review, Multi-Criteria Optimization Model and Qualitative Case Study.Healthcare (Basel, Switzerland) · 2023Article
- Research status of elderly-care robots and safe human-robot interaction methods.Frontiers in neuroscience · 2023Review
- Combining Supervised and Unsupervised Learning Algorithms for Human Activity Recognition.Sensors (Basel, Switzerland) · 2021Article
- Robotic devices for paediatric rehabilitation: a review of design features.Biomedical engineering online · 2021Review
- A Low-Cost Assistive Robot for Children with Neurodevelopmental Disorders to Aid in Daily Living Activities.International journal of environmental research and public health · 2021Article
- Value Sensitive Design to Achieve the UN SDGs with AI: A Case of Elderly Care Robots.Minds and machines · 2021Article
- Field Testing of Ro-Tri, a Robot-Mediated Triadic Interaction for Older Adults.International journal of social robotics · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
There are great physical and cognitive benefits for older adults who are engaged in active aging, a process that should involve daily exercise. In our previous work on the PHysical Assistant RObot System (PHAROS), we developed a system that proposed and monitored physical activities. The system used a social robot to analyse, by means of computer vision, the exercise a person was doing. Then, a recommender system analysed the exercise performed and indicated what exercise to perform next. However, the system needed certain improvements. On the one hand, the vision system captured the movement of the person and indicated whether the exercise had been done correctly or not. On the other hand, the recommender system was based purely on a ranking system that did not take into account temporal evolution and preferences. In this work, we propose an evolution of PHAROS, PHAROS 2.0, incorporating improvements in both of the previously mentioned aspects. In the motion capture aspect, we are now able to indicate the degree of completeness of each exercise, identifying the part that has not been done correctly, and a real-time performance correction. In this way, the recommender system receives a greater amount of information and so can more accurately indicate the exercise to be performed. In terms of the recommender system, an algorithm was developed to weigh the performance, temporal evolution and preferences, providing a more accurate recommendation, as well as expanding the recommendation to a batch of exercises, instead of just one.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.