Evidence map›Paper›PMID 31635278›Full record

ArticleSensors (Basel, Switzerland)2019

PHAROS 2.0-A PHysical Assistant RObot System Improved.

Ester Martinez-Martin, Angelo Costa, Miguel Cazorla

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Review
  6. A Low-Cost Assistive Robot for Children with Neurodevelopmental Disorders to Aid in Daily Living Activities.International journal of environmental research and public health · 2021
    Article
  7. Article
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ester Martinez-MartinRoViT, University of Alicante, 03690 San Vicente del Raspeig (Alicante), Spain. ester@ua.es.ORCID 0000-0003-4495-6912
Angelo CostaALGORITMI Center, University of Minho, 4704-553 Braga, Portugal. acosta@di.uminho.pt.ORCID 0000-0002-6170-4912
Miguel CazorlaRoViT, University of Alicante, 03690 San Vicente del Raspeig (Alicante), Spain. miguel.cazorla@ua.es.ORCID 0000-0001-6805-3633

Funding

Fundação para a Ciência e a Tecnologia SFRH/BPD/102696/2014Ministerio de Economía, Industria y Competitividad, Gobierno de España TIN2016-76515-R
6 · The paper itself

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.

Indexed as

active ageingassistive roboticscognitive assistantdecision support systemdeep learning

Identifiers

PMID31635278
PMCPMC6832539

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.