MétaCan
Menu
Back to cohort
Record W3018662283 · doi:10.1109/jbhi.2020.2990529

Homecare Robotic Systems for Healthcare 4.0: Visions and Enabling Technologies

2020· review· en· W3018662283 on OpenAlexafffund
Geng Yang, Zhibo Pang, M. Jamal Deen, Mianxiong Dong, Yuan‐Ting Zhang, Nigel H. Lovell

Bibliographic record

VenueIEEE Journal of Biomedical and Health Informatics · 2020
Typereview
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsMcMaster University
FundersJapan Society for the Promotion of ScienceKDDI FoundationNatural Science Foundation of Zhejiang ProvinceMcMaster Institute for Research on Aging, McMaster UniversityRecruitment Program of Global ExpertsZhejiang UniversityAcademy of FinlandNational Natural Science Foundation of ChinaState Key Laboratory of Fluid Power and Mechatronic SystemsMcMaster University
KeywordsVisionComputer scienceHealth careHealthcare systemHuman–computer interaction

Abstract

fetched live from OpenAlex

Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.118
GPT teacher head0.384
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations206
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Journal of Biomedical and Health InformaticsSame topicIoT and Edge/Fog ComputingFrench-language works237,207