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Record W4285154674 · doi:10.1109/tcsii.2022.3179680

Voice Activated IoT Devices for Healthcare: Design Challenges and Emerging Applications

2022· article· en· W4285154674 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2022
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsMcMaster UniversityUniversity of Guelph
Fundersnot available
KeywordsVoice command deviceInternet of ThingsComputer scienceTelehealthHealth careTelemedicineMultimediaTelecommunicationsHuman–computer interactionComputer securitySpeech recognition

Abstract

fetched live from OpenAlex

The recent pandemic forced substantial changes in our lives, including the way we interact with physical objects. For example, voice-activated systems that enable users to communicate with them through speech commands are becoming more pervasive. At the same time, recent technology developments delivered voice capability to Internet of Things (IoT) devices with low-power audio transducers. Voice-activated IoT devices have the potential to engage patients and caregivers in new and cost-efficient ways, from telehealth and digital health, to portable diagnostics and remotely delivered care. In this brief, we review voice activated IoT devices, discuss their trends, and identify unique challenges when these devices are used in the healthcare sector. Furthermore, we discuss some future application scenarios and their characteristics.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.057
GPT teacher head0.271
Teacher spread0.213 · 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