Publication2025
Deep Learning-Driven Human Activity Recognition for Remote Health Applications in Internet of Things-Enabled Smart Cities
The record
- Type
- Publication
- Date
- 1 January 2025
- Level
- National
- Journal
- Nanosensors as Robust Non Invasive Diagnostic Tools for Remote Health Monitoring
- Pages
- 41-76
- Published
- 1 January 2025
- DOI
- 10.1201/9781003602729-3
- Scopus ID
- 105004637094
- Authors
- 4
- Open access
- No
- Citations
- 1
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
3.8 Achieve universal health coverage, including financial risk protection and access to essential medicines
Deep learning for human activity recognition in IoT-enabled smart cities enables remote patient monitoring and long-term healthcare management
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
11 Make cities and human settlements inclusive, safe, resilient and sustainable
IoT-based health monitoring in smart cities supports resilient and sustainable urban health infrastructure
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-1.2 Health and well-being
Remote health monitoring through wearable IoT devices directly supports health surveillance and preventive care outcomes
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
Evidence
Kortex hosts no documents. Evidence is an outbound link to the authority that holds it.
No outbound evidence link is recorded for this activity. The record below states which source it came from and when it was last read.
Provenance
Which source system this record came from, how many source rows were resolved into it, and when each was last read.
- Source rows resolved into this record
- 1
Each record carries the date Kortex last read it from its source system. That is a freshness date and not a verification: Kortex records what its sources say and when it read them, and it does not check those statements against any external authority. No claim on this site is described as verified, because none is.
Citing this record
This URL is stable. A record’s permalink is frozen when it is published and is not regenerated if its title is later corrected.
KIIT Deemed to be University. “Deep Learning-Driven Human Activity Recognition for Remote Health Applications in Internet of Things-Enabled Smart Cities”. Kortex.