Publication2021
RestHAR: Residual Feature Learning Transformer for Human Activity Recognition from Multi-sensor Data
The record
- Type
- Publication
- Date
- 1 January 2021
- Level
- National
- Journal
- 2021 8th International Conference on Soft Computing and Machine Intelligence Iscmi 2021
- Pages
- 181-185
- Published
- 1 January 2021
- DOI
- 10.1109/iscmi53840.2021.9654816
- Scopus ID
- 85124381113
- Authors
- 1
- Open access
- No
- Citations
- 5
- 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 Ensure healthy lives and promote well-being for all at all ages
Deep learning transformer model for real-time human activity recognition from smartphone and wearable sensor data for health monitoring and elderly care.
- 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
Viksit Bharat @2047
vb-3.2 Digital technology and artificial intelligence
Digital technology and AI application for health monitoring and well-being of vulnerable populations.
- 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
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Provenance
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “RestHAR: Residual Feature Learning Transformer for Human Activity Recognition from Multi-sensor Data”. Kortex.