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Paper

HARResNext: An efficient ResNext inspired network for human activity recognition with inertial sensors

by Independent / Community 002221b52d9c5a1737fbbabe267e327168e0d1dc

Free2AITools Nexus Index

65.4

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 76

P: Popularity 51

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Human activity recognition (HAR) based on wearable sensors has developed as a new study topic in the domains of artificial intelligence and pattern recognition. HAR has a wide range of applications, including sports activity detection, smart homes, and health assistance, to name a few. Mobile device sensors such as accelerometers, gyroscopes, and magnetometers can generate time-series data for HAR. Computer Vision (CV) methods were previously utilised for HAR, which has a number of drawbacks,...

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Paper Information Summary
Entity Passport
Registry ID	002221b52d9c5a1737fbbabe267e327168e0d1dc
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{002221b52d9c5a1737fbbabe267e327168e0d1dc,
  author = {Unknown},
  title = {HARResNext: An efficient ResNext inspired network for human activity recognition with inertial sensors Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/002221b52d9c5a1737fbbabe267e327168e0d1dc}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). HARResNext: An efficient ResNext inspired network for human activity recognition with inertial sensors [Paper]. Free2AITools. https://api.semanticscholar.org/002221b52d9c5a1737fbbabe267e327168e0d1dc

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Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 76

Popularity (P) 51

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for HARResNext: An efficient ResNext inspired network for human activity recognition with inertial sensors: Authority (A:76), Popularity (P:51), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"Human activity recognition (HAR) based on wearable sensors has developed as a new study topic in the domains of artificial intelligence and pattern recognition. HAR has a wide range of applications, including sports activity detection, smart homes, and health assistance, to name a few. Mobile device sensors such as accelerometers, gyroscopes, and magnetometers can generate time-series data for HAR. Computer Vision (CV) methods were previously utilised for HAR, which has a number of drawbacks,..."

❝ Cite Node

@article{Unknown2026HARResNext:,
  title={HARResNext: An efficient ResNext inspired network for human activity recognition with inertial sensors},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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📊 Research Signals

📈10CitationsSemantic Scholar

🏛️76AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

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🏷️ Research Topics

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📦Data Source: semantic_scholar

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source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
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downloads: 0
stars: null
forks: null
citations: 10

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