Paper Citation Record · LEDGER

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices

As of 17 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2507.07949.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.07949 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:31:33.836249Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T10:20:04.345577Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-08T10:20:04.421246Z

Reference resolution

54 of 54 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a6ed4059-0caf-485e-a1f8-ab21fd853b8f · outbound

This paper cites https://archive.ics.uci.edu/dataset/245/daphnet+freezing+of+gait.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices https://archive.ics.uci.edu/dataset/245/daphnet+freezing+of+gait

Reference 1

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Observation 0b95a0f6-620f-4ecd-a3af-5cff6fb6cd1a · outbound

This paper cites https://archive.ics.uci.edu/dataset/256/daily+and+sports+ activities.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices https://archive.ics.uci.edu/dataset/256/daily+and+sports+ activities

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:31:35.106824Z

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Observation acc37948-61e7-4b23-9131-aee3534dcd89 · outbound

This paper cites https://archive.ics.uci.edu/dataset/341/smartphone+based+ recognition+of+human+activities+and+postural+transitions.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices https://archive.ics.uci.edu/dataset/341/smartphone+based+ recognition+of+human+activities+and+postural+transitions

Reference 3

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Observation 454718a5-2d52-417d-895f-a103fbb16b1a · outbound

This paper cites https://archive.ics.uci.edu/dataset/319/mhealth+dataset.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices https://archive.ics.uci.edu/dataset/319/mhealth+dataset

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:31:35.063709Z

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Observation 3e13882d-fac1-45dc-a605-0d69235cdd8d · outbound

This paper cites https://www.kaggle.com/datasets/malekzadeh/ motionsense-dataset.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices https://www.kaggle.com/datasets/malekzadeh/ motionsense-dataset

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:31:35.029698Z

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Observation 5b6e506c-2f57-4644-8562-bb6517a09f4f · outbound

This paper cites https://archive.ics.uci.edu/dataset/226/opportunity+activity+ recognition.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices https://archive.ics.uci.edu/dataset/226/opportunity+activity+ recognition

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:31:35.007112Z

Source-reported events for the cited work

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Observation 8df14035-c85e-4bbf-9291-e39af31e6118 · outbound

This paper cites https://archive.ics.uci.edu/dataset/231/pamap2+physical+ activity+monitoring.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices https://archive.ics.uci.edu/dataset/231/pamap2+physical+ activity+monitoring

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:31:34.988032Z

Source-reported events for the cited work

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Observation 9776726e-c70b-47d6-980b-39c5ee116f4f · outbound

This paper cites https://zhaxidele.github.io/RecGym/.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices https://zhaxidele.github.io/RecGym/

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:31:34.966078Z

Source-reported events for the cited work

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Observation 429f3030-f70a-4c19-a363-491a74ed90ef · outbound

This paper cites https://www.uni-mannheim.de/dws/research/projects/activity- recognition/dataset/dataset-realworld/.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices https://www.uni-mannheim.de/dws/research/projects/activity- recognition/dataset/dataset-realworld/

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:31:34.936396Z

Source-reported events for the cited work

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Observation cdbd88af-7471-48de-85b0-bed4dc4e2975 · outbound

This paper cites https://www.utwente.nl/en/eemcs/ps/research/dataset/.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices https://www.utwente.nl/en/eemcs/ps/research/dataset/

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:31:34.909225Z

Source-reported events for the cited work

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Observation a78a6bd6-be32-42e4-a591-6d40a8b0e4b8 · outbound

This paper cites http://har-dataset.org/doku.php?id=wiki:dataset.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices http://har-dataset.org/doku.php?id=wiki:dataset

Reference 11

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Observation beafe836-0926-4694-8248-426c2cb8688b · outbound

This paper cites https://archive.ics.uci.edu/dataset/240/human+activity+ recognition+using+smartphones.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices https://archive.ics.uci.edu/dataset/240/human+activity+ recognition+using+smartphones

Reference 12

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Observation 21a610d1-e859-4898-ab2c-108cf1ab745a · outbound

This paper cites https://sipi.usc.edu/had/.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices https://sipi.usc.edu/had/

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:31:34.787201Z

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Observation bfbb7c45-e22d-4ce9-80f0-db3d95383855 · outbound

This paper cites https://archive.ics.uci.edu/dataset/507/wisdm+smartphone+ and+smartwatch+activity+and+biometrics+dataset.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices https://archive.ics.uci.edu/dataset/507/wisdm+smartphone+ and+smartwatch+activity+and+biometrics+dataset

Reference 14

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TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices Unresolved cited work

Reference 15

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Reference 16

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Reference 17

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Reference 18

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Reference 19

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Reference 20

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This paper cites Hybrid CNN-Dilated Self-attention Model Using Inertial and Body-Area Electrostatic Sensing for Gym Workout Recognition, Counting, and User Authentification.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices Hybrid CNN-Dilated Self-attention Model Using Inertial and Body-Area Electrostatic Sensing for Gym Workout Recognition, Counting, and User Authentification

Reference 21

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Reference 22

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Reference 23

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Reference 24

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Reference 25

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Observation 24c2b8f8-d372-4dec-a98d-810593d23d7c · outbound

This paper cites Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets

Reference 26

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Reference 27

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This paper cites Scaling laws in wearable human activity recognition.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices Scaling laws in wearable human activity recognition

Reference 28

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Reference 30

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This paper cites On-Device Training Empowered Transfer Learning For Human Activity Recognition.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices On-Device Training Empowered Transfer Learning For Human Activity Recognition

Reference 32

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TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices Assessing the Impact of Sampling Irregularity in Time Series Data: Human Activity Recognition As A Case Study

Reference 37

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Reference 38

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Reference 41

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Reference 43

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Reference 44

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TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices Unresolved cited work

Reference 45

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Reference 46

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Reference 47

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Reference 48

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Observation 040c5fa6-be72-409a-a46e-71d93daa198a · outbound

This paper cites an unresolved cited work.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices Unresolved cited work

Reference 49

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Observation d555d97d-e30a-4ad9-b91e-93acd9750768 · outbound

This paper cites an unresolved cited work.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:31:34.053321Z

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Observation 30a4c5f5-d83e-4558-a32e-b690e827770a · outbound

This paper cites an unresolved cited work.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:31:34.034699Z

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Observation 0e99bd62-e036-47f9-86d7-b3af7967b2a8 · outbound

This paper cites an unresolved cited work.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:31:34.015298Z

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Observation 681ebc06-cecc-4198-aeec-6472703f6c50 · outbound

This paper cites In Proceedings of the 26th ACM international conference on Multimedia.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices In Proceedings of the 26th ACM international conference on Multimedia

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:31:34.161939Z

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Observation 3d8642b4-5a09-4651-bbdc-08efd4b964fc · outbound

This paper cites Neural Computing and Applications 33 (2021), 13705–13722.

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices Neural Computing and Applications 33 (2021), 13705–13722

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:31:34.520768Z

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Pith citing papers

Observation f89fb65e-7b75-4cdb-ace6-d6f3918665d7 · inbound

Viveka: Context-Aware Sensing for Energy Efficiency in Smart Wearables cites this paper.

Viveka: Context-Aware Sensing for Energy Efficiency in Smart Wearables TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-08T10:20:04.427114Z

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