Human activity recognition leaderboard
capture-24: macro-F1
5 reported results for human activity recognition on capture-24, ranked by macro-F1. Top reported: ActiNet (0.818). Every row cites its source publication.
This page ranks every result ChatPPG has recorded for human activity recognition on capture-24, measured by Macro-F1 on HAR activities (higher is better). Each number is copied from the publication cited in its row, and ranks apply only within this dataset and metric.
| Rank | Method & evaluation | Macro-F1 on HAR activities↑ Higher is better |
|---|---|---|
| 1 | ActiNet actinet-ssl-harnet-hmm-activity-intensity-sci-rep-2026 (ActiNet = harnet-ukb fine-tuned + HMM) Evaluation detailsSleep/sedentary/light/MVPA (Walmsley), 30-s @30 Hz wrist ACC, 151 pts, nested 5-fold group CV; κ 0.860; RF+HMM 0.764. | 0.818 |
| 2 | SSL ResNet, UK Biobank (Yuan) Self-supervised ResNet pre-trained on UK Biobank, all layers fine-tuned Evaluation detailsWrist accelerometer resampled to 30 Hz, 10 s windows; subject-wise 5-fold CV (leave-one-subject-out when fewer than 10 subjects); subject-wise macro F1 (Table 2). Capture-24 4-class labels. Reported as of: 2026-10-16 | 0.726 |
| 3 | ResNet from scratch (Yuan) ResNet-V2 trained from scratch Evaluation detailsWrist accelerometer resampled to 30 Hz, 10 s windows; subject-wise 5-fold CV (leave-one-subject-out when fewer than 10 subjects); subject-wise macro F1 (Table 2). Capture-24 4-class labels. Reported as of: 2026-10-16 | 0.708 |
| 4 | Random forest (Yuan) Random forest on hand-crafted features Evaluation detailsWrist accelerometer resampled to 30 Hz, 10 s windows; subject-wise 5-fold CV (leave-one-subject-out when fewer than 10 subjects); subject-wise macro F1 (Table 2). Capture-24 4-class labels. Reported as of: 2026-10-16 | 0.694 |
| 5 | An open-source deep learning model for classifyi doomen-grip-open-source-wrist-acc-activity-intensity-chronic-pain-fdgth-2026 (GRIP CNN, CC BY 4.0 weights) Evaluation detailsSedentary/light/MVPA (no sleep), 10-s windows, Capture-24 downsampled 100→12.5 Hz, trained on CP+HP lab + Capture-24 ("All on Capture"), repeated 10-fold subject-level CV; acc 67.2 %. | 0.661 |
Rows are ranked only within this task × dataset × metric. Protocols can differ; read each method's evaluation details before comparing. Latest reported row: 2026-10-16.
About the dataset
- Subjects
- 151
- Signals
- accel
- Licence
- CC BY 4.0
Sources on this board
Related leaderboards
Other metrics on capture-24
Human activity recognition on other datasets
- PAMAP2: macro-F1 (7)
- opportunity: macro-F1 (6)
- mhealth: F1 (4)
- mhealth: macro-F1 (4)
- PAMAP2: F1 (4)
- RealWorld HAR: macro-F1 (4)
- WISDM AR v1.1: accuracy (4)
- UCI Human Activity Recognition Using Smartphones (UCI-HAR): F1 (3)
- USC-HAD: F1 (3)
- DEO: balanced accuracy (1)
- har-6bench-avg: macro-F1 (1)
- HHAR: macro-F1 (1)
Data and submissions
Download every leaderboard as JSON or CSV. Missing a published result? Email hello@chatppg.com with the paper link and the exact table it comes from.
Cite this
Cite the page URL and the date you accessed it. Please also cite the original publications listed above.
APA
ChatPPG. (2026). capture-24 macro-F1 leaderboard: human activity recognition [Data set]. ChatPPG Research Hub. https://chatppg.com/research/leaderboards/har/capture-24-macro-f1-har
BibTeX
@misc{chatppg_har_capture_24_macro_f1_har,
author = {{ChatPPG}},
title = {{capture-24 macro-F1 leaderboard: human activity recognition}},
year = {2026},
month = oct,
howpublished = {\url{https://chatppg.com/research/leaderboards/har/capture-24-macro-f1-har}},
note = {ChatPPG Research Hub}
}CITATION.cff
cff-version: 1.2.0 message: "If you use this resource, please cite it as below." type: dataset title: "capture-24 macro-F1 leaderboard: human activity recognition" authors: - name: "ChatPPG" url: "https://chatppg.com/research/leaderboards/har/capture-24-macro-f1-har" date-released: "2026-10-16"