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Model

GRIP activity-intensity models (wrist ACC, chronic pain)

Dataset
CAPTURE-24
Form factor
Wrist
IMUOpen codeWeights: open
Cite
Figure or first page from the GRIP activity-intensity models (wrist ACC, chronic pain) source (opens the source in a new tab)
Annet Doomen et al., "An open-source deep learning model for classifying real-world activity intensities in persons with chronic pain using a wrist-worn accelerometer", doi:10.3389/fdgth.2026.1783507, CC BY 4.0
UnconfirmedCatalog paper date: Not reportedAdded to atlas: Oct 17, 2026
CitationsView DOI record (opens in a new tab)

Activity counts measure attention, not model quality. Missing counts are unknown. Publication status follows the linked source; a listed venue alone does not confirm peer review.

Keras deep-learning models for 3-class activity intensity from wrist accelerometry at 12.5 Hz, developed on 30 healthy and 30 chronic-pain participants plus CAPTURE-24 (Doomen, Felius et al., Frontiers in Digital Health 2026). Of four DL candidates (CNN, LSTM, CNN-LSTM, larger CNN) the authors chose a CNN. The repo ships weights for all candidates plus an HMM.

Form factor
Wrist
Signals
IMU
License

Zenodo snapshot CC BY 4.0; GitHub repo has no LICENSE file

At a glance
Type
Task model
Modalities
IMU
Form factor
Wrist
Parameters
Not reported
Architecture
Not reported
Venue
Not reported
Year
Not reported
arXiv
Not reported
DOI
Not reported
Code
Open
Weights
Open
Licence
Zenodo snapshot CC BY 4.0; GitHub repo has no LICENSE file
Caveats
Not reported
Datasets
Papers
Devices
Added
Oct 17, 2026
Updated
Oct 17, 2026

Key results

0.661
Macro-F1 ↑ · Human activity recognition (IMU)
CAPTURE-24
#1 / 1

How it works

How GRIP activity-intensity models (wrist ACC, chronic pain) works: Input signals → Model → Downstream tasksInput signals: IMU (Accelerometer, 3-axis). Dataset: CAPTURE-24. Model: GRIP activity-intensity models (wrist ACC, chronic pain) (Convolutional network). Downstream tasks: Activity recognition.Input signalsIMU: Accelerometer, 3-axisxyzIMUAccelerometer, 3-axisDataset: CAPTURE-24DatasetCAPTURE-24ModelGRIP activity-intensity…Convolutional networkDownstream tasksActivity recognitionActivity recognition

Datasets linked in the hub; pretraining vs evaluation roles follow the paper.

Inputs
IMU
Approach
HAR · activity intensity · wrist accelerometer · chronic pain · keras · open weights

Devices· 2

Axivity AX3 photo
Axivity AX3
Axivity · Wrist
IMU
Photo: Open Movement, Newcastle University / project docs, CC BY 3.0
OMG Life Autographer photo
OMG Life Autographer
OMG Life · Other
camera
Photo: OMG Plc / Wikimedia Commons, CC BY 3.0

Full photo credits, sources and licences are on each device page.

Leaderboard rows· 1

All leaderboards
TaskDatasetMetricValueRank
Human activity recognition (IMU)CAPTURE-24Macro-F1 ↑0.661#1 / 1

Datasets· 1

Papers· 1

Cite

APA

GRIP activity-intensity models (wrist ACC, chronic pain). (n.d.).

BibTeX

@misc{gripactivityintegrip,
  title         = {{GRIP activity-intensity models (wrist ACC, chronic pain)}}
}

Built from the hub record. Check it against the publisher before submitting.

History

  1. Added to the ChatPPG research hub

Connections

Open in the graph explorer
Dataset: CAPTURE-24 (uses dataset)CAPTURE-24CAPTURE-24Signal: IMU (uses signal)IMUIMUPaper: An open-source deep learning model for classifying real-world activity intensities in persons with chronic pain using a wrist-worn accelerometer (paper and model)An open-source deep…An open-so…GRIP activity-intensity model…GRIP activity-int…
Datasets 1
CAPTURE-24
Signals 1
IMU

Same tasks, data and authors

Papers

Models

Datasets