Malaria Cohort Heart Rate Time Series from Wearable Devices

Figshare hosts anonymized heart rate and resting heart rate time-series recordings for 300 participants in a malaria cohort study. Data was collected using Garmin Vivosmart 5 wearables and supports a 2026 npj Digital Medicine publication on GAN-based imputation for disease detection.

Use Cases

• Train GAN models for imputation of missing data in heart rate and resting heart rate time-series sequences.
• Analyze heart rate variability patterns within the malaria cohort to identify potential physiological markers.
• Benchmark wearable device signal quality and data loss characteristics from Garmin Vivosmart 5 recordings.
• Develop anomaly detection algorithms for heart rate signals in a disease-specific participant population.

Strengths

• Data from 300 participants provides a substantial cohort for analysis.
• Time-series recordings cover the full study duration for longitudinal assessment.
• Associated with a peer-reviewed 2026 publication in npj Digital Medicine.
• Collected using a specific, named device model (Garmin Vivosmart 5).

Limitations

• Data structure and specific column features are unknown, complicating direct analysis.
• The dataset is provided as separate RAR archives per participant, requiring extraction and potential merging.
• Focus on a single disease cohort (malaria) may limit generalizability to other conditions.

Provenance

Source

Jürgen Wallner, via figshare.

Collection Method

Collected using Garmin Vivosmart 5 wearable devices from participants in a malaria cohort study.

Time Range

Covers the full duration of the study (specific dates unknown).

Freshness

Last updated March 2026.

Geography

null