Supported Devices

This guide documents devices that have been tested with ParaDigMa and provides device-specific guidance for data loading and usage.

ParaDigMa is designed to work with wrist sensor data from any device, as long as the data meets the Sensor Requirements. However, validation has only been performed for specific devices listed below.

Scientifically Validated Devices

These devices have undergone scientific validation with ParaDigMa pipelines.

Verily Study Watch

A research-grade smartwatch developed by Verily Life Sciences for clinical research.

Specifications

  • Sensors: 3-axis accelerometer, 3-axis gyroscope, photoplethysmography (PPG)

  • Sampling Rates: 100 Hz (IMU), 30 Hz (PPG)

  • Validated Pipelines: Gait, Tremor, Pulse Rate

Gait-up Physilog 4

A research-grade IMU worn as a wrist-mounted sensor band.

Specifications

  • Sensors: 3-axis accelerometer, 3-axis gyroscope, 3-axis magnetometer

  • Sampling Rate: 200 Hz

  • Validated Pipelines: Gait, Tremor

Empirically Validated Devices

These devices have been tested with ParaDigMa and show promising results, but have not undergone full scientific validation.

Axivity AX6

A compact wrist-worn IMU designed for long-term monitoring.

Specifications

  • Sensors: 3-axis accelerometer, 3-axis gyroscope

  • Sampling Rate: Configurable (typically 100 Hz)

  • Data Format: CWA (native)

  • Validated Pipelines: Gait, Tremor

See the Device-Specific Data Loading Tutorial for CWA-specific loading examples.

Empatica EmbracePlus

A wrist-worn research device with accelerometer sensors.

Specifications

  • Sensors: 3-axis accelerometer

  • Sampling Rate: 64 Hz (accelerometer)

  • Data Format: AVRO (native)

  • Validated Pipelines: None (data preparation only)

See the Device-Specific Data Loading Tutorial for AVRO-specific loading examples.

File Format Support

ParaDigMa works on in-memory Pandas DataFrames, but it also provides support for automatic data loading from multiple formats:

Format

Extension

Device Examples

Notes

TSDF

.meta + .bin

Verily Study Watch

Standard research format

Parquet

.parquet

All

Efficient storage, fast loading

CSV

.csv

All

Universal but slower

Pickle

.pkl, .pickle

All

Python-specific

CWA

.cwa

Axivity AX3/AX6

Native Axivity format

AVRO

.avro

Empatica

Native Empatica format

See Data Input Formats for detailed loading examples.

Using Unsupported Devices

If your device is not listed, you can still use ParaDigMa:

  1. Convert your device’s data to pandas DataFrame or a supported file format (Parquet recommended).

  2. Ensure your data meets the Sensor Requirements.

  3. Follow the Data Preparation Tutorial to format your DataFrame, or set the skip_preparation parameter of run_paradigma to False and provide the required set of parameters to automate data preparation.

  4. Test with Small Dataset.

  5. Check for reasonable output values and inspect intermediate results with save_intermediate=['quantification']

Testing with Example Data

ParaDigMa includes example data in example_data/:

example_data/
├── axivity/          # Axivity AX6 sample
├── empatica/         # Empatica EmbracePlus sample
├── gait_up_physilog/ # Physilog 4 sample
└── verily/           # Verily Study Watch sample

Use these to test your installation:

from paradigma.orchestrator import run_paradigma
from pathlib import Path

# Get example data path
example_dir = Path('path/to/paradigma/example_data/verily/imu')

# Run on example data
results = run_paradigma(
    data_path=example_dir,
    pipelines=['gait', 'tremor'],
    watch_side='left',
    file_pattern='*.json'
)

Hardware Considerations

Wrist Placement

All development and validation was performed with the sensor on the wrist (not upper arm or other locations):

  • Fitted snugly to minimize motion artifacts

  • Worn on either left or right wrist

  • Specify wrist side with watch_side parameter

Device Orientation

ParaDigMa expects a standardized coordinate system. See Coordinate System Guide for details.

If your device uses a different orientation, use the device_orientation parameter:

results = run_paradigma(
    dfs=df,
    pipelines=['gait'],
    watch_side='left',
    device_orientation=['z', '-x', 'y']  # Example custom orientation
)

Contributing Device Support

We welcome community contributions! If you have experience with a device not listed here:

  1. Test ParaDigMa on your device

  2. Document loading procedures and any device-specific considerations

  3. Share validation results

  4. Open a GitHub Discussion or contact paradigma@radboudumc.nl

Your contribution helps expand ParaDigMa’s device ecosystem!

See Also