Signaligner Pro

Signaligner Pro is an interactive tool to visualize, annotate, and explore multi-day raw accelerometer data. The tool is designed for researchers working on activity recognition, health epidemiology, exercise science, and sleep behavior related research. Access to repo available on request.

View the Project on GitHub crowdgames/signaligner


Signaligner Pro

What is Signaligner Pro

Signaligner-Pro is an interactive tool for algorithm-assisted exploration and annotation of raw accelerometer data. The tool can be used by researchers using raw accelerometer data to support research in activity recognition/machine learning, exercise science, and sleep quality research among others.

Download Signaligner Pro for Mac or PC


Windows (v2.1-beta)

In current release

  1. UI improvements and bugfixes
  2. Improved support for missing sensor data
  3. Improved error handling and precision for imported labels
  4. Auto-generation of color schemes for imported label files


Starting up

  1. Select the appropriate operating system and download the package
  2. Unzip the package
  3. If you are using Windows OS, click on the Windows Batch file to run the software
  4. if you are using Mac OS,
  5. This starts Signaligner Pro’s launcher interface

In-built test data

  1. The tool comes packaged with test data to run
  2. On launcher, click on “Open Test Dataset”
  3. It will launch the interface in your default browser
  4. The test data is a simulated sinusoidal data along the X, Y, Z access

Example dataset

An example dataset is available here to test the tool. For convinience, we have included only two hours worth of raw data collected at 100Hz. Please get in touch if you want to try the tool with a larger dataset (7-days or more).

Import your own data

  1. The tool supports data from research-grade Actigraph (GT3X+ or link) as well as with raw data collected from commercial smartwatches and fitness trackers.

  2. The data should be in the following format (standard export format for Actigraph devices).

------------ Data File Created By ActiGraph GT3X+ ActiLife v6.13.1 Firmware v1.3.0 date format M/d/yyyy at 100 Hz  Filter Normal -----------,,
Serial Number: XXXXXXXXX,,
Start Time 22:00:00,,
Start Date 1/26/2017,,
Epoch Period (hh:mm:ss) 00:00:00,,
Download Time 17:31:48,,
Download Date 1/01/1901,,
Current Memory Address: 0,,
Current Battery Voltage: 3.89     Mode = 12,,
Accelerometer X,Accelerometer Y,Accelerometer Z

The first ten lines contain header information. Your imported dataset must have at least the Start Time, Start Date, and Sampling Rate (e.g., 100 Hz) information to process and prepare the dataset for visualization and annotation. Pleae get in touch for questions related to converting your sensor data format into Signaligner Pro format.

  1. To import your own dataset, click on “Import Single DataSet”.
  2. If you have data from multiple participants, please select “Import Multiple Datasets”.
  3. You will also be asked to confirm if your participant dataset has data from multiple sensors. For instance, you may have dataset where each participant wore sensors on the wrist and ankle.
  4. If you have data from multiple sensors, please make sure that you place files from different sensors (but for the same participant) in the same sub-folder of the participant.
  5. Once imported, Signaligner Pro will process your data for visualization. This may take several minutes, depending on the size of the dataset.

Labeling data with algorithms

  1. Signaligner Pro is pre-packaged with three algorithms
  2. MUSS: Classifies data into 7 physical activities namely (to be updated). More details can be found here (TBA)
  3. SWAN: Classifies data into sleep, wear, and non-wear states. More details can be found here (TBA)
  4. QC: Checks if there are segments of bad data in the dataset that you may want to ignore. More details can be found here (TBA)
  5. For your imported dataset, select an algorithm of your choice to pre-label the data with. This process may take several minutes

Configuring labels

  1. The tool allows you to configure label colors and text
  2. Please go to /users/Documents/SignalignerData/Datasets/(Dataset folder name)/config.json, as shown in the example below.
    "title": "ADITYA_2017_06_02_RAW",
    "tile_size": 1024,
    "tile_subsample": 4,
    "zoom_max": 7,
    "length": 47485200,
    "start_time_ms": 1495823940000,
    "sample_rate": 80,
    "start_day_idx": 1,
    "range_min": -8,
    "range_max": 8,
    "range_unit": "g",
    "sensors": [
        { "sname": "ADITYA_2017_06_02_RAW", "color": [ 1.00, 1.00, 1.00 ], "channels": [ "X", "Y", "Z" ] }
    "channels": [
        { "cname": "X", "color": [ 0.80, 0.20, 0.20 ] },
        { "cname": "Y", "color": [ 0.20, 0.80, 0.20 ] },
        { "cname": "Z", "color": [ 0.20, 0.20, 0.80 ] }
    "labels": [
        { "lname": "Wear", "color": [ 0.00, 0.90, 0.30 ] },
        { "lname": "Nonwear", "color": [ 0.00, 0.50, 0.50 ] },
        { "lname": "Sleep", "color": [ 0.00, 0.00, 0.80 ] },
        { "lname": "Walking", "color": [ 0.20, 0.00, 0.00 ] },
        { "lname": "Running", "color": [ 0.70, 0.00, 0.00 ] },
        { "lname": "Standing", "color": [ 0.50, 0.50, 0.00 ] },
        { "lname": "Biking", "color": [ 0.80, 0.50, 0.60 ] },
        { "lname": "Sitting", "color": [ 0.20, 0.00, 0.50 ] },
        { "lname": "Brisk Walking", "color": [ 0.65, 0.30, 0.00 ] }

This configuration file can be edited to change the g-range, measurement unit, label colors, as well as sensor channels (e.g., when using a diaxial accelerometer instead of a triaxial accelerometer).


For questions related to repo access, feature contribution, data processing scripts, sample datasets, and interface code, please contact: Aditya Ponnada and Prof. Seth Cooper

For questions related to MUSS and/or QC algorithm, please contact: Qu Tang

For questions related to SWAN and/or QC algorithm, please contact: Binod Thapa-Chhetry

For collaboration-related questions, please contact: Prof. Stephen Intille, Prof. Seth Cooper, or Prof. Dinesh John


The software development was supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health (NIH) under award number UH2EB024407. The work was also supported by NU-TECH AWS credits award from Northeastern University, Boston, MA. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

This is a joint effort between Northeastern University’s mHealth Research Group, Crowdgames Lab, and Exercise Physiology Lab. We are also sincerely thankful to our teammates from for their support in improving this software as we continue to work with them.