Database Open Access
Complex Upper-Limb Movements
Published: Aug. 30, 2018. Version: 1.0.0
Jose Garcia Vivas Miranda, Jean-François Daneault, Gloria Vergara-Diaz, Ângelo Frederico Souza de Oliveira e Torres, Ana Paula Quixadá, Marcus de Lemos Fonseca, João Paulo Bomfim Cruz Vieira, Vitor Sotero dos Santos, Thiago Cruz da Figueiredo, Elen Beatriz Pinto, Norberto Peña & Paolo Bonato (2018). Complex Upper-Limb Movements Are Generated by Combining Motor Primitives that Scale with the Movement Size. Scientific Reports. 8(1). DOI:10.1038/s41598-018-29470-y.
Please include the standard citation for PhysioNet:
(show more options)
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
The Complex Upper-Limb Movements database contains hand trajectory data collected from ten subjects as they performed various upper-limb motor tasks. The data was used in the above-referenced manuscript to identify the motor primitives contributing to the observed motor patterns.
The hand trajectory of motion during the performance of one-dimensional point-to-point movements has been shown to be marked by motor primitives with a bell-shaped velocity profile. Researchers have investigated if motor primitives with the same shape mark also complex upper-limb movements. They have done so by analyzing the magnitude of the hand trajectory velocity vector. This approach has failed to identify motor primitives with a bell-shaped velocity profile as the basic elements underlying the generation of complex upper-limb movements. In this study, we examined upper-limb movements by analyzing instead the movement components defined according to a Cartesian coordinate system with axes oriented in the medio-lateral, antero-posterior, and vertical directions.
Ten healthy subjects (7 males; 26.4±4.52 years of age; 9 right-handed) with no known neurological or orthopedic conditions affecting the control of motion were recruited to participate in the study. Written informed consent was obtained from all subjects. The protocol was approved by the Institutional Review Board of Spaulding Rehabilitation Hospital. The dataset was collected in the Motion Analysis Lab (http://srh-mal.net/) at Spaulding Rehabilitation Hospital.
To explore the validity of the theoretical model proposed in the above-referenced manuscript, we asked a group of ten healthy subjects to perform a battery of upper-limb motor tasks. Data collected using a camera-based motion capture system (VICON, Oxford UK) was preprocessed using the system’s software to reconstruct the three-dimensional position of a reflective marker representing the hand trajectory of motion.
The csv data files contain four columns. The data in the first column is the time axis in seconds. The data samples in the other three columns are the x-, y-, and z-coordinates of the reflective marker utilized in each experiment to represent the hand trajectory of movement. The data is reported in meters. Different files contain data pertaining to different subjects for different motor tasks. Different filenames are used for different tasks as follows:
- BostonCA --> Writing the word Boston in Capital letters
- BostonCU --> Writing the word Boston in cursive letters
- HarvardCA --> Writing the word Harvard in Capital letters
- HarvardCU --> Writing the word Harvard in cursive letters
- Can --> 3D arm movements to reach for and transport a can of soda positioned on a table
- Circle --> Drawing circles
- Ellipse --> Drawing ellipses
- Flower --> Drawing a pure frequency curve with v=4/3
- Spiral --> Drawing a pure frequency curve with v=0
- SuperMegaCloud --> Drawing a pure frequency curve with v=4/5
- Triangle --> Drawing a pure frequency curve with v=3
- Planned --> series of 2D planned (i.e. with target) movements of the arm
- Unplanned --> series of 2D unplanned (i.e. without target) movements of the arm
- Randomness --> 3D random movements
More information about the experimental protocol can be obtained from José Miranda (email@example.com) or Paolo Bonato (firstname.lastname@example.org).
Anyone can access the files, as long as they conform to the terms of the specified license.
License (for files):
Open Data Commons Attribution License v1.0
Total uncompressed size: 54.7 MB.
Access the files
- Download the ZIP file (54.8 MB)
- Access the files using the Google Cloud Storage Browser here. Login with a Google account is required.
- Access the data using the Google Cloud command line tools (please refer to the gsutil documentation for guidance):
gsutil -m -u YOUR_PROJECT_ID cp -r gs://culm-1.0.0.physionet.org DESTINATION
- Download the files using your terminal:
wget -r -N -c -np https://physionet.org/files/culm/1.0.0/