Activity
recognition dataset - HCI gestures dataset
Daniel Roggen, Wearable Computing Laboratory, ETH Zurich
droggen@gmail.com
Initial documentation: 10.03.2009
Last updated: 06.09.2010
Description
This dataset contains 5 classes, recorded with an 8 acceleration
sensors USB sensors
placed on the right lower and upper arm.
It was recorded by Kilian Förster to investigate the effect of
sensor displacement on activity recognition performance
[Förster09].
In addition motion-jacket was recorded (not included in this dataset -
available in original dataset - contact us for more informations).
Availability
This dataset can be freely used in publications provided the following
paper is cited: [Förster09].
Sensors
8 3D acceleration sensors are placed on the arm used to do the
gestures, at regular intervals.
Sample rate: 96Hz.
Activities
Gestures were performed vertically in 'freehand way' and also
against a styrofoam support to limit gesture variability. The gestures
are:
- Triangle, pointing up
- Square
- Circle
- Infinity
- Triangle, pointing down
All gestures were done clockwise (for infinity the first right loop
done clockwise).
- 1 Subject
- 10+ repetitions of freehand gesture
- 60+ repetitions of guided gesture
Files
Dataset is available in two versions: guided
and freehand.
The dataset in dataset_usb_hci_dtc.mat
contains two variables: dataset_usb_hci_freehand_dtc for freehand
movement in DTC format; dataset_usb_hci_guided_dtc for guided movement
in DTC format.
This format is: acceleration = dataset_xxx{sensor}{activity}{instance}
with sensor=0...23 are the sensor axis (8 3-axis sensors = 8x3 = 24
axis).
Sensor node number is mod(s,3). For the exact position please contact
us.
The original processed and corrected dataset is in usb_hci_freehand.mat and usb_hci_guided.mat. In this format,
the sensor are in the order: 3, 4, 18, 19, 20, 27, 29 31

References
[Förster09] K. Förster, D. Roggen, G. Tröster. Unsupervised Classifier Self-Calibration
through Repeated Context Occurences: Is there Robustness against Sensor
Displacement to Gain? In Proc.
13th {IEEE} Int. Symposium on Wearable Computers (ISWC 2009),
pages 77-84, 2009