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:

  1. Triangle, pointing up
  2. Square
  3. Circle
  4. Infinity
  5. Triangle, pointing down
All gestures were done clockwise (for infinity the first right loop done clockwise).

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

Sensor placement



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