# Activity recognition dataset - BodyAttack Fitness dataset

# Description

This dataset contains 6 fitness activity classes, done mostly with the legs.
This dataset was collected by Kilian Förster to investigate the effect of sensor displacement on activity recognition performance [Förster09].

## Availability

This dataset can be freely used in publications provided the following paper is cited: [Förster09].

## Sensors

This dataset contains 6 activity classes, recorded 10 sensors placed on the right leg of the subject, at regular intervals.
Sample rate: 64Hz.

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## Activities

- 6 fitness activities
- 1 subject
- 5 runs
- About 30 seconds per activity class and run (i.e. total of 2.5mn/activity class, and total recording length of 15mn)

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## Files

baclass_20090317 contains the segemented dataset. Load into matlab with the load command.
Variables loaded: datasetall.
Format: acceleration = datasetall{sensor}{activity}{run}
sensor number (30 sensors: 10 3-axis sensors = 10x3 = 30 axis). With sensor=0...29, the sensor number is mod(s,3). The sensor number corresponds to the above figure.
Acceleration is calibrated in milli-g units (1000 = earth gravity vector).
activity is:

- 1: flick kicks
- 2: knee lifts
- 3: jumping jacks
- 4: superman jumps
- 5: high knee runs
- 6: feet back runs

run is the recording number.

### Caveats

Due to data acquisition issue, some data intervals were lost. The lost inverals were replaced by data of same length from the same sensor, same activity and same run (unless otherwise noted). This has no influence on a window-based activity recognition. It may however lead to discontinuities in the signal where a data interval is repeated.
Essentially, however, this dataset may be used as-is for many activity recognition problems.
The following sensors / activities / runs are concerned (indicated the samples that were replaced):

- Sensor 6, run 3, activity 4: samples 2800->end
- Sensor 6, run 3, activity 5: samples end-1000->end
- Sensor 6, run 3, activity 6: all samples (data copied from sensor 2)
- Sensor 7, run 4, activity 1: samples 1720->end
- Sensor 7, run 4, activity 2: samples end-620->end
- Sensor 9, run 3, activity 1: samples end-1000->end

## 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