smartwatch_gestures

• Description:

The SmartWatch Gestures Dataset has been collected to evaluate several gesture recognition algorithms for interacting with mobile applications using arm gestures.

Eight different users performed twenty repetitions of twenty different gestures, for a total of 3200 sequences. Each sequence contains acceleration data from the 3-axis accelerometer of a first generation Sony SmartWatch™, as well as timestamps from the different clock sources available on an Android device. The smartwatch was worn on the user's right wrist. The gestures have been manually segmented by the users performing them by tapping the smartwatch screen at the beginning and at the end of every repetition.

• Homepage: https://tev.fbk.eu/resources/smartwatch

• Source code: tfds.datasets.smartwatch_gestures.Builder

• Versions:

  • 1.0.0 (default): Initial release.

• Download size: 2.06 MiB

• Dataset size: 2.64 MiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'train'	3,251

• Feature structure:

FeaturesDict({
    'attempt': uint8,
    'features': Sequence({
        'accel_x': float64,
        'accel_y': float64,
        'accel_z': float64,
        'time_event': uint64,
        'time_millis': uint64,
        'time_nanos': uint64,
    }),
    'gesture': ClassLabel(shape=(), dtype=int64, num_classes=20),
    'participant': uint8,
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
attempt	Tensor		uint8
features	Sequence
features/accel_x	Tensor		float64
features/accel_y	Tensor		float64
features/accel_z	Tensor		float64
features/time_event	Tensor		uint64
features/time_millis	Tensor		uint64
features/time_nanos	Tensor		uint64
gesture	ClassLabel		int64
participant	Tensor		uint8

• Supervised keys (See as_supervised doc): ('features', 'gesture')

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@INPROCEEDINGS{
  6952946,
  author={Costante, Gabriele and Porzi, Lorenzo and Lanz, Oswald and Valigi, Paolo and Ricci, Elisa},
  booktitle={2014 22nd European Signal Processing Conference (EUSIPCO)},
  title={Personalizing a smartwatch-based gesture interface with transfer learning},
  year={2014},
  volume={},
  number={},
  pages={2530-2534},
  doi={} }