This is the research repository for the CHI 2019 Paper: "Sensing Fine-Grained Hand Activity with Smartwatches." This repository contains links and instructions to a subset of our dataset.
The system is written in python3, specifically tensorflow and keras.
To begin, we recommend using virtualenv to run a self-contained setup:
$ virtualenv ./hand-activities -p python3.6
$ source hand-activities/bin/activateOnce virtualenv is activated, install the following dependencies via pip:
(hand-activities)$ git clone https://github.com/FIGLAB/hand-activities.git
(hand-activities)$ pip install numpy
(hand-activities)$ pip install tensorflow
(hand-activities)$ pip install keras
(hand-activities)$ pip install wgetDownload the dataset here: https://www.dropbox.com/sh/vawocn8ae2eiy96/AAAA7rgUCSp4RIXLixARh1Y8a?dl=1
Unzip each file, and you’ll get numpy arrays that are organized by user or by round.
data = np.load('path_to_features_file.npy’)
print(data.shape)
You’ll have matched label array pairs per condition. For example:
round1_x = np.load(‘round1_features_X.npy’)
round1_y = np.load(‘round1_features_Y.npy’)
round1_labels = np.load(‘round1_features_labels.npy’)
print(round1_x.shape)
print(round1_y.shape)
print(round1_labels.shape)
If you print dataset shapes, their row counts should line up. X is a 3 x 256 x 48 array (x, y, z axes of the high-speed accelerometer signal).
Each row is a datapoint. You can assemble your train / test / validation splits by concatenating different conditions.
We suggest extracting a row from the input features (X), and plotting then using python matplotlib.
THE PROGRAM IS DISTRIBUTED IN THE HOPE THAT IT WILL BE USEFUL, BUT WITHOUT ANY WARRANTY. IT IS PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
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