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Use a LSTM network to predict human activities from sensor signals collected from a smartphone

License: GNU General Public License v3.0

Python 100.00%

human-activity-recognition-1's Introduction

Human Activity Recognition from smartphone signals

Usage

Download the dataset:

wget https://archive.ics.uci.edu/ml/machine-learning-databases/00240/UCI%20HAR%20Dataset.zip
unzip UCI\ HAR\ Dataset.zip

Build the image and run:

docker build -t keras .
docker run -it -v `pwd`:/app keras python3 main.py

Results

Confusion matrix:

Pred                LAYING  SITTING  STANDING  WALKING  WALKING_DOWNSTAIRS  WALKING_UPSTAIRS
True
LAYING                 510        0        27        0                   0                 0
SITTING                  0      384       105        1                   0                 1
STANDING                 0       89       443        0                   0                 0
WALKING                  0        0         0      449                  28                19
WALKING_DOWNSTAIRS       0        0         0        0                 419                 1
WALKING_UPSTAIRS         0        5         1        2                  58               405

Resources:

Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012

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