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All Projects → csarron → lstm_har

csarron / lstm_har

Licence: MIT license
LSTM based human activity recognition using smart phone sensor dataset

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deep-learninglstmrnnsmartphonehuman-activity-recognitionsensor-data

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LSTM for Human Activity Recognition

LSTM based human activity recognition using smart phone sensor dataset(a cellphone attached on the waist). Classifying the type of movement amongst six categories:

  • WALKING,
  • WALKING_UPSTAIRS,
  • WALKING_DOWNSTAIRS,
  • SITTING,
  • STANDING,
  • LAYING.

Dataset

Dataset can be downloaded at https://archive.ics.uci.edu/ml/machine-learning-databases/00240/UCI%20HAR%20Dataset.zip

Follow this link to see a video of the how data is collected

The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.

Model

In this repo, we adopt a 2 layer stacked basic LSTM and use almost the raw data: only the gravity effect has been filtered out of the accelerometer as a preprocessing step for another 3D feature as an input to help learning.

Usage

  1. Install TensorFlow r1.1
  2. Clone this repo by git clone https://github.com/csarron/lstm_har.git
  3. For training the model, use python train_lstm.py (you can pass layer, unit, training epochs as arguments)
  4. For prediction, use python predict_lstm.py

Results

We got 93.48% test accuracy, it took 9186.56s for training on CPU (MacBookPro12,1; Intel Core i7, 3.1GHz; Mem 16GB DDR3 1867 MHz)

Acknowledgement

The original source code is modified from guillaume-chevalier

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