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 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.
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.
- Install TensorFlow r1.1
- Clone this repo by
git clone https://github.com/csarron/lstm_har.git - For training the model, use
python train_lstm.py(you can pass layer, unit, training epochs as arguments) - For prediction, use
python predict_lstm.py
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)
The original source code is modified from guillaume-chevalier