rebeen,Rebeen Ali Hamad,github

human-activity-recognition

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

human-activity-recognition--using-deep-nn

Human Activity Recognition Using Deep Learning

human-activity-recognition-0.97-kaggle

Human Activity Recognition UCI Dataset, final score 0.97196

Classifying the physical activities performed by a user based on accelerometer and gyroscope sensor data collected by a smartphone in the user’s pocket. The activities to be classified are: Standing, Sitting, Stairsup, StairsDown, Walking and Cycling.

human-activity-recognition-2

Simple 1D CNN approach to human-activity-recognition (HAR) in PyTorch.

human-activity-recognition-using-cnn

Convolutional Neural Network for Human Activity Recognition in Tensorflow

human-activity-recognition-using-recurrent-neural-nets-rnn-lstm-and-tensorflow-on-smartphones

This was my Master's project where i was involved using a dataset from Wireless Sensor Data Mining Lab (WISDM) to build a machine learning model to predict basic human activities using a smartphone accelerometer, Using Tensorflow framework, recurrent neural nets and multiple stacks of Long-short-term memory units(LSTM) for building a deep network. After the model was trained, it was saved and exported to an android application and the predictions were made using the model and the interface to speak out the results using text-to-speech API.

human-activity-recognition-with-neural-network-using-gyroscopic-and-accelerometer-variables

The VALIDATION ACCURACY is BEST on KAGGLE. Artificial Neural Network with a validation accuracy of 97.98 % and a precision of 95% was achieved from the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this: 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.

imbalanced-data-with-smote-techniques

This repository contains implementation of some techniques like SMOTE, ADASYN, SMOTE + Tomek Links, SMOTE + ENN to overcome class imbalance in a binary classification problem.

keras-global-context-networks

Keras implementation of Global Context Attention blocks

learn_pytorch

lstm-human-activity-recognition

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN (Deep Learning algo). Classifying the type of movement amongst six activity categories - Guillaume Chevalier