GitHub / girishp92 / 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.

JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/girishp92%2FHuman-activity-recognition-using-Recurrent-Neural-Nets-RNN-LSTM-and-Tensorflow-on-Smartphones

Topics: android-application, androidstudio, deep-learning, hidden-units, human-activities, lstm-neural-networks, machine-learning, pickle, proof-of-concept, pycharm-ide, python3, rnn-tensorflow, scikit-learn, smartphone-accelerometer

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