Data Mining Project
Recognizing human activity using multiple wearable accelerometer sensors placed at different body positions.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Human Activity Recognition Using Recurrent Neural Nets Rnn Lstm And Tensorflow On Smartphones | 46 | 7 years ago | 1 | Python | ||||||
| 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 | 44 | 7 years ago | Python | |||||||
| 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. | ||||||||||
| Data Mining Project | 20 | 8 years ago | Python | |||||||
| Recognizing human activity using multiple wearable accelerometer sensors placed at different body positions. | ||||||||||
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