Human Actvity Recognition using mobile Sensors data is a research based project to build a model/system using RCNN-LSTM to detect realtime human activity using mobile's motion sensors data like Accelerometer and Gyroscope.

If the 3-axes of the human motion model are considered as the 3 channels of a RGB image, the value of the XYZ axial data can be mapped into the value of the RGB channel data in a RGB image respectively. Namely, each 3-axial data can be converted into an RGB pixel. The 400 pieces of 3-axial data cached in the sliding window can be viewed as a bitmap with size of 20 or 20 pixels.

• python3
• tensorflow: >=2.0.0
• numpy
• pandas
• matplotlib

• 1.MobiFall＆MobiAct DataSet http://www.bmi.teicrete.gr/index.php/research/mobiact

• 2.SisFall http://sistemic.udea.edu.co/en/investigacion/proyectos/english-falls/

Max Training Accuracy: 92.405%
Max Testing Accuracy: 96.94%