Abstract:
The study presented interface of Deep learning for classification of Human activity
Recognition for activities like running, walk, stair climbing and stair climbing down.
Custom made dataset is used based on WISDM standard. Accordingly, CNN-2D
algorithm was designed for training and classification of HAR. Time series data is input
for CNN-2D architecture after conversion of 1D time series data into 2D in pre-processing
stage before fed into CNN network. Classification was performed by firstly training on
PC, later on testing was done raspberry pi by wireless transmission of input data to
raspberry pi from data recording hardware. After classification of HAR on real-time data
prediction was given. The state-of-the-art algorithm performs excellent on custom dataset
and provides accuracy of 97.58%
