Abstract:
The difficulty of identifying a body's behavior based on sensor data, such as an
accelerometer in a smartphone, is known as activity recognition. It's among the most
widely studied topics in the field of machine learning-based classification. Cooking
Activity Recognition Challenge (CARC) asked participants to recognize food
preparation using motion capture and acceleration sensors. Two smartphones, two
wristbands, and motion-capturing equipment were used to collect three-axis (x, y,
z) acceleration data and motion data for the CARC dataset. One of the most challenging
difficulties to solve in this investigation was identifying complicated tasks as smaller
activities that are part of larger activities. Using a Convolutional Neural Network
(CNN) and a Bidirectional LSTM, we’ve built a deep learning approach that extracts
dynamical data for macro and micro activity identification. The model we proposed for
that kind of dataset has a classification accuracy of 83% for macro activity and
85.3% for micro activity, respectively.
