Rocky5502/Web-Data-Mining-Project-2021-SCU-

In this work, To implement this project we used Python Programming language and Libraries: Scikit-Learn/SciPy, NumPy, Pandas, Matplotlibwe carried out and evaluated a classification algorithm to notice 4 fundamental human physical activities(walking, cycling, sitting, and lying) using five triaxial accelerometers worn simultaneously on unique parts of thebody (dominant hip, higher arm, ankle, thigh, and wrist). The accelerometer information was collected, cleaned, and preprocessed to extract elements from the 10 s window. These time and frequency area aspects had been used with Random Forest and k-Nearest Neighbour classifier to classify challenge activities. The algorithms had been evaluated based totallyon Leave-One-Subject-Out (LOSO) and ten-fold cross-validation method the usage of each accelerometer records as well as annotated undertaking labels from 33 contributors in a lab. Random Forest showed the first-rate overall performance recognizing the activities with common accuracy of 89 % for the LOSO approach for hip data. Combining statistics from both hip and ankle improved the common accuracy with the aid of 3.5 %, and by using 10% for mendacity activity, which had the lowest classification accuracy (80%) for hip data. We conclude that our algorithm that makes use of 10 aspects suggests right endeavor classification, and is computationally efficient to be carried out in real-time cell systems.