Modern life is becoming more linked to our devices, and work is being done in a more regulated way. As life became more complicated, it is becoming challenging to keep track of human health and fitness, leading to unexpected illnesses and diseases. Moreover, a lack of activity monitoring and corresponding reminders is preventing the adoption of a healthier lifestyle. This research provides a practical approach for identifying Human Activity by using accelerometer data obtained from wearable devices. The model automatically finds patterns among 33 different physical exercises such as running, rowing, cycling, jogging, etc. and correctly identifies them. The principal component analysis algorithm was used on the statistical features to make the system more robust. Classification of the physical exercise was performed on the reduced features using WEKA. The overall accuracy of 85.51% was obtained using the 10-Fold Cross-Validation method and K nearest Neighbor Algorithm while 84% accuracy for Random Forest. The accuracy obtained was better than previous models and could improve recognition systems in monitoring user activity more precisely.
A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data
