Jiankun Zheng (Ken)

A. Human Activity Recognition using Wearable Sensors by Deep Convolutional Neural Networks

SVM method has a relatively high accuracy but a relatively high computational cost.
Feature Selection method has a relatively low computational cost but a significantly low accuracy.
A trade-off exists between accuracy and computational cost.
DCNN+ and DCNN methods achieve a good balance between the two (accuracy and efficiency).

Aug/02/22

Learned about the basics of Human Activity Recognition and gathered that 3D motion sensors often utilized accelerometers and gyroscopes send motion signals to a laptop with code ready to interpret the signals to classify the motion.
Research on Xsens and the different modules of sensors it offered was done.

Aug/04/22

Looked into the modules MTw Awinda and Xsens DOT.
Created a document detailing the differences between the two to aid my professor in deciding which one to purchase.

Aug/06/22

Attended the weekly online meeting with UC Berkeley students and professors and exchanged our findings regarding the most suitable motion sensors for our project.

Aug/08/22

Studied Human Activity Recognition using Wearable Sensors by Deep Convolutional Neural Networks and discussed this paper with my professor

Aug/10/22

Aug/12/22

Aug/14/22