Jiankun Zheng (Ken)

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

• Also discussed common actions by construction workers (i.e. excavating, laying bricks, climbing, soldering, etc.), which our final algorithm would have to be able to distinguish.

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

• Did research on some common libraries implemented by machine learning programs, such as SciKit-Learn, NumPy, MatPlotLib, etc.
• Coded an iris flower-distinguishing program using Sklearn.
• Began to code and train a standing-or-walking-distinguishing program as a first step to our final program to be implemented at construction sites.

Aug/12/22

Aug/14/22