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intern:construction_har [2022/08/12 01:33] kenzjkintern:construction_har [2022/08/13 10:28] (current) kenzjk
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   * Research on Xsens and the different modules of sensors it offered was done.   * Research on Xsens and the different modules of sensors it offered was done.
 __Aug/04/22__ __Aug/04/22__
-  * Looked into the modules MTw Awinda and Xsens DOT.+  * Looked into the modules MTw Awinda (https://www.xsens.com/products/mtw-awinda) and Xsens DOT (https://www.xsens.com/xsens-dot).
   * Created a document detailing the differences between the two to aid my professor in deciding which one to purchase.   * Created a document detailing the differences between the two to aid my professor in deciding which one to purchase.
 __Aug/06/22__ __Aug/06/22__
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 __Aug/08/22__ __Aug/08/22__
   * Studied // Human Activity Recognition using Wearable Sensors by Deep Convolutional Neural Networks // and discussed this paper with my professor.   * Studied // Human Activity Recognition using Wearable Sensors by Deep Convolutional Neural Networks // and discussed this paper with my professor.
-  * Did research on some common libraries implemented by machine learning programs, such as SciKit-Learn, NumPy, MatPlotLib, etc.+  * Did research and watched lecture videos on common libraries implemented by machine learning programs, such as SciKit-Learn (https://www.youtube.com/watch?v=0Lt9w-BxKFQ&t=904s), NumPy (https://www.youtube.com/watch?v=zoLHuhefMNw), MatPlotLib (https://www.youtube.com/watch?v=3Xc3CA655Y4), etc.
 __Aug/10/22__ __Aug/10/22__
   * Coded an iris flower-distinguishing program using Sklearn.   * 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.   * 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/12/22__
-  * Continued training the HAR program using the UCI HAR Dataset.+  * Successfully read in all the data from the UCI HAR Dataset (https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones) into my machine learning program. 
 +  * Studied an open source HAR algorithm on GitHub (https://github.com/ma-shamshiri/Human-Activity-Recognition/tree/main/code) to familiarize myself with typical HAR programs.
 __Aug/14/22__ __Aug/14/22__
 +  * Successfully trained my program using the UCI HAR Dataset, but prediction accuracy was not ideal.
 +  * Experimented with common HAR classifiers seen on GitHub to try to find better prediction accuracy.
  
 ==== 5. Final Report PPT ==== ==== 5. Final Report PPT ====
intern/construction_har.1660282384.txt.gz · Last modified: 2022/08/12 01:33 by kenzjk