intern:construction_har
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| intern:construction_har [2022/08/11 02:16] – kenzjk | intern:construction_har [2022/08/13 10:28] (current) – kenzjk | ||
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| ==== 2. Reading (can make subsections for notes) ==== | ==== 2. Reading (can make subsections for notes) ==== | ||
| // Human Activity Recognition using Wearable Sensors by Deep Convolutional Neural Networks // | // Human Activity Recognition using Wearable Sensors by Deep Convolutional Neural Networks // | ||
| - | > SVM method has a relatively high accuracy but a relatively high computational cost. | + | * 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). | + | |
| ====3. Coding (w/ description) ==== | ====3. Coding (w/ description) ==== | ||
| Line 16: | Line 16: | ||
| * 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/ | __Aug/ | ||
| - | > Looked into the modules MTw Awinda and Xsens DOT. | + | * Looked into the modules MTw Awinda |
| - | > Created a document detailing the differences between the two to aid my professor in deciding which one to purchase. | + | |
| __Aug/ | __Aug/ | ||
| - | > Attended weekly online meeting with UC Berkeley students and professors and exchanged our findings regarding the most suitable motion sensors for our project. | + | * 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/ | __Aug/ | ||
| - | > 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 and watched lecture videos on common libraries implemented by machine learning programs, such as SciKit-Learn (https:// | ||
| __Aug/ | __Aug/ | ||
| - | > Did research on some common libraries implemented by machine learning programs, such as SciKit-Learn, | + | * 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. | + | |
| __Aug/ | __Aug/ | ||
| + | * Successfully read in all the data from the UCI HAR Dataset (https:// | ||
| + | * Studied an open source HAR algorithm on GitHub (https:// | ||
| __Aug/ | __Aug/ | ||
| + | * 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. | ||
| - | ==== 4.1 Final Report PPT ==== | + | ==== 5. Final Report PPT ==== |
intern/construction_har.1660198595.txt.gz · Last modified: 2022/08/11 02:16 by kenzjk