https://github.com/sushantdhumak/human-activity-recognition-with-smartphones
Kaggle Machine Learning Competition Project : To classify activities into one of the six activities performed by individuals by reading the inertial sensors data collected using Smartphone.
https://github.com/sushantdhumak/human-activity-recognition-with-smartphones
accelerometer classification confusion-matrix decision-tree-classifier exploratory-data-analysis gradient-boosting-classifier grid-search gridsearchcv gyroscope human-activity-recognition iot-application kernel-svm-classifier linear-discriminant-analysis logistic-regression python-3 random-forest-classifier rbf-classifier seaborn support-vector-machine t-sne
Last synced: 5 months ago
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Kaggle Machine Learning Competition Project : To classify activities into one of the six activities performed by individuals by reading the inertial sensors data collected using Smartphone.
- Host: GitHub
- URL: https://github.com/sushantdhumak/human-activity-recognition-with-smartphones
- Owner: sushantdhumak
- Created: 2019-10-31T15:51:12.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-10-31T16:51:50.000Z (over 5 years ago)
- Last Synced: 2025-01-01T18:34:23.734Z (6 months ago)
- Topics: accelerometer, classification, confusion-matrix, decision-tree-classifier, exploratory-data-analysis, gradient-boosting-classifier, grid-search, gridsearchcv, gyroscope, human-activity-recognition, iot-application, kernel-svm-classifier, linear-discriminant-analysis, logistic-regression, python-3, random-forest-classifier, rbf-classifier, seaborn, support-vector-machine, t-sne
- Language: Jupyter Notebook
- Homepage:
- Size: 46.4 MB
- Stars: 6
- Watchers: 1
- Forks: 2
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Human Activity Recognition using Machine Learning
## Kaggle Machine Learning Project
The Human Activity Recognition database was built from the recordings of 30 study participants performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. The objective is to classify activities into one of the six activities performed.
### Disclaimer:
The given solutions in this project are only for reference purpose.
### Kaggle Competition Link:
https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
### Description of experiment
The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data.
The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain.
### Attribute information
For each record in the dataset the following is provided:
* Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration.
* Triaxial Angular velocity from the gyroscope.
* A 561-feature vector with time and frequency domain variables.
* Its activity label.
* An identifier of the subject who carried out the experiment.
### Video dataset overview
Follow this link to see a video of the 6 activities recorded in the experiment with one of the participants:
https://www.youtube.com/watch?v=XOEN9W05_4A&feature=youtu.be
### Results of Model used in the Notebook
Logistic Regression:
Accuracy - 96.13% , Error - 3.868%
Linear SVC:
Accuracy - 96.30% , Error - 3.699%
RBF SVM classifier:
Accuracy - 96.57% , Error - 3.427%
Decision Tree:
Accuracy - 95.15% , Error - 4.852%
Random Forest:
Accuracy - 96.20% , Error - 3.8%
GradientBoosting:
Accuracy - 95.42% , Error - 4.581%
### Relevant papers
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge L. Reyes-Ortiz. Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic. Journal of Universal Computer Science. Special Issue in Ambient Assisted Living: Home Care. Volume 19, Issue 9. May 2013
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. 4th International Workshop of Ambient Assited Living, IWAAL 2012, Vitoria-Gasteiz, Spain, December 3-5, 2012. Proceedings. Lecture Notes in Computer Science 2012, pp 216-223.
Jorge Luis Reyes-Ortiz, Alessandro Ghio, Xavier Parra-Llanas, Davide Anguita, Joan Cabestany, Andreu Català. Human Activity and Motion Disorder Recognition: Towards Smarter Interactive Cognitive Environments. 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.