Human Activity Recognition using Machine Learning
| dc.contributor.advisor | Melkamu Hunegnaw (PhD) | |
| dc.contributor.author | Rediet Desalegn | |
| dc.date.accessioned | 2025-05-14T09:32:46Z | |
| dc.date.available | 2025-05-14T09:32:46Z | |
| dc.date.issued | 2023-08 | |
| dc.description.abstract | Human activity recognition is a broad field of study concerned with identifying the specific movement or action of a person based on sensor data. It can improve people’s well-being by automatically assessing and summarizing their daily activities. In this thesis an automatic detection of nine different human activities from body sensor data was developed. The human activity data was collected from 30 participants between the age of 12 and 55. From those 17 participants were male and 13 participants were female. Participants completed nine activities (sitting, standing, walking, running, upstairs, downstairs, sit-up, jumping and cycling) while wearing an I-phone eight plus in the pocket. Records from 18 individuals was used for training, 6 participants data was used for validation and the remaining 6 records were used for testing. The human activity data is captured via phone’s integrated acceleration and gyroscope sensors. Hence a total of nine-dimensional data (triaxial accelerometer data (linear acceleration and body acceleration) and triaxial gyroscope data) was acquired using these two sensors. In all cases the sample rate used to capture the data was 50Hz which is in phone application. In this study both classical machine learning and deep learning methods were used. In classical techniques four machine learning classifiers (SVM, KNN, logistic regression and decision tree) have been used to categorize the data. For this to work 18 set of features were carefully computed. Three deep learning models (CNN, LSTM and hybrid CNN-LSTM) have been also trained in the accurate detection of human activity classification. The activity recognition of these algorithms has been objectively measured by using accuracy, f1 score, precision, recall and confusion matrix. Under this study accuracy of SVM 89%, KNN 81%, Logistic Regression 87%, Decision Tree 79%, CNN 92%, LSTM 91% and CNN-LSTM 94% are computed. The highest performance accuracy of 94% was achieved when classifying using hybrid CNN-LSTM model. This indicates that with a simplified approach, multiple human activities can be reasonably detected. By analyzing these activities, one can promote a healthy life style for the subjects. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/5459 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | human activity recognition | |
| dc.subject | classical machine learning | |
| dc.subject | deep learning | |
| dc.title | Human Activity Recognition using Machine Learning | |
| dc.type | Thesis |