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Introduction:

The development of technology has enabled the collection of large amounts of data through various sources such as sensors, smartphones, and wearables. Time series data, in particular, has become increasingly valuable in understanding human behavior and activities. Human activity recognition (HAR) is the process of automatically identifying activities based on sensor data. HAR has a wide range of applications such as healthcare monitoring, sports performance analysis, and smart home automation.

This thesis aims to develop a human activity recognition system using time series data. The system will utilize machine learning techniques to analyze and classify activities based on sensor data. This research is important as it can provide valuable insights into human behavior and could potentially improve the quality of life for individuals in various domains.

Table of Contents:

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Introduction to Human Activity Recognition
2.2 Time Series Data Analysis
2.3 Machine Learning Algorithms for HAR
2.4 Previous Studies on HAR
2.5 Challenges in HAR Using Time Series Data
2.6 Data Preprocessing Techniques
2.7 Feature Extraction and Selection
2.8 Performance Evaluation Metrics
2.9 Open Source Tools for HAR
2.10 Summary of Literature Review

Chapter 3: System Design and Methodology
3.1 System Architecture
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Extraction
3.5 Machine Learning Model Selection
3.6 Model Training and Testing
3.7 Hyperparameter Tuning
3.8 Performance Evaluation
3.9 Cross-validation
3.10 Summary of System Design and Methodology

Chapter 4: System Implementation
4.1 Implementation Environment
4.2 Data Collection Setup
4.3 Data Preprocessing Pipeline
4.4 Feature Extraction Module
4.5 Machine Learning Model Implementation
4.6 Model Training and Evaluation
4.7 Hyperparameter Tuning Results
4.8 Performance Evaluation Metrics Results
4.9 System Testing and Validation
4.10 Summary of System Implementation

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contribution to the Field
5.3 Future Research Directions
5.4 Conclusion

Thesis Overview:

The thesis aims to develop a human activity recognition system using time series data. The research will focus on utilizing machine learning techniques to analyze and classify activities based on sensor data. The study will begin with an introduction to the topic, providing background information, stating the problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of terms.

Chapter two will consist of a literature review on human activity recognition, time series data analysis, machine learning algorithms for HAR, previous studies, challenges, data preprocessing techniques, feature extraction, performance evaluation metrics, and open-source tools for HAR.

Chapter three will cover the system design and methodology, including system architecture, data collection, preprocessing, feature extraction, machine learning model selection, training, testing, hyperparameter tuning, performance evaluation, and cross-validation.

Chapter four will detail the system implementation, discussing the implementation environment, data collection setup, preprocessing pipeline, feature extraction module, machine learning model implementation, training and evaluation, hyperparameter tuning results, performance evaluation metrics results, system testing, and validation.

Finally, chapter five will conclude the thesis with a summary of findings, contribution to the field, future research directions, and a conclusion. The thesis will contribute to the field of human activity recognition by developing a robust system using time series data and machine learning techniques.

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