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Human Activity Recognition Using Smartphone Sensors

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Introduction

Human Activity Recognition (HAR) is an emerging field that focuses on identifying and understanding human actions through the data collected from sensors. With the proliferation of smartphones equipped with various sensors, there is a growing interest in utilizing these devices for HAR. This project proposal outlines a system that leverages smartphone sensor data to accurately recognize human activities using machine learning techniques.

Background

Recent research has demonstrated the potential of using smartphone sensors, such as accelerometers and gyroscopes, for effective HAR. These sensors provide continuous data streams that can be analyzed to identify patterns corresponding to specific activities like walking, running, sitting, and standing. Machine learning algorithms, particularly deep learning models, have shown promise in enhancing the accuracy and efficiency of HAR systems by effectively processing and interpreting sensor data.

Project Objective

The primary objective of this project is to develop a robust HAR system using smartphone sensor data. The system aims to improve upon existing methods by incorporating advanced feature extraction techniques and leveraging large-scale datasets for training and evaluation.

Methodology

1. Data Collection and Preprocessing

  • Datasets: Utilize publicly available datasets such as the UCI HAR Dataset for training and evaluation.
  • Feature Extraction: Extract relevant features from raw sensor data, including time-domain and frequency-domain features.

2. Model Architecture

  • Deep Learning Model: Implement a deep learning model, such as a Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN), to process sensor data and recognize activities.
  • Feature Selection: Use feature selection techniques to identify the most informative features for activity recognition.

3. Training and Evaluation

  • Training: Use appropriate loss functions and optimization algorithms to train the model.
  • Evaluation Metrics: Measure performance using metrics such as accuracy, precision, recall, and F1-score.

Expected Outcomes

The proposed system is expected to achieve high accuracy in recognizing human activities compared to traditional methods. By utilizing deep learning techniques, the system should effectively handle variations in sensor data across different users and environments.

Conclusion

This project aims to advance the field of human activity recognition by developing a state-of-the-art system capable of accurately detecting activities using smartphone sensors. The integration of deep learning models is anticipated to provide significant improvements in performance.

For further details on related research, please refer to the paper "Human Activity Recognition Using Smartphone Sensors," available at https://ieeexplore.ieee.org/document/8489099.

Dataset link: UCI HAR Dataset

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