Human Activity Recognition

Project Overview

Built a human activity recognition system during my undergraduate studies that combines multiple machine learning approaches to classify different human activities. The system processes sensor data from smartphones (accelerometer, gyroscope) and integrates computer vision techniques for comprehensive activity detection. I implemented various classification algorithms and explored feature extraction methods to improve recognition accuracy. The project involved data collection, preprocessing, feature engineering, model training, and evaluation. This work enhanced my understanding of sensor data analysis, machine learning model comparison, and the practical challenges of building real-time classification systems. The experience provided valuable insights into applications in healthcare monitoring, fitness tracking, and human-computer interaction.

Key Features

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  Real-time activity classification
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  Multi-sensor data fusion
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  Computer vision integration
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  High accuracy recognition models
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  Healthcare monitoring applications
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  Fitness tracking capabilities
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  Security system integration
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  Scalable architecture design

Technical Challenges

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  Handling multi-modal sensor data
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  Real-time processing optimization
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  Activity classification accuracy
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  System scalability requirements