Project Title : Human Activity Recognition

Objective:
To build an AI system that can automatically identify and classify human activities (like walking, running, sitting, etc.) using data from sensors or videos.

What It Does:
The model learns patterns in sensor data (like from smartphones or wearables) or video frames to recognize what activity a person is performing.

Key Concepts:

Time-series classification (for sensor data)

Sequence modeling (activities happen over time)

Use of deep learning (like CNNs or LSTMs) for high accuracy

Steps Involved:

Dataset Collection:

Use popular datasets like UCI HAR, WISDM, or PAMAP2.

These usually contain accelerometer and gyroscope data from smartphones or smartwatches.

Preprocessing:

Normalize the sensor data.

Segment data into windows (e.g., 2-5 seconds).

Label each window with the corresponding activity.

Feature Extraction (Optional):

Extract statistical features (mean, standard deviation, energy).

Or use raw data directly with deep learning.

Model Building:

Traditional ML: Random Forest, SVM, KNN.

Deep Learning: CNN (for spatial features), LSTM or GRU (for time-based sequence modeling), or a hybrid CNN-LSTM model.

Model Evaluation:

Use accuracy, confusion matrix, precision, and recall.

Test on unseen subjects for generalization.

Deployment (Optional):

Build a mobile or web app for real-time activity tracking.

Applications:

Fitness tracking and smartwatches

Healthcare (e.g., fall detection)

Elderly monitoring systems

Smart home automation

Sports performance analysis

Tools & Technologies:

Languages: Python

Libraries: TensorFlow/Keras, Scikit-learn, Pandas, NumPy, Matplotlib

Hardware: Smartphone, smartwatch, or wearable sensors (for custom data)