Deep Learning Pose Detection: Real-Time Sitting Posture Recognition System Based on TensorFlow/Keras

This project is the final project for a deep learning course. It implements a real-time sitting posture recognition system based on TensorFlow/Keras, addressing the problem of bad sitting postures during prolonged sitting through a custom dataset and single-frame image classification. The core is using CNN technology to automatically identify correct and incorrect sitting postures, providing a technical solution to improve sedentary health. It features high computational efficiency and easy deployment, with application scenarios covering personal health, office management, and rehabilitation medicine.

In modern society, prolonged sitting has become the norm. Bad sitting postures lead to health risks such as spinal problems, muscle strain, and blood circulation disorders—over 80% of adults experience back pain related to bad sitting postures. Traditional self-reminders have limited effectiveness. This project aims to develop an automatic sitting posture recognition system using deep learning technology to address the sedentary health crisis.

A single-frame classification strategy is adopted, simplifying pose detection into a binary classification task (good/bad posture). It takes a single RGB image as input and outputs the classification result. Advantages: high computational efficiency, low data demand, and easy deployment. Custom dataset construction: covers diverse scenarios, populations, and postures, guided by expert annotation and cross-validation, combined with data augmentation techniques like geometric transformations and color adjustments to improve generalization ability. The model is based on TensorFlow/Keras, with a network architecture including input layer, convolutional layers, pooling layers, fully connected layers, and Dropout layers. Training uses binary cross-entropy/Focal Loss as the loss function, Adam/SGD as the optimizer, and regularization techniques to prevent overfitting.

Evaluation metrics include accuracy, precision, recall, F1-Score, confusion matrix, ROC curve/AUC. Deployment considerations: inference speed optimization (model quantization, pruning, knowledge distillation, hardware acceleration); user experience aspects focus on feedback delay, false alarm handling, and sampling frequency for continuous monitoring.

Application scenarios: 1. Personal health assistant: real-time monitoring and reminders via desktop/mobile apps, automatic adjustment of smart seats; 2. Office health management: corporate health solutions, workstation optimization, health report generation; 3. Rehabilitation medicine: physical therapy assistance, remote rehabilitation supervision, exercise posture assessment.

Technical challenges and solutions: 1. Lighting changes: data augmentation to simulate lighting, preprocessing (histogram equalization), Batch Normalization; 2. Occlusion issues: designing occlusion-robust features, multi-view fusion, context reasoning; 3. Individual differences: data covering diverse populations, domain adaptation techniques, personalized calibration.

Future directions: 1. Multi-frame temporal modeling: LSTM/GRU to capture temporal patterns, 3D CNN to model spatiotemporal dimensions, Transformer self-attention mechanism; 2. Fine-grained posture analysis: key point localization, angle measurement, posture scoring; 3. Cross-modal fusion: combining data from depth cameras, pressure sensors, and IMU wearable devices.

This project demonstrates the application potential of deep learning in daily health scenarios, realizing practical functions through a complete process (from data collection to deployment). Value: enhancing health awareness, helping behavior change, and preventing diseases. It is a good practice project for developers, covering the entire process and having practical effects. In the future, with technological progress, more AI health assistants will enter daily life.