AI Pose Recognition Based on CNN and MediaPipe: From Academic Research to Health Monitoring Practice

This article delves into an academic study on human pose recognition using convolutional neural networks (CNNs), analyzing its technical architecture, transfer learning strategies, edge deployment solutions, and practical application value in detecting head-neck-trunk imbalance. The study aims to develop a low-cost, high-precision, and easily deployable automated posture recognition system to address the problems of strong subjectivity and high cost in traditional posture assessment methods, and apply the technology to multiple scenarios such as personal health management and rehabilitation medical assistance.

In today's digital age, people spending long hours in front of computers and mobile devices has become the norm, leading to widespread head-neck-trunk imbalance issues such as forward head posture and rounded shoulders/hunched back. Over 70% of office workers have varying degrees of posture abnormalities. Traditional posture assessment relies on professional doctors' visual observation (strong subjectivity, difficult to quantify) or expensive 3D motion capture systems (high cost, complex operation), making it hard to popularize in daily health monitoring scenarios. Therefore, developing a low-cost, high-precision, and easily deployable automated posture recognition system has important practical significance.

This study uses CNN as the core technology, combined with transfer learning and edge computing frameworks, to build a complete AI pose recognition solution, divided into three layers:

Data Acquisition Layer: Uses ordinary cameras to capture human images/video streams, lowering deployment barriers; Feature Extraction Layer: Uses Google MediaPipe framework to detect 33 human key points (face, trunk, etc.) in real time; its lightweight design supports real-time inference on mobile devices; Analysis and Decision Layer: Deploys the trained CNN model via TensorFlow Lite, analyzes key point data, and determines whether there is head-neck-trunk imbalance.

One of the core innovations of this study is transfer learning technology: transferring the general feature extraction capabilities of pre-trained models from large-scale datasets (such as ImageNet) to the posture recognition task. The advantages include:

1. Improved Data Efficiency: Only a small amount of domain-specific data is needed to achieve good performance, avoiding the need for massive data;
2. Reduced Training Time: Fine-tuning based on pre-trained weights can converge in tens to hundreds of cycles;
3. Enhanced Generalization: The general visual features of pre-trained models help cope with different lighting conditions, backgrounds, and body types.

To achieve practical application, TensorFlow Lite is selected as the model deployment framework, whose features include: Model Quantization: INT8 quantization compresses the model size to 1/4, increases inference speed by 2-4 times, and controls accuracy loss within 1%; Hardware Acceleration: Supports heterogeneous computing such as GPU, DSP, and NPU; Cross-Platform Support: A unified format can run on multiple platforms like Android and iOS. Through this framework, the model can achieve real-time inference of more than 30 frames per second on ordinary smartphones, meeting daily monitoring needs.

Head-neck-trunk imbalance detection is based on geometric relationship analysis of key points: Key Point Definition: Select core points detected by MediaPipe, such as the nose (head position), left and right acromions, left and right hip joints, etc.; Angle Calculation: Calculate the ear-shoulder-hip angle and the tilt angle of the head relative to the vertical axis (normally, the earlobe should be directly above the acromion); Imbalance Determination: Set angle thresholds; if exceeded for a certain period of time, imbalance is determined, and trend evaluation is performed through time series analysis; Visual Feedback: Overlay bone lines and color markers for imbalance areas on real-time video, and provide quantitative angle values.

The research results have broad application prospects: Personal Health Management: Mobile/desktop applications help users monitor posture in real time and develop good sitting habits; Rehabilitation Medical Assistance: Provide quantitative data for therapists to track rehabilitation progress, and patients can self-monitor at home; Occupational Health Monitoring: Enterprises provide posture screening for employees to prevent occupational diseases; Sports Training Optimization: Coaches analyze athletes' movement postures, correct errors, and prevent injuries.

The current study has limitations and improvement directions: Clothing and Environment Dependence: Loose clothing, complex backgrounds, and extreme lighting affect accuracy; data augmentation and domain adaptation technologies are needed; Lack of 3D Information: Monocular cameras are difficult to obtain depth information; multi-view or depth sensors can be combined; Personalized Adaptation: Need to learn users' personalized baselines and introduce calibration and long-term tracking mechanisms; Privacy Protection: Localized computing and differential privacy technologies are needed to protect user data.