1 Project Introduction
1.1 Demo Video
1.2 Project Design
Ever wanted to experience the thrill of skiing without leaving home? This project leverages the Pose Classification module in the Mind+ model training tool, combined with RealTime Mode deployment, to create an immersive "Skiing Adventure" interactive experience. By training a model to recognize human skiing postures (straight, left turn, right turn), it enables real-time control of the skier in the game to glide freely and avoid obstacles on a virtual snow path, allowing users to enjoy the fun of skiing indoors.
By completing this project, you will learn:
• Model Training: How to train a pose classification model using the Mind+ v2.0 model training tool.
• Model Deployment: How to deploy the trained model to RealTime Mode for real-time pose recognition and game interaction.
• Ethical Discussion: How to think about and address artificial intelligence ethical issues during technical practice, such as the boundaries between virtual immersion and reality, and algorithm fairness.
1.3 Project Implementation Process
This project trains a "Skiing Pose Classification Model" using the Pose Classification module in the Mind+ v2.0 model training tool and deploys it to the RealTime Mode to achieve real-time pose recognition and control. The entire project process is as follows:
2 AI Knowledge Garden-Pose Classification
2.1 Pose Classification
Pose Classification is an important technology in the field of artificial intelligence computer vision. Its core involves identifying the position and angle of human keypoints to determine the current pose category. For example, distinguishing actions such as "running," "jumping," and "standing" whose core lies in feature extraction and classification of human skeletal joints.
2.2 Applications of Pose Classification
Pose classification technology has a wide range of application scenarios across multiple fields:
• Fitness Tracking: Recognizes exercise postures such as running, squats, push-ups, and yoga to help users correct movements.
• Health Monitoring: Detects sitting, standing, or sleeping postures to remind users of poor posture and assist in office work or rehabilitation training.
• Smart Interaction Control: Controls devices through specific gestures or body postures, such as waving to switch pages or raising one’s hand to answer calls.
• Education & Training Feedback: Used in physical education or dance training to analyze action accuracy and provide feedback.
• Safety Monitoring Alerts: Detects dangerous postures (e.g., excessive bending, falling) to enable real-time alarm and safety protection.
3 Skiing Pose Classification Model Training
3.1 Download Software and Create Training Project
Download and install Mind+ 2.0 or higher from the official website, then double-click to open it once installed.Download address: https://www.mindplus.cc/.
Create a new project, click "Model" in the left navigation bar, and select the "Pose Classification" task.
3.2 Data Preparation
• Label Settings
• Data Collection
Data can be collected via the "Webcam" or "Upload" from local files. At least 20 image samples are required for each category.
3.3 Model Training
• Model Training
Click the "Train Model" button to start model training.
• Training Parameter Settings
Click the "Advanced Settings" button to configure parameters.
• Training Process and Results Observation
During training, click "Learn More" in "Advanced Settings" to view the training log.
3.4 Model Validation
• Testing a Single Image
Select "File", click "Click to Upload File", and select an untrained image for validation.
• Real-time Detection Test
Select "Webcam" to perform real-time Instance Segmentation.
• Model Optimization and Retraining
When the model validation result is not ideal, we can retrain the model through methods such as data quality optimization and model parameter adjustment.
3.5 Model Export
Click the "Export Model" button to export the model compressed package to your local computer.
4 Skiing Pose Classification Model Deployment
4.1 Hardware List
Windows 10 or later versions
4.2 Model Inference and Application
5 AI Ethics Discussion
Technology itself is neutral, but the designers and users of technology bear responsibility. When enjoying the convenience AI brings, we need to actively think about the ethical issues behind technology and explore responsible solutions. Now, let us explore the core ethical challenges that may arise in the electronic floral designer project.
5.1 Virtual Immersion and Reality Boundaries
Problem: Pose recognition games create highly immersive virtual experiences, which may lead users to become overly engrossed in virtual worlds for extended periods.
Suggestion: Design mandatory "break reminders" in the program, such as displaying a prompt every 10 minutes of gameplay and pausing the game to encourage users to take a break.
5.2 Algorithm Fairness and Body Diversity
Problem: Pose recognition models may have biases in training data, affecting users with different body types, heights, ages, or physical abilities...
Suggestion: Expand the dataset to include people's postures across different ages and body types, ensuring broad coverage across diverse demographics.
6 Self-Test
6.1 Extended Exercise
Enrich pose categories, add labels such as "brake" and "jump" for skiing poses, add additional samples, and retrain the model to enhance gameplay diversity.
Attachment
https://drive.google.com/file/d/1Kp0TlFmnW7prqb6YlpkntIvypLdzX0P-/view?usp=sharing
