IoT-based snowboard self-training system

Snowboard training system providing posture analysis and voice coaching based on sensors and beacons

This technology is an IoT-based snowboard self-training system consisting of a sub-control unit that measures speed via a helmet air pressure sensor to provide speeding alerts, and a main control unit that analyzes posture using board motion and foot pressure data, estimates location via beacon signals, and transmits the information to a cloud server.

Because both feet are bound to a single board, snowboarding carries a higher risk of injury than skiing, and the number of injuries has been increasing annually. However, there has been a lack of means to correct posture or manage speed on the slopes. Existing virtual reality training equipment is disconnected from actual slope riding, making it difficult to simultaneously improve a beginner's skills and provide engaging elements for experienced riders.

This technology uses a 9-axis motion sensor and binding pressure sensors to analyze tilt, rotation angle, and external pressure, providing voice feedback on bad habits or posture issues. It offers free, pendulum, turn, and record modes, along with tracking and ranking features. It can be applied to ski resort safety management services and wearable sports coaching devices, helping to reduce accidents while creating experiential content that keeps users coming back.

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Key Features:

• Sub-control unit that measures a snowboarder's speed using sensed air pressure and plays a speed alert and voice file when the speed limit is exceeded
• Main control unit that identifies a snowboarder's habits and posture issues based on snowboard motion, foot pressure, and speed data to provide self-training information
• Location estimation function that acquires current coordinate data using triangulation of RSSI values from multiple beacons installed on the slope and fingerprinting techniques
• Training mode configuration including free mode, pendulum mode for beginners, turn mode for intermediate riders, and record mode for sharing slope logs

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This invention was developed with research support from the Ministry of Science, ICT and Future Planning for the development of edge computing and data networking-based edge network systems to realize next-generation IoT services.

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