Intelligent Wearable IoT Device for Road Safety with Advanced Gesture Detection via Machine Learning

Abstract

This invention relates to an intelligent wearable IoT device designed to enhance road user safety through advanced gesture detection powered by machine learning. The device incorporates a compact array of sensors, including accelerometers, gyroscopes, and proximity sensors, to continuously monitor the user's movements and surrounding environment. Leveraging a machine learning model trained on a diverse dataset of gestures and road scenarios, the device accurately identifies critical hand gestures, body postures, or motions that indicate the user's intent or a potential safety hazard. Real-time data is processed on an edge-computing module and transmitted to a centralized IoT platform for actionable insights. The system supports features such as automatic signaling for lane changes, emergency alerts, and collision avoidance notifications to nearby vehicles or infrastructure. Designed for cyclists, motorcyclists, and pedestrians, the wearable integrates seamlessly with smart traffic management systems, ensuring proactive and context-aware safety interventions. The device emphasizes low power consumption, scalability, and adaptability to diverse road environments, promoting safer mobility for all road users.

Specification

Description:This invention relates to an intelligent wearable IoT device designed to enhance road user safety through advanced gesture detection powered by machine learning. The device incorporates a compact array of sensors, including accelerometers, gyroscopes, and proximity sensors, to continuously monitor the user's movements and surrounding environment. Leveraging a machine learning model trained on a diverse dataset of gestures and road scenarios, the device accurately identifies critical hand gestures, body postures, or motions that indicate the user's intent or a potential safety hazard. Real-time data is processed on an edge-computing module and transmitted to a centralized IoT platform for actionable insights. The system supports features such as automatic signaling for lane changes, emergency alerts, and collision avoidance notifications to nearby vehicles or infrastructure. Designed for cyclists, motorcyclists, and pedestrians, the wearable integrates seamlessly with smart traffic management systems, ensuring proactive and context-aware safety interventions. The device emphasizes low power consumption, scalability, and adaptability to diverse road environments, promoting safer mobility for all road users. , Claims:1. A wearable IoT device for enhancing road user safety, comprising:
• a set of sensors configured to capture motion and gesture data of a road user;
• a processing unit with machine learning capabilities, trained to recognize a set of predefined gestures based on said motion and gesture data;
• visual and audible indicators to provide alerts to the wearer and surrounding road users;
• a proximity sensor to detect nearby vehicles or obstacles;
• a communication module for transmitting data to a central traffic management system; and
• a power source to provide energy to the device.
2. The device of claim 1, wherein the set of sensors includes at least one accelerometer, gyroscope, and proximity sensor.
3. The device of claim 2, wherein the processing unit employs machine learning algorithms to analyze and classify gestures in real-time.
4. The device of claim 2, wherein the visual indicators comprise LEDs or other light sources to signal the intentions of the road user.
5. The device of claim 3, wherein the audible indicators emit sounds or notifications to alert surrounding road users.
6. The device of claim 3, further comprising a data logging capability to record and store motion and gesture data for analysis of road user behavior patterns.
7. The method of claim 4, further comprising detecting the presence of nearby vehicles or obstacles using the proximity sensor and providing warnings to the wearer.

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