Wearable Device Motion State Recognition for Delivery Accuracy

The system continuously monitors the worker's motion state through camera-based image recognition and provides real-time feedback by presenting attention information when abnormal motion states are detected. This closed-loop feedback mechanism improves delivery accuracy by correcting improper handling behaviors as they occur, rather than relying on post-event analysis or simple predefined alerts.

The patent replaces traditional mechanical or manual monitoring systems with an automated vision-based detection system. Image recognition algorithms analyze camera footage to identify motion states, substituting human observation and manual intervention with automated optical detection and computational analysis, thereby improving reliability without proportionally increasing mechanical complexity.

The system performs self-monitoring and self-assessment by automatically analyzing its own operational data through image recognition. The wearable device captures its own motion data, the system independently processes this data through algorithms, and generates appropriate alerts without external intervention. This self-service capability improves delivery efficiency by providing autonomous real-time guidance while keeping the system architecture relatively simple.

The system monitors changes in motion state parameters (such as acceleration, position, orientation) to determine when attention information should be presented. By detecting parameter changes that indicate abnormal motion patterns, the system can dynamically adjust alert presentation timing and content, improving delivery efficiency through adaptive behavior without requiring complex decision-making logic.

Instead of continuous real-time analysis, the system performs image recognition at periodic intervals or triggered by specific events (such as when motion state changes are detected). This periodic processing approach maintains adequate measurement precision for detecting delivery-related motion states while significantly reducing computational load and energy consumption compared to truly continuous analysis.

The system applies partial image recognition processing by focusing analysis only on critical moments or specific regions of interest in the video stream, rather than processing every frame in full detail. This selective processing maintains sufficient accuracy for detecting abnormal motion states while reducing overall computational requirements and energy usage.