In fields such as sports rehabilitation, wearable devices, and human-computer interaction, the precise capture of human motion remains a core technological challenge. Among these, the shoulder joint—one of the most complex and highly mobile joints in the body—presents persistent difficulties in long-term stable 3D motion monitoring.

From April 14 to 16, 2026, the Shenzhen International Sensor and Application Technology Exhibition (Sensor Shenzhen 2026) officially commenced at the Futian Convention and Exhibition Center in Shenzhen. As the annual flagship event of China's sensor industry, this edition of the exhibition centered on the theme "Core Sensing ·

In the intelligent upgrading of modern machine tools and processing equipment, sensors serve as the "sensory nerves," permeating the entire processing workflow by converting physical and mechanical quantities into recognizable electrical signals. They provide core support for equipment control, precision enhancement, and fault prediction.

As the core power equipment in industrial production, energy supply, and other fields, the operational stability of motors directly determines production efficiency, safety assurance, and operational costs. Against the backdrop of intelligent industrial upgrading, traditional manual inspection monitoring methods can no longer meet the demands for precision and real-time performance.

As the core continuous production equipment in the plastic processing industry, the operational stability of an extruder directly determines product quality, production efficiency, and operating costs. Equipment condition monitoring serves as a critical means to achieve predictive maintenance and avoid sudden failures. Sensors, acting as the "sensory organs" for data acquisition, are the essential link connecting equipment operational status and monitoring systems. These two elements interdependently and synergistically form the foundation of intelligent extruder operation and maintenance.

Artificial intelligence is fundamentally transforming synthetic biology biosensors (SBBs) from traditional rational design to AI-driven predictive engineering. This review establishes the first systematic integration framework of AI algorithms with the design-build-test-learn (DBTL) full cycle of synthetic biology biosensors