Sensor-Based Frameworks for Predictive Condition Assessment and Resource Recovery Efficiency in Labor-Intensive Recycling Systems

Introduction
Efficient resource recovery in labor-intensive recycling systems is essential for improving circular economy performance, particularly in developing economies where manual sorting is dominant. In such systems, worker fatigue, environmental stressors, and inconsistent operational conditions significantly affect sorting accuracy, leading to missed recyclable materials, increased contamination, reduced material purity, and overall resource inefficiency through material downcycling. This study addresses these challenges by proposing a sensor-based framework to enhance predictive condition assessment and improve recycling performance outcomes.

Methods
The proposed framework integrates multi-modal sensing technologies, including wearable devices for physiological workload monitoring and environmental sensors for thermal and noise exposure assessment. These heterogeneous data streams are integrated through a data fusion layer to generate operational risk indicators that capture both human and environmental influences on sorting performance. The framework follows a structured process of data acquisition, signal preprocessing, feature extraction, and predictive assessment to support continuous monitoring of recycling operations.

Results
The framework enables early identification of operational conditions associated with reduced sorting accuracy, material loss, and processing inefficiencies. By translating sensor-derived data into actionable operational risk indicators, the system supports timely interventions that improve sorting precision, increase material recovery rates, and reduce contamination levels, thereby enhancing overall recycling efficiency.

Conclusions
Although conceptual and requiring empirical validation, the framework demonstrates how human-centered sensing can be integrated into recycling operations to improve decision-making and material recovery performance. By linking operational conditions directly to recycling outcomes, the approach supports reduced waste, improved sorting efficiency, and strengthened circular economy performance.