Vision and Weight Sensor Fusion for an Automated Hospital Waste Sorting System: A Practical Approach to Safer Healthcare Waste Management

Sindhuja Vispute, Asmit Drolia, Aayush Doke, Rohit Taile and Ayesha Sayyad

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 5, 360-374

Abstract: Hospital waste management presents a critical chal- lenge in healthcare infrastructure worldwide, with improper segregation posing serious health and environmental risks. This research addresses the limitations of manual sorting and ex- isting automated systems by developing an intelligent waste segregation bin that combines computer vision with weight sensing. Our system utilizes a convolutional neural network (CNN) for visual classification of common hospital waste into four categories—plastic, glass, metal, and paper—while simulta- neously employing load cells for weight measurement. A novel fusion algorithm intelligently combines these complementary data streams, achieving a remarkable classification accuracy of 94.6%, significantly outperforming vision-only (88.2%) and weight-only (72.4%) approaches. The practical implementation includes a mechanical sorting mechanism and an IoT dashboard for real- time monitoring. Priced at approximately Rs.45,000 , our solution offers hospitals an affordable, reliable, and scalable alternative to error-prone manual sorting, potentially transforming waste management practices in healthcare facilities of varying sizes and resources.

Keywords: Hospital Waste Management; Deep Learning; Convolutional Neural Networks; Sensor Fusion; Internet of Things; Automated Sorting; Healthcare Safety (search for similar items in EconPapers)
Date: 2025
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DOI: 10.32628/IJSRST25126246

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