TY - GEN
T1 - IWaste
T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
AU - Chen, Junbo
AU - Mao, Jeffrey
AU - Thiel, Cassandra
AU - Wang, Yao
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Waste auditing is important for effectively reducing the medical waste generated by resource-intensive operating rooms. To replace the current time-intensive and dangerous manual waste auditing method, we propose a system named iWASTE to detect and classify medical waste based on videos recorded by a camera-equipped waste container. In this pilot study, we collected a video dataset of 4 waste items (gloves, hairnet, mask, and shoecover) and designed a motion detection based preprocessing method to extract and trim useful frames. We propose a novel architecture named R3D+C2D to classify waste videos by combining features learnt by 2D convolutional and 3D convolutional neural networks. The proposed method obtained a promising result (79.99% accuracy) on our challenging dataset.Clinical Relevance - iWaste enables consistent and effective real-time monitoring of solid waste generation in operating rooms, which can be used to enforce medical waste sorting policies and to identify waste reduction strategies.
AB - Waste auditing is important for effectively reducing the medical waste generated by resource-intensive operating rooms. To replace the current time-intensive and dangerous manual waste auditing method, we propose a system named iWASTE to detect and classify medical waste based on videos recorded by a camera-equipped waste container. In this pilot study, we collected a video dataset of 4 waste items (gloves, hairnet, mask, and shoecover) and designed a motion detection based preprocessing method to extract and trim useful frames. We propose a novel architecture named R3D+C2D to classify waste videos by combining features learnt by 2D convolutional and 3D convolutional neural networks. The proposed method obtained a promising result (79.99% accuracy) on our challenging dataset.Clinical Relevance - iWaste enables consistent and effective real-time monitoring of solid waste generation in operating rooms, which can be used to enforce medical waste sorting policies and to identify waste reduction strategies.
UR - http://www.scopus.com/inward/record.url?scp=85091005743&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091005743&partnerID=8YFLogxK
U2 - 10.1109/EMBC44109.2020.9175645
DO - 10.1109/EMBC44109.2020.9175645
M3 - Conference contribution
AN - SCOPUS:85091005743
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 5794
EP - 5797
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 July 2020 through 24 July 2020
ER -