Through electrical impedance measurement between the bar of the cutting tool and the operator’s body, an active impedance protection system can discriminate between the body proximity while cutting a tree. However, the performance of simple threshold-based systems can be affected by the unpredictability of the working environment in addition to the complexity of cutting tasks. This paper proposes and evaluates a protection system for portable cutting tools based on end-to-end anomaly detection using TinyML paradigm. The model is based on the fully connected neural network and Kmeans clustering algorithm, which is developed on the ESP32- DevKitC-V4. Considering anomaly rank while approaching normal or hazardous situations, the proposed system shows an improvement in detecting different states enabling real-time decision-making through a compact, low-cost, and low-power consumption solution.

TinyML Anomaly Detection in Portable Cutting Tools

Esmaili, Parisa;Cavedo, Federico;Norgia, Michele
2023-01-01

Abstract

Through electrical impedance measurement between the bar of the cutting tool and the operator’s body, an active impedance protection system can discriminate between the body proximity while cutting a tree. However, the performance of simple threshold-based systems can be affected by the unpredictability of the working environment in addition to the complexity of cutting tasks. This paper proposes and evaluates a protection system for portable cutting tools based on end-to-end anomaly detection using TinyML paradigm. The model is based on the fully connected neural network and Kmeans clustering algorithm, which is developed on the ESP32- DevKitC-V4. Considering anomaly rank while approaching normal or hazardous situations, the proposed system shows an improvement in detecting different states enabling real-time decision-making through a compact, low-cost, and low-power consumption solution.
2023
Conference: 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)
979-8-3503-0080-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1260118
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