TinyML Anomaly Detection in Portable Cutting Tools
Parisa Esmaili, Federico Cavedo, Parvaneh Esmaılı, Michele Norgia · 2023
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 K-means 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.