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- W4360604879 abstract "Poor sitting posture can lead to a variety of serious diseases raging from spinal disorders to psychological stress. This paper aims to design a sitting posture monitoring system that detects improper postures and notifies the user in real time through a mobile application. The system leverages the use of low-cost EMG sensors, and relies on energy-efficient communication via Bluetooth Low energy (BLE). To ensure bad posture detection, different machine learning algorithms are tested and compared, namely support vector machine (SVM), K-nearest neighbours (KNN), decision tree (DT), random forest (RF), and multi-layer perception (MLP). We formulated the problem as a binary classification (good vs. bad posture) and multi-class classification (good, tilted to the front, right and left). The results of the training performed on a real dataset showed that KNN have the best accuracy (91% accuracy) and execution time (0.0066 ms)." @default.
- W4360604879 created "2023-03-24" @default.
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- W4360604879 date "2023-02-20" @default.
- W4360604879 modified "2023-09-25" @default.
- W4360604879 title "Bad Sitting Posture Detection and Alerting System using EMG Sensors and Machine Learning" @default.
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- W4360604879 doi "https://doi.org/10.1109/icaiic57133.2023.10067076" @default.
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