@article{Miawarni_Eko Setijadi_Tri Arief Sardjono_Wijayanti_Mauridhi Hery Purnomo_2022, title={Towards Improvement of LSTM and SVM Approach for Multiclass Fall Detection System}, volume={10}, url={https://emitter.pens.ac.id/index.php/emitter/article/view/639}, DOI={10.24003/emitter.v10i1.639}, abstractNote={<p>Telemonitoring of human physiological data helps detect emergency occurrences for subsequent medical diagnosis in daily living environments. One of the fatal emergencies in falling incidents. The goal of this paper is to detect significant incidents such as falls. The fall detection system is essential for human body movement investigation for medical practitioners, researchers, and healthcare businesses. Accelerometers have been presented as a practical, low-cost, and dependable approach for detecting and predicting outpatient movements in the user. The accurate detection of body movements based on accelerometer data enables the creation of more dependable systems for incorporating long-term development in physiological remarks. This research describes an accelerometer-based platform for detecting users’ body movement when they fall. The ADXL345, MMA8451q, and ITG3200 body sensors capture activity data, subsequently classified into 15 fall incident classes based on SisFall dataset. Falling incidents classification is performed using Long Short-Term Memory results in best AUC-ROC value of 97.7% and best calculation time of 6.16 seconds. Meanwhile, Support Vector Machines results in the best AUC-ROC value of 98.5% and best calculation times of 17.05 seconds.</p&gt;}, number={1}, journal={EMITTER International Journal of Engineering Technology}, author={Miawarni, Herti and Eko Setijadi and Tri Arief Sardjono and Wijayanti and Mauridhi Hery Purnomo}, year={2022}, month={Apr.}, pages={31-46} }