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This paper presents a cost-effective, low-power approach to unintentionalfall detection using knowledge distillation-based LSTM (Long Short-Term Memory)models to significantly improve accuracy. With a primary focus on analyzingtime-series data collected from various sensors, the solution offers real-timedetection capabilities, ensuring prompt and reliable identification of falls.The authors investigate fall detection models that are based on differentsensors, comparing their accuracy rates and performance. Furthermore, theyemploy the technique of knowledge distillation to enhance the models'precision, resulting in refined accurate configurations that consume lowerpower. As a result, this proposed solution presents a compelling avenue for thedevelopment of energy-efficient fall detection systems for future advancementsin this critical domain.

Fall Detection using Knowledge Distillation Based Long short-term memory for Offline Embedded and Low Power Devices