Fall Detection using Knowledge Distillation Based Long short-term memory for Offline Embedded and Low Power Devices
This work addresses fall detection for elderly or at-risk individuals using offline embedded devices, but it appears incremental as it builds on existing LSTM and knowledge distillation methods.
The paper tackled fall detection by developing a knowledge distillation-based LSTM model to improve accuracy and reduce power consumption, achieving refined configurations for energy-efficient systems.
This paper presents a cost-effective, low-power approach to unintentional fall detection using knowledge distillation-based LSTM (Long Short-Term Memory) models to significantly improve accuracy. With a primary focus on analyzing time-series data collected from various sensors, the solution offers real-time detection capabilities, ensuring prompt and reliable identification of falls. The authors investigate fall detection models that are based on different sensors, comparing their accuracy rates and performance. Furthermore, they employ the technique of knowledge distillation to enhance the models' precision, resulting in refined accurate configurations that consume lower power. As a result, this proposed solution presents a compelling avenue for the development of energy-efficient fall detection systems for future advancements in this critical domain.