Fall Detection using Knowledge Distillation Based Long short-term memory
  for Offline Embedded and Low Power Devices
Hannah Zhou Allison Chen Celine Buer Emily Chen Kayleen Tang Lauryn Gong Zhiqi Liu Jianbin Tang
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.