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

TL;DR

This paper introduces a low-power, accurate fall detection method using knowledge distillation-enhanced LSTM models optimized for offline embedded and low-power devices, focusing on sensor data analysis.

Contribution

It presents a novel application of knowledge distillation to improve LSTM-based fall detection models for energy-efficient, real-time performance on resource-constrained devices.

Findings

Enhanced accuracy through knowledge distillation

Reduced power consumption of fall detection models

Effective real-time detection on embedded devices

Abstract

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.