Recall-driven Precision Refinement: Unveiling Accurate Fall Detection using LSTM

TL;DR

This paper introduces a fall detection system using LSTM networks with pruning techniques, achieving high recall and real-time performance on Raspberry Pi to improve elderly safety.

Contribution

It presents a novel fall detection approach combining LSTM with pruning for optimized accuracy and real-time deployment on embedded hardware.

Findings

High recall rate in fall detection

Specificity maintained at 96%

Real-time implementation on Raspberry Pi

Abstract

This paper presents an innovative approach to address the pressing concern of fall incidents among the elderly by developing an accurate fall detection system. Our proposed system combines state-of-the-art technologies, including accelerometer and gyroscope sensors, with deep learning models, specifically Long Short-Term Memory (LSTM) networks. Real-time execution capabilities are achieved through the integration of Raspberry Pi hardware. We introduce pruning techniques that strategically fine-tune the LSTM model's architecture and parameters to optimize the system's performance. We prioritize recall over precision, aiming to accurately identify falls and minimize false negatives for timely intervention. Extensive experimentation and meticulous evaluation demonstrate remarkable performance metrics, emphasizing a high recall rate while maintaining a specificity of 96\%. Our research culminates in a state-of-the-art fall detection system that promptly sends notifications, ensuring vulnerable individuals receive timely assistance and improve their overall well-being. Applying LSTM models and incorporating pruning techniques represent a significant advancement in fall detection technology, offering an effective and reliable fall prevention and intervention solution.