  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.
