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

arXiv: https://arxiv.org/abs/2308.12481 · HTML: https://mlsanity.com/paper/2308.12481/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 24 Aug 2023.

## Builds on (most cited first, 2 of 2)

- [TinyBERT: Distilling BERT for Natural Language Understanding](https://mlsanity.com/paper/1909.10351/) (2019)
- [On the Efficacy of Knowledge Distillation](https://mlsanity.com/paper/1910.01348/) (2019)

## Built on by (most cited first, 1 of 1)

- [AI-Generated Fall Data: Assessing LLMs and Diffusion Model for Wearable Fall Detection](https://mlsanity.com/paper/2505.04660/) (2025)

## Abstract (arXiv)

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.