# Physics Sensor Based Deep Learning Fall Detection System

arXiv: https://arxiv.org/abs/2403.06994 · HTML: https://mlsanity.com/paper/2403.06994/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications](https://mlsanity.com/paper/1804.09028/) (2018)
- [A Markov Chain Model for Identifying Changes in Daily Activity Patterns of People Living with Dementia](https://mlsanity.com/paper/2307.11126/) (2023)

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

- [Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data](https://mlsanity.com/paper/2607.25710/) (2026)

## Abstract (arXiv)

Fall detection based on embedded sensor is a practical and popular research direction in recent years. In terms of a specific application: fall detection methods based upon physics sensors such as [gyroscope and accelerator] have been exploited using traditional hand crafted features and feed them in machine learning models like Markov chain or just threshold based classification methods. In this paper, we build a complete system named TSFallDetect including data receiving device based on embedded sensor, mobile deep-learning model deploying platform, and a simple server, which will be used to gather models and data for future expansion. On the other hand, we exploit the sequential deep-learning methods to address this falling motion prediction problem based on data collected by inertial and film pressure sensors. We make a empirical study based on existing datasets and our datasets collected from our system separately, which shows that the deep-learning model has more potential advantage than other traditional methods, and we proposed a new deep-learning model based on the time series data to predict the fall, and it may be superior to other sequential models in this particular field.