# Early Mobility Recognition for Intensive Care Unit Patients Using Accelerometers

arXiv: https://arxiv.org/abs/2106.15017 · HTML: https://mlsanity.com/paper/2106.15017/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 28 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [Online Human Activity Recognition using Low-Power Wearable Devices](https://mlsanity.com/paper/1808.08615/) (2018)
- [BWCNN: Blink to Word, a Real-Time Convolutional Neural Network Approach](https://mlsanity.com/paper/2006.01232/) (2020)
- [Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning](https://mlsanity.com/paper/1904.10829/) (2019)

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

- [MU-MAE: Multimodal Masked Autoencoders-Based One-Shot Learning](https://mlsanity.com/paper/2408.04243/) (2024)

## Abstract (arXiv)

With the development of the Internet of Things(IoT) and Artificial Intelligence(AI) technologies, human activity recognition has enabled various applications, such as smart homes and assisted living. In this paper, we target a new healthcare application of human activity recognition, early mobility recognition for Intensive Care Unit(ICU) patients. Early mobility is essential for ICU patients who suffer from long-time immobilization. Our system includes accelerometer-based data collection from ICU patients and an AI model to recognize patients' early mobility. To improve the model accuracy and stability, we identify features that are insensitive to sensor orientations and propose a segment voting process that leverages a majority voting strategy to recognize each segment's activity. Our results show that our system improves model accuracy from 77.78% to 81.86% and reduces the model instability (standard deviation) from 16.69% to 6.92%, compared to the same AI model without our feature engineering and segment voting process.