# MoRSE: Deep Learning-based Arm Gesture Recognition for Search and Rescue Operations

arXiv: https://arxiv.org/abs/2210.08307 · HTML: https://mlsanity.com/paper/2210.08307/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 15 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable Devices](https://mlsanity.com/paper/2203.14907/) (2022)
- [PerceptionNet: A Deep Convolutional Neural Network for Late Sensor Fusion](https://mlsanity.com/paper/1811.00170/) (2018)
- [Driver State and Behavior Detection Through Smart Wearables](https://mlsanity.com/paper/2104.13889/) (2021)
- [Towards a Practical Pedestrian Distraction Detection Framework using Wearables](https://mlsanity.com/paper/1710.03755/) (2017)

## Abstract (arXiv)

Efficient and quick remote communication in search and rescue operations can be life-saving for the first responders. However, while operating on the field means of communication based on text, image and audio are not suitable for several disaster scenarios. In this paper, we present a smartwatch-based application, which utilizes a Deep Learning (DL) model, to recognize a set of predefined arm gestures, maps them into Morse code via vibrations enabling remote communication amongst first responders. The model performance was evaluated by training it using 4,200 gestures performed by 7 subjects (cross-validation) wearing a smartwatch on their dominant arm. Our DL model relies on convolutional pooling and surpasses the performance of existing DL approaches and common machine learning classifiers, obtaining gesture recognition accuracy above 95%. We conclude by discussing the results and providing future directions.