# Online Human Gesture Recognition using Recurrent Neural Networks and Wearable Sensors

arXiv: https://arxiv.org/abs/1810.11297 · HTML: https://mlsanity.com/paper/1810.11297/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2018.

## Built on by (most cited first, 3 of 3)

- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [Human Activity Recognition Models in Ontology Networks](https://mlsanity.com/paper/2105.02264/) (2021)
- [WaveGlove: Transformer-based hand gesture recognition using multiple inertial sensors](https://mlsanity.com/paper/2105.01753/) (2021)

## Abstract (arXiv)

Gestures are a natural communication modality for humans. The ability to interpret gestures is fundamental for robots aiming to naturally interact with humans. Wearable sensors are promising to monitor human activity, in particular the usage of triaxial accelerometers for gesture recognition have been explored. Despite this, the state of the art presents lack of systems for reliable online gesture recognition using accelerometer data. The article proposes SLOTH, an architecture for online gesture recognition, based on a wearable triaxial accelerometer, a Recurrent Neural Network (RNN) probabilistic classifier and a procedure for continuous gesture detection, relying on modelling gesture probabilities, that guarantees (i) good recognition results in terms of precision and recall, (ii) immediate system reactivity.