# Memory-Efficient, Limb Position-Aware Hand Gesture Recognition using Hyperdimensional Computing

arXiv: https://arxiv.org/abs/2103.05267 · HTML: https://mlsanity.com/paper/2103.05267/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 9 Mar 2021.

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

- [Superposition of many models into one](https://mlsanity.com/paper/1902.05522/) (2019)
- [An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier](https://mlsanity.com/paper/1802.10237/) (2018)
- [A review of learning vector quantization classifiers](https://mlsanity.com/paper/1509.07093/) (2015)
- [Task-Projected Hyperdimensional Computing for Multi-Task Learning](https://mlsanity.com/paper/2004.14252/) (2020)

## Built on by (most cited first, 8 of 8)

- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges](https://mlsanity.com/paper/2112.15424/) (2021)
- [Tiny Machine Learning: Progress and Futures](https://mlsanity.com/paper/2403.19076/) (2024)
- [Machine Learning for Microcontroller-Class Hardware: A Review](https://mlsanity.com/paper/2205.14550/) (2022)
- [PhiNets: a scalable backbone for low-power AI at the edge](https://mlsanity.com/paper/2110.00337/) (2021)
- [An Encoding Framework for Binarized Images using HyperDimensional Computing](https://mlsanity.com/paper/2312.00454/) (2023)
- [Incremental Online Learning Algorithms Comparison for Gesture and Visual Smart Sensors](https://mlsanity.com/paper/2209.00591/) (2022)
- [Training a HyperDimensional Computing Classifier using a Threshold on its Confidence](https://mlsanity.com/paper/2305.19007/) (2023)
- [Computing with Hypervectors for Efficient Speaker Identification](https://mlsanity.com/paper/2208.13285/) (2022)

## Abstract (arXiv)

Electromyogram (EMG) pattern recognition can be used to classify hand gestures and movements for human-machine interface and prosthetics applications, but it often faces reliability issues resulting from limb position change. One method to address this is dual-stage classification, in which the limb position is first determined using additional sensors to select between multiple position-specific gesture classifiers. While improving performance, this also increases model complexity and memory footprint, making a dual-stage classifier difficult to implement in a wearable device with limited resources. In this paper, we present sensor fusion of accelerometer and EMG signals using a hyperdimensional computing model to emulate dual-stage classification in a memory-efficient way. We demonstrate two methods of encoding accelerometer features to act as keys for retrieval of position-specific parameters from multiple models stored in superposition. Through validation on a dataset of 13 gestures in 8 limb positions, we obtain a classification accuracy of up to 93.34%, an improvement of 17.79% over using a model trained solely on EMG. We achieve this while only marginally increasing memory footprint over a single limb position model, requiring 8× less memory than a traditional dual-stage classification architecture.