# Unimodal and Multimodal Sensor Fusion for Wearable Activity Recognition

arXiv: https://arxiv.org/abs/2404.16005 · HTML: https://mlsanity.com/paper/2404.16005/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 24 Apr 2024.

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

- [CaptAinGlove: Capacitive and Inertial Fusion-Based Glove for Real-Time on Edge Hand Gesture Recognition for Drone Control](https://mlsanity.com/paper/2306.04319/) (2023)
- [InMyFace: Inertial and Mechanomyography-Based Sensor Fusion for Wearable Facial Activity Recognition](https://mlsanity.com/paper/2302.04024/) (2023)
- [MeciFace: Mechanomyography and Inertial Fusion-based Glasses for Edge Real-Time Recognition of Facial and Eating Activities](https://mlsanity.com/paper/2306.13674/) (2023)

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

- [OpenMarcie: Dataset for Multimodal Action Recognition in Industrial Environments](https://mlsanity.com/paper/2603.02390/) (2026)

## Abstract (arXiv)

Combining different sensing modalities with multiple positions helps form a unified perception and understanding of complex situations such as human behavior. Hence, human activity recognition (HAR) benefits from combining redundant and complementary information (Unimodal/Multimodal). Even so, it is not an easy task. It requires a multidisciplinary approach, including expertise in sensor technologies, signal processing, data fusion algorithms, and domain-specific knowledge. This Ph.D. work employs sensing modalities such as inertial, pressure (audio and atmospheric pressure), and textile capacitive sensing for HAR. The scenarios explored are gesture and hand position tracking, facial and head pattern recognition, and body posture and gesture recognition. The selected wearable devices and sensing modalities are fully integrated with machine learning-based algorithms, some of which are implemented in the embedded device, on the edge, and tested in real-time.