# BIOWISH: Biometric Recognition using Wearable Inertial Sensors detecting Heart Activity

arXiv: https://arxiv.org/abs/2210.09843 · HTML: https://mlsanity.com/paper/2210.09843/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 75 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Evaluation of PPG Biometrics for Authentication in different states](https://mlsanity.com/paper/1712.08583/) (2017)
- [Summary: Multi-modal Biometric-based Implicit Authentication of Wearable Device Users](https://mlsanity.com/paper/1907.06563/) (2019)
- [Ballistocardiogram-based Authentication using Convolutional Neural Networks](https://mlsanity.com/paper/1807.03216/) (2018)
- [Heartbeats in the Wild: A Field Study Exploring ECG Biometrics in Everyday Life](https://mlsanity.com/paper/2003.03273/) (2020)

## Abstract (arXiv)

Wearable devices are increasingly used, thanks to the wide set of applications that can be deployed exploiting their ability to monitor physical activity and health-related parameters. Their usage has been recently proposed to perform biometric recognition, leveraging on the uniqueness of the recorded traits to generate discriminative identifiers. Most of the studies conducted on this topic have considered signals derived from cardiac activity, detecting it mainly using electrical measurements thorugh electrocardiography, or optical recordings employing photoplethysmography. In this paper we instead propose a BIOmetric recognition approach using Wearable Inertial Sensors detecting Heart activity (BIOWISH). In more detail, we investigate the feasibility of exploiting mechanical measurements obtained through seismocardiography and gyrocardiography to recognize a person. Several feature extractors and classifiers, including deep learning techniques relying on transfer learning and siamese training, are employed to derive distinctive characteristics from the considered signals, and differentiate between legitimate and impostor subjects. An multi-session database, comprising acquisitions taken from subjects performing different activities, is employed to perform experimental tests simulating a verification system. The obtained results testify that identifiers derived from measurements of chest vibrations, collected by wearable inertial sensors, could be employed to guarantee high recognition performance, even when considering short-time recordings.