# Opportunistic Multi-Modal User Authentication for Health-Tracking IoT Wearables

arXiv: https://arxiv.org/abs/2109.13705 · HTML: https://mlsanity.com/paper/2109.13705/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [Evaluation of PPG Biometrics for Authentication in different states](https://mlsanity.com/paper/1712.08583/) (2017)
- [Adherence to Personal Health Devices: A Case Study in Diabetes Management](https://mlsanity.com/paper/2006.04947/) (2020)
- [Continuous Authentication of Wearable Device Users from Heart Rate, Gait, and Breathing Data](https://mlsanity.com/paper/2008.10779/) (2020)
- [Biometric-Based Wearable User Authentication During Sedentary and Non-sedentary Periods](https://mlsanity.com/paper/1811.07060/) (2018)
- [Summary: Multi-modal Biometric-based Implicit Authentication of Wearable Device Users](https://mlsanity.com/paper/1907.06563/) (2019)
- [Context-Dependent Implicit Authentication for Wearable Device User](https://mlsanity.com/paper/2008.12145/) (2020)
- [Understanding College Students' Phone Call Behaviors Towards a Sustainable Mobile Health and Wellbeing Solution](https://mlsanity.com/paper/2011.06007/) (2020)

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

- [Transfer Learning to Detect COVID-19 Coughs with Incremental Addition of Patient Coughs to Healthy People's Cough Detection Models](https://mlsanity.com/paper/2311.06707/) (2023)
- [Are Neuro-Inspired Multi-Modal Vision-Language Models Resilient to Membership Inference Privacy Leakage?](https://mlsanity.com/paper/2511.20710/) (2025)
- [A Comprehensive Survey of Challenges and Opportunities of Few-Shot Learning Across Multiple Domains](https://mlsanity.com/paper/2504.04017/) (2025)

## Abstract (arXiv)

With the advancement of technologies, market wearables are becoming increasingly popular with a range of services, including providing access to bank accounts, accessing cars, monitoring patients remotely, among several others. However, often these wearables collect various sensitive personal information of a user with no to limited authentication, e.g., knowledge-based external authentication techniques, such as PINs. While most of these external authentication techniques suffer from multiple limitations, including recall burden, human errors, or biases, researchers have started using various physiological and behavioral data, such as gait and heart rate, collected by the wearables to authenticate a wearable user implicitly with a limited accuracy due to sensing and computing constraints of wearables. In this work, we explore the usefulness of blood oxygen saturation SpO2 values collected from the Oximeter device to distinguish a user from others. From a cohort of 25 subjects, we find that 92% of the cases SpO2 can distinguish pairs of users. From detailed modeling and performance analysis, we observe that while SpO2 alone can obtain an average accuracy of 0.69 and F1 score of 0.69, the addition of heart rate (HR) can improve the average identification accuracy by 15% and F1 score by 13%. These results show promise in using SpO2 along with other biometrics to develop implicit continuous authentications for wearables.