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Smartphone-based Gait Authentication for Health Insurance Incentives: A Data-Driven Approach to Verify Walking Compliance

by Sandip Dutta, Soumen Roy, Purba Banerjee, Utpal Roy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 46
Year of Publication: 2024
Authors: Sandip Dutta, Soumen Roy, Purba Banerjee, Utpal Roy
10.5120/ijca2024924109

Sandip Dutta, Soumen Roy, Purba Banerjee, Utpal Roy . Smartphone-based Gait Authentication for Health Insurance Incentives: A Data-Driven Approach to Verify Walking Compliance. International Journal of Computer Applications. 186, 46 ( Nov 2024), 1-13. DOI=10.5120/ijca2024924109

@article{ 10.5120/ijca2024924109,
author = { Sandip Dutta, Soumen Roy, Purba Banerjee, Utpal Roy },
title = { Smartphone-based Gait Authentication for Health Insurance Incentives: A Data-Driven Approach to Verify Walking Compliance },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2024 },
volume = { 186 },
number = { 46 },
month = { Nov },
year = { 2024 },
issn = { 0975-8887 },
pages = { 1-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number46/smartphone-based-gait-authentication-for-health-insurance-incentives-a-data-driven-approach-to-verify-walking-compliance/ },
doi = { 10.5120/ijca2024924109 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-11-08T23:09:21+05:30
%A Sandip Dutta
%A Soumen Roy
%A Purba Banerjee
%A Utpal Roy
%T Smartphone-based Gait Authentication for Health Insurance Incentives: A Data-Driven Approach to Verify Walking Compliance
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 46
%P 1-13
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Some of few health insurance companies now offer lower premiums to people who walk 10,000 steps every day. The main problem in this plan is figuring out who qualifies. We suggest using data from smartphone sensors while people walk. This data can track steps and confirm who’s walking. This could help prevent misuse of the program. In response to the aforementioned conundrum, we have procured and meticulously analysed the gait patterns of 87 volunteers. Our investigation has determined that the Scale Manhattan an efficacious anomaly detector, suitable for individual verification in active mode, achieving an equal error rate from 10.20% to 13.76%. Our proposed detection system has undergone validation procedures employing datasets gathered across multiple sessions and repetitions. Based on a judiciously conducted realistic appraisal of the proposed model for each subject, we assert that this method of individual authentication holds merit for the aforementioned campaign, engendering tangible benefits for prospective beneficiaries. Beyond the immediate insurance incentives, this approach possesses the potential to motivate individuals not only to procure insurance coverage but also to foster their physical and psychological well-being. Our innovative methodology provides a substantive solution for health insurance companies contemplating the implementation of analogous promotional endeavours.

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Index Terms

Computer Science
Information Sciences
Biometrics
User authentication
Health insurance

Keywords

Human gait analysis Human computer interaction Gesture Action recognition Anomaly detection User authentication Scaled manhattan

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