TrustIoT: Building Trust in Human-Thing Interactions for Healthcare Systems Using Machine Learning

Al-Ansari, Abir, D'Aout, Kristiaan ORCID: 0000-0002-6043-7744 and Selis, Valerio ORCID: 0000-0002-1856-4707 (2025) TrustIoT: Building Trust in Human-Thing Interactions for Healthcare Systems Using Machine Learning In: 2025 IEEE 24th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2025-11-14 - 2025-11-17.

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Abstract

Internet of Things (IoT) health monitoring devices allow the collection of data while performing various daily physical activities. These devices are not only affordable but also capable of collecting a wide variety of data, such as physiological data and environmental information. In the case of IoT healthcare systems, it is important to know which activity is performed to determine if there is any abnormality in a person's physical behaviour during the activity, which may ultimately provide an indication of the person's health condition. Human Activity Recognition (HAR) methods can be used to recognise activities. HAR is a field of identifying and categorising activities using Artificial Intelligence (AI), which uses raw data collected from different devices equipped with sensors such as cameras, accelerometers, gyroscopes, etc. The unique patterns and characteristics exhibited in human physical behaviours, such as gait, enable the identification of individuals with the use of a technology known as behavioural biometrics. This paper presents and evaluates HAR and user detection models, which serve as the foundation for constructing a trust model. The HAR binary classification proposed method achieved an accuracy of 97.5%, a precision of 97.7%, a recall of 97.5% and an F1 score of 97.6%. Additionally, the user detection model achieved an accuracy and a recall of 82.1% a precision of 91.6% and an F1 score of 85.7%. We then detail the methodology for calculating and updating trust scores over time and defining trust levels in the IoT system. Once trust is established, the system can provide reliable information regarding the individual's health condition.

Item Type:	Conference Item (Unspecified)
Uncontrolled Keywords:	IoT trust, human-to-thing interactions, healthcaresystem, physical behaviour, human activity recognition, user detection
Divisions:	Faculty of Science & Engineering Faculty of Science & Engineering > School of Computer Science & Informatics Faculty of Science & Engineering > School of Computer Science & Informatics > Trustworthy Computing
Depositing User:	Symplectic Admin
Date Deposited:	09 Feb 2026 10:06
Last Modified:	20 Jul 2026 14:53
DOI:	10.1109/Trustcom66490.2025.00378
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3196919
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