A transformer-based model for effective and exportable IoMT-based stress detection

Security Threats and Promising Solutions Arising from the Intersection of AI and IoT: A Study of IoMT and IoET Applications

“…The first is presented in Section 2.1, ETD, which falls under the IoET paradigm and uses connected smart meters as the embodiment of IoET devices [2]. In Section 2.2, we present the second application within the ambit of IoMT, which is focused on remote intelligent stress monitoring and uses connected sensors as IoMT devices to measure physiological signals [1].…”

“…The HIW is shaped by IoT data and empowered by artificial intelligence (AI); it is essential for creating data-driven actionable insights. In this work, we closely examine two of such systems: energy theft detection enabled by the Internet of Energy Things (IoET) and remote stress detection enabled by the Internet of Medical Things (IoMT) [1,2]. IoET-based systems are key in the transition toward clean energy; however, the penetration rate of smart meters (basic IoET devices) is very slow (e.g., only 57% in 2023 in the UK (https://assets.publishing.service.gov.uk/media/ 646f7ff47dd6e70012a9b3f6/Q1_2023_Smart_Meters_Statistics_Report.pdf, accessed on 28 December 2023)).…”

“…In this context, this work presents one of the first FL-based AI methods for energy theft detection based on IoET-enabled energy consumption data. The third work investigated differential privacy, an alternative effective countermeasure against MITM attacks, to unlock the potential of IoMT-based tress detection reported in [1]. Differential privacy is a mechanism in which signals carrying private data are contaminated with additive noise to alleviate the risk of data leakage in the event of an attack.…”

IoT and machine learning for enabling sustainable development goals

“…In this context, Intelligent IoT for pervasive mental health support falls within the ambit of the Data and Digital Health efforts and promises to bridge the gap by reaching those that have been failed by existing mental health services. Intelligent stress monitoring assistant is examined in (Alshareef et al, 2022) in which affective computing is employed to detect stress levels based on biological signals. The Detecting stress is a problem of critical importance when threequarters of people in our society feel unable to cope due to stress (Foundation, 2018).…”

“…The use of HIW in healthcare raises concerns regarding the security of sensitive patient data, which when transferred and stored could be exposed to unauthorised access and misuse. Furthermore, training a model on sensitive data, such as (Alshareef et al, 2022), involves significant privacy risks. In this work, multiple sensors are employed including electrocardiogram (ECG), electrodermal activity (EDA), and electromyogram (EMG).…”