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crossrefFuture Internet2022-12-27Cited by 52

Human–Machine Interaction through Advanced Haptic Sensors: A Piezoelectric Sensory Glove with Edge Machine Learning for Gesture and Object Recognition

Roberto De Fazio, Vincenzo Mastronardi, Matteo Petruzzi, Massimo De Vittorio, Paolo Visconti

Human–machine interaction (HMI) refers to systems enabling communication between machines and humans. Systems for human–machine interfaces have advanced significantly in terms of materials, device design, and production methods. Energy supply units, logic circuits, sensors, and data storage units must be flexible, stretchable, undetectable, biocompatible, and self-healing to act as human–machine interfaces. This paper discusses the technologies for providing different haptic feedback of different natures. Notably, the physiological mechanisms behind touch perception are reported, along with a classification of the main haptic interfaces. Afterward, a comprehensive overview of wearable haptic interfaces is presented, comparing them in terms of cost, the number of integrated actuators and sensors, their main haptic feedback typology, and their future application. Additionally, a review of sensing systems that use haptic feedback technologies—specifically, smart gloves—is given by going through their fundamental technological specifications and key design requirements. Furthermore, useful insights related to the design of the next-generation HMI devices are reported. Lastly, a novel smart glove based on thin and conformable AlN (aluminum nitride) piezoelectric sensors is demonstrated. Specifically, the device acquires and processes the signal from the piezo sensors to classify performed gestures through an onboard machine learning (ML) algorithm. Then, the design and testing of the electronic conditioning section of AlN-based sensors integrated into the smart glove are shown. Finally, the architecture of a wearable visual-tactile recognition system is presented, combining visual data acquired by a micro-camera mounted on the user’s glass with the haptic ones provided by the piezoelectric sensors.

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crossrefFuture Internet2025-06-20Cited by 2

Fortified-Edge 2.0: Advanced Machine-Learning-Driven Framework for Secure PUF-Based Authentication in Collaborative Edge Computing

Seema G. Aarella, Venkata P. Yanambaka, Saraju P. Mohanty, Elias Kougianos

This research introduces Fortified-Edge 2.0, a novel authentication framework that addresses critical security and privacy challenges in Physically Unclonable Function (PUF)-based systems for collaborative edge computing (CEC). Unlike conventional methods that transmit full binar…

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crossrefFuture Internet2025-09-11Cited by 17

Intelligent Edge Computing and Machine Learning: A Survey of Optimization and Applications

Sebastián A. Cajas Ordóñez, Jaydeep Samanta, Andrés L. Suárez-Cetrulo, Ricardo Simón Carbajo

Intelligent edge machine learning has emerged as a paradigm for deploying smart applications across resource-constrained devices in next-generation network infrastructures. This survey addresses the critical challenges of implementing machine learning models on edge devices withi…

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crossrefFuture Internet2024-12-01Cited by 15

SIGNIFY: Leveraging Machine Learning and Gesture Recognition for Sign Language Teaching Through a Serious Game

Luca Ulrich, Giulio Carmassi, Paolo Garelli, Gianluca Lo Presti, Gioele Ramondetti, Giorgia Marullo, et al.

Italian Sign Language (LIS) is the primary form of communication for many members of the Italian deaf community. Despite being recognized as a fully fledged language with its own grammar and syntax, LIS still faces challenges in gaining widespread recognition and integration into…

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crossrefFuture Internet2025-02-12Cited by 6

Secure Tiny Machine Learning on Edge Devices: A Lightweight Dual Attestation Mechanism for Machine Learning

Vlad-Eusebiu Baciu, An Braeken, Laurent Segers, Bruno da Silva

Emerging edge devices are transforming the Internet of Things (IoT) by enabling more responsive and efficient interactions between physical objects and digital networks. These devices support diverse applications, from health-monitoring wearables to environmental sensors, by movi…

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crossrefFuture Internet2025-09-08Cited by 2

Detection and Mitigation in IoT Ecosystems Using oneM2M Architecture and Edge-Based Machine Learning

Yu-Yong Luo, Yu-Hsun Chiu, Chia-Hsin Cheng

Distributed denial-of-service (DDoS) attacks are a prevalent threat to resource-constrained IoT deployments. We present an edge-based detection and mitigation system integrated with the oneM2M architecture. By using a Raspberry Pi 4 client and five Raspberry Pi 3 attack nodes in…

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crossrefFuture Internet2023-05-30Cited by 74

Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review

Shereen Ismail, Diana W. Dawoud, Hassan Reza

As an Internet of Things (IoT) technological key enabler, Wireless Sensor Networks (WSNs) are prone to different kinds of cyberattacks. WSNs have unique characteristics, and have several limitations which complicate the design of effective attack prevention and detection techniqu…

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