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

Author

Listed:
  • Roberto De Fazio

    (Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
    Facultad de Ingeniería, Universidad Panamericana, Aguascalientes 20290, Mexico)

  • Vincenzo Mariano Mastronardi

    (Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
    Center for Biomolecular Nanotechnologies, Italian Technology Institute IIT, 73010 Arnesano, Italy)

  • Matteo Petruzzi

    (Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy)

  • Massimo De Vittorio

    (Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
    Center for Biomolecular Nanotechnologies, Italian Technology Institute IIT, 73010 Arnesano, Italy)

  • Paolo Visconti

    (Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
    Center for Biomolecular Nanotechnologies, Italian Technology Institute IIT, 73010 Arnesano, Italy)

Abstract

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.

Suggested Citation

  • Roberto De Fazio & Vincenzo Mariano Mastronardi & Matteo Petruzzi & Massimo De Vittorio & Paolo Visconti, 2022. "Human–Machine Interaction through Advanced Haptic Sensors: A Piezoelectric Sensory Glove with Edge Machine Learning for Gesture and Object Recognition," Future Internet, MDPI, vol. 15(1), pages 1-42, December.
  • Handle: RePEc:gam:jftint:v:15:y:2022:i:1:p:14-:d:1016903
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    References listed on IDEAS

    as
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    2. Vito Cacucciolo & Jun Shintake & Yu Kuwajima & Shingo Maeda & Dario Floreano & Herbert Shea, 2019. "Stretchable pumps for soft machines," Nature, Nature, vol. 572(7770), pages 516-519, August.
    3. Bernardo Calabrese & Ramiro Velázquez & Carolina Del-Valle-Soto & Roberto de Fazio & Nicola Ivan Giannoccaro & Paolo Visconti, 2020. "Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired," Energies, MDPI, vol. 13(22), pages 1-30, November.
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