Roadmap to Human–Machine Interaction through Triboelectric Nanogenerator and Machine Learning Convergence

Babu, Anand; Mandal, Dipankar

doi:10.1021/acsaem.3c02642

Cited by 4 publications

(1 citation statement)

References 121 publications

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“…Intelligent recognition, for example, usually refers to an ability based on AI technology that enables a system to understand, learn, and recognize a variety of information or patterns and make decisions accordingly. By combining self-powered sensors with intelligent recognition and machine learning [114], systems are able to more intelligently understand and process information gathered from the environment and are widely used in a variety of applications, including face recognition, voice recognition, object detection, and physical gaming [115]. This combination has brought more efficient and accurate data processing and decision-making capabilities to many industries.…”

Section: Intelligent Recognitionmentioning

confidence: 99%

Recent advances in piezoelectric and triboelectric self-powered sensors for human–machine interface applications

Du,

Li,

Qiu

et al. 2024

J. Micromech. Microeng.

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The burgeoning internet of things (IoTs) and artificial intelligence (AI) technologies have prospered a variety of emerging applications. Human-machine interfaces (HMIs), for instance, enables users with intuitive, efficient, and friendly way to interact with machines, capable of instant information acquisition, processing, communication, and feedback, etc. These features require ultra-compact and high-performance transducers, and therefore self-powered sensors have become the key underlying technology for HMI applications. This review focuses on the piezoelectric, triboelectric, and hybrid self-powered sensors with particular attention to their microstructures and fabrication methods, showing that both traditional microfabrication and emerging fabrication methods like three-dimensional (3D) printing, electrospinning, and braiding have contributed to the planar, array, porous, fabric, and composite type self-powered sensors. Moreover, the integration method of piezoelectric and triboelectric sensor arrays (TSAs) is investigated. The crosstalk issue is highlighted, i.e. the signal interference between adjacent sensing units, and current solutions such as array design optimization, signal processing improvement, and material innovation to reduce crosstalk sensitivity have been reviewed through specific examples. Three categories of HMI applications have been outlined, including intelligent interaction, robotics, and human monitoring, with detailed explanations of how the self-powered sensors support these HMI applications. Through discussion of challenges and prospects, it is proposed that further coordinating the design and fabrication of micro devices with HMIs will potentially boost the intelligent application with even higher level of diversification, convenience, and interconnectivity.

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Section: Intelligent Recognitionmentioning

confidence: 99%

Recent advances in piezoelectric and triboelectric self-powered sensors for human–machine interface applications

Du,

Li,

Qiu

et al. 2024

J. Micromech. Microeng.

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Self‐Learning e‐Skin Respirometer for Pulmonary Disease Detection

Babu,

Kassahun,

Dufour

et al. 2024

Advanced Sensor Research

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Amid the landscape of respiratory health, lung disorders stand out as the primary contributors to pulmonary intricacies and respiratory diseases. Timely precautions through accurate diagnosis hold the key to mitigating their impact. Nevertheless, the existing conventional methods of lungs monitoring exhibit limitations due to bulky instruments, intrusive techniques, manual data recording, and discomfort in continuous measurements. In this context, an unintrusive organic wearable piezoelectric electronic‐skin respirometer (eSR) exhibiting a high‐sensitivity (385 mV N−1), precise conversion factor (12 mL mV−1), high signal‐to‐noise ratio (58 dB), and a low limit of detection down to 100 mL is demonstrated, which is perfectly suitable to record diverse breathing signals. To empower the eSR with early diagnosis functionality, self‐learning capability is further added by integrating the respirometer with the machine learning algorithms. Among various tested algorithms, gradient boosting regression emerges as the most suitable, leveraging sequential model refinement to achieve an accuracy exceeding 95% in detection of chronic obstructive pulmonary diseases (COPD). From conception to validation, the approach not only provides an alternative pathway for tracking the progression of lung diseases but also has the capability to replace the conventional techniques, with the conformable AI‐empowered respirometer.

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Flexible Organic Molecular Single Crystal-Based Triboelectric Device as a Self-Powered Tactile Sensor

Marandi,

Saini,

Arora

et al. 2024

J. Am. Chem. Soc.

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Triboelectric nanogenerators (TENGs) have proven to be effective at converting mechanical energy into electrical power, making them a viable technology for operating self-powered electronic devices used in medical diagnostics and environmental monitoring. In the present study, we demonstrate the utility of the flexible single crystals of an organic compound for the fabrication of a TENG as a self-powered tactile sensor. Triboelectrification was attained in single crystals as a result of surface functionalization with positively and negatively charged moieties, viz. Zn 2+ and F − , respectively, which resulted in a variable surface potential and reversible adhesion through electrostatic interaction and induction phenomena. TENG incorporating the single crystals showed an output voltage of 2.4 V, a current density of ∼2.2 μA/m 2 , and a power density of ∼850 mW/m 2 and was capable of charging commercial capacitors thereby ensuring its ability to be used as a self-powered touch sensor. Capitalizing on these features, a self-powered tactile sensor was fabricated to demonstrate limb movements. The excellent mechano-electric sensitivity (∼102 mV/kPa until 6 kPa range) and response time (∼38 ms) establish the viability of flexible organic single crystals for mechanical energy harvesting and biosensing applications that could pave the way for their utilization as biomedical wearable devices.

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Roadmap to Human–Machine Interaction through Triboelectric Nanogenerator and Machine Learning Convergence

Cited by 4 publications

References 121 publications

Recent advances in piezoelectric and triboelectric self-powered sensors for human–machine interface applications

Recent advances in piezoelectric and triboelectric self-powered sensors for human–machine interface applications

Self‐Learning e‐Skin Respirometer for Pulmonary Disease Detection

Flexible Organic Molecular Single Crystal-Based Triboelectric Device as a Self-Powered Tactile Sensor

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