Biometric Signals Estimation Using Single Photon Camera and Deep Learning

Paracchini, Marco; Marcon, Marco; Villa, Federica; Zappa, Franco; Tubaro, Stefano

doi:10.3390/s20216102

Cited by 11 publications

(5 citation statements)

References 32 publications

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“…Its main aim is to capture real time images and send to the chip. Video processing microchip: the images are captured serially and is been processed and convert to electrical pulses which is mostly light and dark and further sends to the transmitter (Paracchini, Marcon, Villa, Zappa, & Tubaro, 2020). Transmitter: transmit the pulse to the receiver wirelessly Receiver: sends the pulses to the retinal implant by a wire which is as thin as a hair Retinal implant: composed of an array of electrodes on a chip which is about 1mm by 1mm in size which further triggers the pulse emission.…”

Section: Discussionmentioning

confidence: 99%

The Future of New Learning Way for The Visually Impaired Learners: Use Bionic Eye with Voice Assistant

Sheng¹

2021

JINOTEP

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Chatbots can be defined as artificial narrow intelligence (ANI) which can perform a single task such as answering people's question by harnessing the power of machine learning. There is some educational-related chatbots developed to interact with learners in mobile instant messaging (MIM) apps. Nowadays, there are more and more voice assistant developed in our world. They aim to fulfill user requests by choosing the best intent from multiple options generated by its Automated Speech Recognition and Natural Language Understanding sub-systems. Bionic eye is also still develop for the blind, it plays a vital part by restoring the vision. Technologies that are involved in bionic eyes are-multiple unit artificial retina chip system (MARC). This report describes an approach for the design and development of a voice assistant based on chatbot development basis combined with bionic eye application for an eLearning system with the learner's personal needs.

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Section: Discussionmentioning

confidence: 99%

The Future of New Learning Way for The Visually Impaired Learners: Use Bionic Eye with Voice Assistant

Sheng¹

2021

JINOTEP

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“…Paracchini et al 2020 [ 80 ] implemented rPPG based on a single-photon avalanche diode (SPAD) camera. This method combines deep learning and conventional signal processing to extract and examine the pulse signal.…”

Section: Contactless Ppg Methods Based On Deep Learningmentioning

confidence: 99%

A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Azarang

Kehtarnavaz

2021

Sensors

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The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

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“…In Reference [ 44 ], a single-photon avalanche diode (SPAD) camera was used to record videos. This camera was able to work well in dark environments.…”

Section: Hybrid Deep Learning Methodsmentioning

confidence: 99%

Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda

Cheng

Wong

Chin

et al. 2021

Sensors

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Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations.

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Biometric Signals Estimation Using Single Photon Camera and Deep Learning

Cited by 11 publications

References 32 publications

The Future of New Learning Way for The Visually Impaired Learners: Use Bionic Eye with Voice Assistant

The Future of New Learning Way for The Visually Impaired Learners: Use Bionic Eye with Voice Assistant

A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda

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