A Pulse Rate Detection Method for Mouse Application Based on Multi-PPG Sensors

Lin, S. W.; Chen, Wei-Hao; Lin, Yuan-Hsiang

doi:10.3390/s17071628

Cited by 25 publications

(11 citation statements)

References 26 publications

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“…. [42] Pulse rate PPG sensors ECG signal Images According to the findings of the experiment, the suggested strategy may improve the usefulness and likelihood of PPG signal identification on palms.…”

Section: Heartbeat Detectionmentioning

confidence: 99%

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Deepfake Detection using Biological Features: A Survey

Patil¹,

Kale²,

Dhokey³

et al. 2023

Preprint

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A deep learning-based technique called deepfake has made it easier to change or modify images and videos. In investigations and court, visual evidence is commonly employed. These pieces of evidence may now be a suspect due to technological advancements, particularly Deepfake. Photographs and movies that have been edited are incredibly lifelike and difficult to tell apart from the original. Deepfakes have been utilized to blackmail individuals, plan terrorist attacks, disseminate incorrect facts, defame individuals as well as foment political turmoil. Our study describes the history of the Deepfake to acquire a thorough understanding of the technology. Additionally, the study focuses on the development and detection of Deepfakes and their challenges based on physiological measurements. Here, biological features such as eyebrow recognition, eye blinking detection, eye movement detection, ear and mouth detection, and heartbeat detection are clearly described and a scope in this domain is proposed. A comparison of each biological feature concerning classifiers or techniques along with its key findings is discussed. Generated using the generative adversarial network (GANs) model, Deepfakes are created by iterating an actual data-based generation and verification task through two opposite deep learning models. It was easier to identify deepfakes by humans during the nascent stage of this technology owing to pixel collapse phenomena that generated visible artifacts in skin tone and the general face structure. However, with the technology's advancement, DeepFakes have evolved to be highly indistinguishable from natural images hence it is important to review these detection methods.

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Section: Heartbeat Detectionmentioning

confidence: 99%

“…For generalized fake video detection [12], face matching [57][30], and eye blinking [42], they looked at a variety of forensic techniques. The most popular Deepfake detection techniques include training deep neural networks on a dataset of forged faces or spotting an aberrant pixel [27][35].…”

Section: Research On Detection Of Deepfakesmentioning

confidence: 99%

Deepfake Detection using Biological Features: A Survey

Patil¹,

Kale²,

Dhokey³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Pada contoh implementasi kali ini kami menggunakan sinyal photopletysmography (PPG) yang kami rekam untuk mengetahui tingkat stress yang dialami oleh pengguna. Implementasi menggunakan mouse PPG dan sebuah stress test game yang dapat dilihat detailnya pada referensi ilmiah [11]. Terdapat 3 kelas yang diukur yakni, "no stress", "time pressure", dan "interruption".…”

Section: Contoh Implementasiunclassified

Tahapan Desain dan Implementasi Model Machine Learning untuk Sistem Tertanam

Rizal¹

2020

ULTIMA Computing

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machine learning and edge computing currently becomes popular technology used in any discipline. Flexibility and adapt to the problem are the main advantages of its technology. In this paper, we explain step-by-step way to make a lightweight machine learning model especially intended for embedded system application. We use open source machine learning tool called as Weka to design the model. Moreover, we performed a simple stress recognition experiment to make our own dataset for evaluation. We evaluate algorithm complexity and accuracy for different well-known classifier such as support vector machine, simple logistic and hoeffding tree.

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“…Respiration rate, heart rate, foot pressure, and joint mobility are crucial in diagnosing health issues and abnormal occurrences such as tachypnea, hypoxemia, tachycardia, and bradycardia [ 1 , 2 ]. Different sensors have been utilized to gather physiological indications and body movement signals, especially electronic device-based sensors, which are critical for measuring parameters such as pulse beat [ 3 ], heart rate [ 4 ], and finger bending [ 5 ]. Electricity sensing has apparent limitations, such as a small linear response interval, electrical safety difficulties, and electromagnetic interference (EMI) [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Semiconductor Multimaterial Optical Fibers for Biomedical Applications

et al. 2022

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Integrated sensors and transmitters of a wide variety of human physiological indicators have recently emerged in the form of multimaterial optical fibers. The methods utilized in the manufacture of optical fibers facilitate the use of a wide range of functional elements in microscale optical fibers with an extensive variety of structures. This article presents an overview and review of semiconductor multimaterial optical fibers, their fabrication and postprocessing techniques, different geometries, and integration in devices that can be further utilized in biomedical applications. Semiconductor optical fiber sensors and fiber lasers for body temperature regulation, in vivo detection, volatile organic compound detection, and medical surgery will be discussed.

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A Pulse Rate Detection Method for Mouse Application Based on Multi-PPG Sensors

Cited by 25 publications

References 26 publications

Deepfake Detection using Biological Features: A Survey

Deepfake Detection using Biological Features: A Survey

Tahapan Desain dan Implementasi Model Machine Learning untuk Sistem Tertanam

Semiconductor Multimaterial Optical Fibers for Biomedical Applications

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