PPG-based human identification using Mel-frequency cepstral coefficients and neural networks

Siam, Ali I.; Elazm, Atef Abou; El‐Bahnasawy, Nirmeen A.; Banby, Ghada M. El; El‐Samie, Fathi E. Abd

doi:10.1007/s11042-021-10781-8

Cited by 31 publications

(18 citation statements)

References 22 publications

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“…The gathered signal information is processed by using Mel-frequency Cepstral Coefficients (MFCC) feature extraction method [ 30 ]. The MFCC based derived features are input to the neural network used to recognise the patient’s health condition.…”

Section: Methodsmentioning

confidence: 99%

Remotely Monitoring COVID-19 Patient Health Condition Using Metaheuristics Convolute Networks from IoT-Based Wearable Device Health Data

Jaber

Alameri

Ali

et al. 2022

Sensors

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Today, COVID-19-patient health monitoring and management are major public health challenges for technologies. This research monitored COVID-19 patients by using the Internet of Things. IoT-based collected real-time GPS helps alert the patient automatically to reduce risk factors. Wearable IoT devices are attached to the human body, interconnected with edge nodes, to investigate data for making health-condition decisions. This system uses the wearable IoT sensor, cloud, and web layers to explore the patient’s health condition remotely. Every layer has specific functionality in the COVID-19 symptoms’ monitoring process. The first layer collects the patient health information, which is transferred to the second layer that stores that data in the cloud. The network examines health data and alerts the patients, thus helping users take immediate actions. Finally, the web layer notifies family members to take appropriate steps. This optimized deep-learning model allows for the management and monitoring for further analysis.

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Section: Methodsmentioning

confidence: 99%

Remotely Monitoring COVID-19 Patient Health Condition Using Metaheuristics Convolute Networks from IoT-Based Wearable Device Health Data

Jaber

Alameri

Ali

et al. 2022

Sensors

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“…We extracted the DWT [19], short-time Fourier transform (STFT) [30], and melfrequency cepstral coefficients (MFCCs) [31] of the PPG samples as separate frequency-domain…”

Section: Frequency-domain Featuresmentioning

confidence: 99%

Time–frequency fusion learning for photoplethysmography biometric recognition

Liu

Huang

et al. 2022

IET Biometrics

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Photoplethysmography (PPG) signal is a novel biometric trait related to the identity of people; many time-and frequency-domain methods for PPG biometric recognition have been proposed. However, the existing domain methods for PPG biometric recognition only consider a single domain or the feature-level fusion of time and frequency domains, without considering the exploration of the fusion correlations of the time and frequency domains. The authors propose a time-frequency fusion for a PPG biometric recognition method with collective matrix factorisation (TFCMF) that leverages collective matrix factorisation to learn a shared latent semantic space by exploring the fusion correlations of the time and frequency domains. In addition, the authors utilise the ℓ 2,1 norm to constrain the reconstruction error and shared matrix, which can alleviate the influence of noise and intra-class variation, and ensure the robustness of learnt semantic space. Experiments demonstrate that TFCMF has better recognition performance than current state-of-the-art methods for PPG biometric recognition.

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“…The more the amount of blood is, the more absorbed light and less reflected light arriving at the PD. The Direct Current (DC) component in the resulting waveform is due to the reflectance of light on bones, tissues, and other stationary parts, while the Alternating Current (AC) component represents the pulsatile change of the arterial blood that forms the photoplethysmography (PPG) signal [ 25 – 27 ].…”

Section: Preliminariesmentioning

confidence: 99%

Secure Health Monitoring Communication Systems Based on IoT and Cloud Computing for Medical Emergency Applications

Siam

Almaiah

Alzahrani

et al. 2021

Computational Intelligence and Neuroscience

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Smart health surveillance technology has attracted wide attention between patients and professionals or specialists to provide early detection of critical abnormal situations without the need to be in direct contact with the patient. This paper presents a secure smart monitoring portable multivital signal system based on Internet-of-Things (IoT) technology. The implemented system is designed to measure the key health parameters: heart rate (HR), blood oxygen saturation (SpO2), and body temperature, simultaneously. The captured physiological signals are processed and encrypted using the Advanced Encryption Standard (AES) algorithm before sending them to the cloud. An ESP8266 integrated unit is used for processing, encryption, and providing connectivity to the cloud over Wi-Fi. On the other side, trusted medical organization servers receive and decrypt the measurements and display the values on the monitoring dashboard for the authorized specialists. The proposed system measurements are compared with a number of commercial medical devices. Results demonstrate that the measurements of the proposed system are within the 95% confidence interval. Moreover, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Relative Error (MRE) for the proposed system are calculated as 1.44, 1.12, and 0.012, respectively, for HR, 1.13, 0.92, and 0.009, respectively, for SpO2, and 0.13, 0.11, and 0.003, respectively, for body temperature. These results demonstrate the high accuracy and reliability of the proposed system.

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PPG-based human identification using Mel-frequency cepstral coefficients and neural networks

Cited by 31 publications

References 22 publications

Remotely Monitoring COVID-19 Patient Health Condition Using Metaheuristics Convolute Networks from IoT-Based Wearable Device Health Data

Remotely Monitoring COVID-19 Patient Health Condition Using Metaheuristics Convolute Networks from IoT-Based Wearable Device Health Data

Time–frequency fusion learning for photoplethysmography biometric recognition

Secure Health Monitoring Communication Systems Based on IoT and Cloud Computing for Medical Emergency Applications

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