RGB-D imaging used for OSAS diagnostics

Volák, Jozef; Koniar, Dušan; Hargaš, Libor; Jablončík, František; Sekel'ova, Nikola; Ďurdík, Peter

doi:10.1109/elektro.2018.8398326

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…We will refer to the former as Kinect v1, and to the latter as Kinect v2. Both versions have been widely used by the research community in various scientific such as object detection and object recognition [ 1 , 2 , 3 ], mapping and SLAM [ 4 , 5 , 6 ], gesture recognition and human–machine interaction (HMI) [ 7 , 8 , 9 ], telepresence [ 10 , 11 ], virtual reality, mixed reality, and medicine and rehabilitation [ 12 , 13 , 14 , 15 , 16 ]. According to [ 17 ] there have been hundreds of papers written and published on this subject.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Azure Kinect and Its Comparison to Kinect V1 and Kinect V2

Tölgyessy¹,

Dekan²,

Chovanec³

et al. 2021

Sensors

195

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The Azure Kinect is the successor of Kinect v1 and Kinect v2. In this paper we perform brief data analysis and comparison of all Kinect versions with focus on precision (repeatability) and various aspects of noise of these three sensors. Then we thoroughly evaluate the new Azure Kinect; namely its warm-up time, precision (and sources of its variability), accuracy (thoroughly, using a robotic arm), reflectivity (using 18 different materials), and the multipath and flying pixel phenomenon. Furthermore, we validate its performance in both indoor and outdoor environments, including direct and indirect sun conditions. We conclude with a discussion on its improvements in the context of the evolution of the Kinect sensor. It was shown that it is crucial to choose well designed experiments to measure accuracy, since the RGB and depth camera are not aligned. Our measurements confirm the officially stated values, namely standard deviation ≤17 mm, and distance error <11 mm in up to 3.5 m distance from the sensor in all four supported modes. The device, however, has to be warmed up for at least 40–50 min to give stable results. Due to the time-of-flight technology, the Azure Kinect cannot be reliably used in direct sunlight. Therefore, it is convenient mostly for indoor applications.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the Azure Kinect and Its Comparison to Kinect V1 and Kinect V2

Tölgyessy¹,

Dekan²,

Chovanec³

et al. 2021

Sensors

195

View full text Add to dashboard Cite

show abstract

“…We will refer to the former as Kinect v1 and to the latter as Kinect v2. Both versions have been widely used by the research community for various scientific purposes, such as object detection and object recognition [1][2][3], mapping and SLAM [4][5][6], gesture recognition and human-machine interaction (HMI) [7][8][9], telepresence [10,11], virtual reality, mixed reality, and medicine and rehabilitation [12][13][14][15][16]. However, both sensors are now discontinued and are no longer being officially distributed and sold.…”

Section: Introductionmentioning

confidence: 99%

Skeleton Tracking Accuracy and Precision Evaluation of Kinect V1, Kinect V2, and the Azure Kinect

Tölgyessy¹,

Dekan²,

Chovanec³

2021

Applied Sciences

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The Azure Kinect, the successor of Kinect v1 and Kinect v2, is a depth sensor. In this paper we evaluate the skeleton tracking abilities of the new sensor, namely accuracy and precision (repeatability). Firstly, we state the technical features of all three sensors, since we want to put the new Azure Kinect in the context of its previous versions. Then, we present the experimental results of general accuracy and precision obtained by measuring a plate mounted to a robotic manipulator end effector which was moved along the depth axis of each sensor and compare them. In the second experiment, we mounted a human-sized figurine to the end effector and placed it in the same positions as the test plate. Positions were located 400 mm from each other. In each position, we measured relative accuracy and precision (repeatability) of the detected figurine body joints. We compared the results and concluded that the Azure Kinect surpasses its discontinued predecessors, both in accuracy and precision. It is a suitable sensor for human–robot interaction, body-motion analysis, and other gesture-based applications. Our analysis serves as a pilot study for future HMI (human–machine interaction) designs and applications using the new Kinect Azure and puts it in the context of its successful predecessors.

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“…In recent years, scholars have proposed a variety of OSAHS disease discrimination techniques based on various symptom characteristics. Among them, A. Garde used the visual midpoint (radius and angle) distribution characteristics of SpO 2 signals to distinguish OSAHS symptoms [ 6 ]; Kim used the patient's breathing sound signal to develop a classification of OSAHS severity model [ 7 ]; Volak made preliminary judgments on OSAHS through image recognition of children's dental features [ 8 ]; Castillo-Escario et al develop an algorithm for detecting silence events and classifying them into apneas and hypopneas [ 9 ]. The current medical research reports show that the clinical apnea syndrome events manifestations of an adult are as follows [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Snore from OSAHS Patients Based on Deep Learning

Shen

Cheng

Zhu

et al. 2020

Journal of Healthcare Engineering

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Obstructive sleep apnea-hypopnea syndrome (OSAHS) is extremely harmful to the human body and may cause neurological dysfunction and endocrine dysfunction, resulting in damage to multiple organs and multiple systems throughout the body and negatively affecting the cardiovascular, kidney, and mental systems. Clinically, doctors usually use standard PSG (Polysomnography) to assist diagnosis. PSG determines whether a person has apnea syndrome with multidimensional data such as brain waves, heart rate, and blood oxygen saturation. In this paper, we have presented a method of recognizing OSAHS, which is convenient for patients to monitor themselves in daily life to avoid delayed treatment. Firstly, we theoretically analyzed the difference between the snoring sounds of normal people and OSAHS patients in the time and frequency domains. Secondly, the snoring sounds related to apnea events and the nonapnea related snoring sounds were classified by deep learning, and then, the severity of OSAHS symptoms had been recognized. In the algorithm proposed in this paper, the snoring data features are extracted through the three feature extraction methods, which are MFCC, LPCC, and LPMFCC. Moreover, we adopted CNN and LSTM for classification. The experimental results show that the MFCC feature extraction method and the LSTM model have the highest accuracy rate which was 87% when it is adopted for binary-classification of snoring data. Moreover, the AHI value of the patient can be obtained by the algorithm system which can determine the severity degree of OSAHS.

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RGB-D imaging used for OSAS diagnostics

Cited by 3 publications

References 5 publications

Evaluation of the Azure Kinect and Its Comparison to Kinect V1 and Kinect V2

Evaluation of the Azure Kinect and Its Comparison to Kinect V1 and Kinect V2

Skeleton Tracking Accuracy and Precision Evaluation of Kinect V1, Kinect V2, and the Azure Kinect

Detection of Snore from OSAHS Patients Based on Deep Learning

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