Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis

Procházka, Aleš; Schätz, Martin; Vyšata, Oldřich; Vališ, Martin

doi:10.3390/s16070996

Cited by 75 publications

(50 citation statements)

References 40 publications

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“…The classification accuracies and cross-validation errors evaluated by the leave-one-out method for a sample two-hour long EEG record are presented in Table 4. Input features include relative power in 4 frequency bands (1)(2)(3)(4)(4)(5)(6)(7)(8)(8)(9)(10)(11)(12)(12)(13)(14)(15)(16)(17)(18)(19)(20). For the given set of features, the neural network model provide classification results with higher accuracy and lower cross-validation error than did the k-nearest neighbour and decision tree methods.…”

Section: Methods Parameters Accuracy (%) Cross-validmentioning

confidence: 99%

“…Multimodal data acquired in sleep laboratories form multichannel records that require wire attachments to the patients in most cases. Selected physiological signals are simultaneously monitored by specific sensors [1][2][3] and they form time series that are recorded with different sampling frequencies and often combined with videosequences acquired by infra and thermographic imaging cameras. Figure 1(a1,a2) present an example of a multichannel PSG record 5 s long used for diagnosis and classification of sleep disorders either by an experienced neurologist or by an automatic classification model.…”

Section: Introductionmentioning

confidence: 99%

“…(1)(2)(3)(4)(4)(5)(6)(7)(8)(8)(9)(10)(11)(12)(12)(13)(14)(15)(16)(16)(17)(18)(19)(20)), 2. additional features evaluated from the 4 frequency bands (0.05-0.2, 0.2-0.3, 0.3-0.4, 0.4-0.8 Hz) of the flow channel and 3 frequency bands (4-15, 15-30, 30-40 Hz) of the electro-oculogram channel.…”

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confidence: 99%

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Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition

et al. 2018

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Multimodal signal analysis based on sophisticated sensors, efficient communication systems and fast parallel processing methods has a rapidly increasing range of multidisciplinary applications. The present paper is devoted to pattern recognition, machine learning, and the analysis of sleep stages in the detection of sleep disorders using polysomnography (PSG) data, including electroencephalography (EEG), breathing (Flow), and electro-oculogram (EOG) signals. The proposed method is based on the classification of selected features by a neural network system with sigmoidal and softmax transfer functions using Bayesian methods for the evaluation of the probabilities of the separate classes. The application is devoted to the analysis of the sleep stages of 184 individuals with different diagnoses, using EEG and further PSG signals. Data analysis points to an average increase of the length of the Wake stage by 2.7% per 10 years and a decrease of the length of the Rapid Eye Movement (REM) stages by 0.8% per 10 years. The mean classification accuracy for given sets of records and single EEG and multimodal features is 88.7% ( standard deviation, STD: 2.1) and 89.6% (STD:1.9), respectively. The proposed methods enable the use of adaptive learning processes for the detection and classification of health disorders based on prior specialist experience and man-machine interaction.

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Section: Methods Parameters Accuracy (%) Cross-validmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition

et al. 2018

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“…The first dataset is generally regarded as wearable sensor data, which are generated locally at the user; the second type represents the sensing data, which are obtained remotely within a short distance. They have wide applications, which have been reported recently, such as [26,27,28] for wearable sensors and [29,30,31] for Kinect-based sensors.…”

Section: Methodsmentioning

confidence: 99%

Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition

Fong

Song

Cho

et al. 2017

Sensors

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In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called ‘shadow features’ are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.

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“…Therefore, the BVP signal is often used to detect the heart rate and breathing rate. 300,301 Galvanic skin response (GSR) was used in Refs. 302 and 303 to design GSR-based sensors for the detection of stress states and prediction of performance under stressful conditions.…”

Section: Other Sensesmentioning

confidence: 99%

A Review on Human-Computer Interaction and Intelligent Robots

Ren

Bao

2020

Int. J. Info. Tech. Dec. Mak.

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In the field of artificial intelligence, human–computer interaction (HCI) technology and its related intelligent robot technologies are essential and interesting contents of research. From the perspective of software algorithm and hardware system, these above-mentioned technologies study and try to build a natural HCI environment. The purpose of this research is to provide an overview of HCI and intelligent robots. This research highlights the existing technologies of listening, speaking, reading, writing, and other senses, which are widely used in human interaction. Based on these same technologies, this research introduces some intelligent robot systems and platforms. This paper also forecasts some vital challenges of researching HCI and intelligent robots. The authors hope that this work will help researchers in the field to acquire the necessary information and technologies to further conduct more advanced research.

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Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis

Cited by 75 publications

References 40 publications

Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition

Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition

Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition

A Review on Human-Computer Interaction and Intelligent Robots

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