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12 pages, 4588 KiB

Open AccessArticle

Evaluation of Dry Electrodes in Canine Heart Rate Monitoring

by Juhani Virtanen, Sanni Somppi, Heini Törnqvist, Vala Jeyhani, Patrique Fiedler, Yulia Gizatdinova, Päivi Majaranta, Heli Väätäjä, Anna Valldeoriola Cardó, Jukka Lekkala, Sampo Tuukkanen, Veikko Surakka, Outi Vainio and Antti Vehkaoja

Sensors 2018, 18(6), 1757; https://doi.org/10.3390/s18061757 - 30 May 2018

Cited by 9 | Viewed by 6065

Abstract

The functionality of three dry electrocardiogram electrode constructions was evaluated by measuring canine heart rate during four different behaviors: Standing, sitting, lying and walking. The testing was repeated (n = 9) in each of the 36 scenarios with three dogs. Two of the [...] Read more.

The functionality of three dry electrocardiogram electrode constructions was evaluated by measuring canine heart rate during four different behaviors: Standing, sitting, lying and walking. The testing was repeated (n = 9) in each of the 36 scenarios with three dogs. Two of the electrodes were constructed with spring-loaded test pins while the third electrode was a molded polymer electrode with Ag/AgCl coating. During the measurement, a specifically designed harness was used to attach the electrodes to the dogs. The performance of the electrodes was evaluated and compared in terms of heartbeat detection coverage. The effect on the respective heart rate coverage was studied by computing the heart rate coverage from the measured electrocardiogram signal using a pattern-matching algorithm to extract the R-peaks and further the beat-to-beat heart rate. The results show that the overall coverage ratios regarding the electrodes varied between 45–95% in four different activity modes. The lowest coverage was for lying and walking and the highest was for standing and sitting. Full article

(This article belongs to the Special Issue Advanced Physiological Sensing)

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16 pages, 2551 KiB

Open AccessArticle

Acquiring Respiration Rate from Photoplethysmographic Signal by Recursive Bayesian Tracking of Intrinsic Modes in Time-Frequency Spectra

by Mikko Pirhonen, Mikko Peltokangas and Antti Vehkaoja

Sensors 2018, 18(6), 1693; https://doi.org/10.3390/s18061693 - 24 May 2018

Cited by 25 | Viewed by 5465

Abstract

Respiration rate (RR) provides useful information for assessing the status of a patient. We propose RR estimation based on photoplethysmography (PPG) because the blood perfusion dynamics are known to carry information on breathing, as respiration-induced modulations in the PPG signal. We studied the [...] Read more.

Respiration rate (RR) provides useful information for assessing the status of a patient. We propose RR estimation based on photoplethysmography (PPG) because the blood perfusion dynamics are known to carry information on breathing, as respiration-induced modulations in the PPG signal. We studied the use of amplitude variability of transmittance mode finger PPG signal in RR estimation by comparing four time-frequency (TF) representation methods of the signal cascaded with a particle filter. The TF methods compared were short-time Fourier transform (STFT) and three types of synchrosqueezing methods. The public VORTAL database was used in this study. The results indicate that the advanced frequency reallocation methods based on synchrosqueezing approach may present improvement over linear methods, such as STFT. The best results were achieved using wavelet synchrosqueezing transform, having a mean absolute error and median error of 2.33 and 1.15 breaths per minute, respectively. Synchrosqueezing methods were generally more accurate than STFT on most of the subjects when particle filtering was applied. While TF analysis combined with particle filtering is a promising alternative for real-time estimation of RR, artefacts and non-respiration-related frequency components remain problematic and impose requirements for further studies in the areas of signal processing algorithms an PPG instrumentation. Full article

(This article belongs to the Special Issue Advanced Physiological Sensing)

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22 pages, 25630 KiB

Open AccessArticle

Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals

by Ning Zhuang, Ying Zeng, Kai Yang, Chi Zhang, Li Tong and Bin Yan

Sensors 2018, 18(3), 841; https://doi.org/10.3390/s18030841 - 12 Mar 2018

Cited by 63 | Viewed by 7165

Abstract

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and [...] Read more.

Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods. Full article

(This article belongs to the Special Issue Advanced Physiological Sensing)

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26 pages, 2939 KiB

Open AccessArticle

Using Psychophysiological Sensors to Assess Mental Workload During Web Browsing

by Angel Jimenez-Molina, Cristian Retamal and Hernan Lira

Sensors 2018, 18(2), 458; https://doi.org/10.3390/s18020458 - 3 Feb 2018

Cited by 69 | Viewed by 8925

Abstract

Knowledge of the mental workload induced by a Web page is essential for improving users’ browsing experience. However, continuously assessing the mental workload during a browsing task is challenging. To address this issue, this paper leverages the correlation between stimuli and physiological responses, [...] Read more.

Knowledge of the mental workload induced by a Web page is essential for improving users’ browsing experience. However, continuously assessing the mental workload during a browsing task is challenging. To address this issue, this paper leverages the correlation between stimuli and physiological responses, which are measured with high-frequency, non-invasive psychophysiological sensors during very short span windows. An experiment was conducted to identify levels of mental workload through the analysis of pupil dilation measured by an eye-tracking sensor. In addition, a method was developed to classify mental workload by appropriately combining different signals (electrodermal activity (EDA), electrocardiogram, photoplethysmo-graphy (PPG), electroencephalogram (EEG), temperature and pupil dilation) obtained with non-invasive psychophysiological sensors. The results show that the Web browsing task involves four levels of mental workload. Also, by combining all the sensors, the efficiency of the classification reaches 93.7%. Full article

(This article belongs to the Special Issue Advanced Physiological Sensing)

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9914 KiB

Open AccessArticle

Photoplethysmography Signal Analysis for Optimal Region-of-Interest Determination in Video Imaging on a Built-In Smartphone under Different Conditions

by Yunyoung Nam and Yun-Cheol Nam

Sensors 2017, 17(10), 2385; https://doi.org/10.3390/s17102385 - 19 Oct 2017

Cited by 7 | Viewed by 7591

Abstract

Smartphones and tablets are widely used in medical fields, which can improve healthcare and reduce healthcare costs. Many medical applications for smartphones and tablets have already been developed and widely used by both health professionals and patients. Specifically, video recordings of fingertips made [...] Read more.

Smartphones and tablets are widely used in medical fields, which can improve healthcare and reduce healthcare costs. Many medical applications for smartphones and tablets have already been developed and widely used by both health professionals and patients. Specifically, video recordings of fingertips made using a smartphone camera contain a pulsatile component caused by the cardiac pulse equivalent to that present in a photoplethysmographic signal. By performing peak detection on the pulsatile signal, it is possible to estimate a continuous heart rate and a respiratory rate. To estimate the heart rate and respiratory rate accurately, which pixel regions of the color bands give the most optimal signal quality should be investigated. In this paper, we investigate signal quality to determine the best signal quality by the largest amplitude values for three different smartphones under different conditions. We conducted several experiments to obtain reliable PPG signals and compared the PPG signal strength in the three color bands when the flashlight was both on and off. We also evaluated the intensity changes of PPG signals obtained from the smartphones with motion artifacts and fingertip pressure force. Furthermore, we have compared the PSNR of PPG signals of the full-size images with that of the region of interests (ROIs). Full article

(This article belongs to the Special Issue Advanced Physiological Sensing)

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9210 KiB

Open AccessArticle

A Wireless Monitoring System Using a Tunneling Sensor Array in a Smart Oral Appliance for Sleep Apnea Treatment

by Kun-Ying Yeh, Chao-Chi Yeh, Chun-Chang Wu, Kuan Tang, Jyun-Yi Wu, Yun-Ting Chen, Ming-Xin Xu, Yunn-Jy Chen, Yao-Joe Yang and Shey-Shi Lu

Sensors 2017, 17(10), 2358; https://doi.org/10.3390/s17102358 - 16 Oct 2017

Cited by 6 | Viewed by 5095

Abstract

Sleep apnea is a serious sleep disorder, and the most common type is obstructive sleep apnea (OSA). Untreated OSA will cause lots of potential health problems. Oral appliance therapy is an effective and popular approach for OSA treatment, but making a perfect fit [...] Read more.

Sleep apnea is a serious sleep disorder, and the most common type is obstructive sleep apnea (OSA). Untreated OSA will cause lots of potential health problems. Oral appliance therapy is an effective and popular approach for OSA treatment, but making a perfect fit for each patient is time-consuming and decreases its efficiency considerably. This paper proposes a System-on-a-Chip (SoC) enabled sleep monitoring system in a smart oral appliance, which is capable of intelligently collecting the physiological data about tongue movement through the whole therapy. A tunneling sensor array with an ultra-high sensitivity is incorporated to accurately detect the subtle pressure from the tongue. When the device is placed on the wireless platform, the temporary stored data will be retrieved and wirelessly transmitted to personal computers and cloud storages. The battery will be recharged by harvesting external RF power from the platform. A compact prototype module, whose size is 4.5 × 2.5 × 0.9 cm³, is implemented and embedded inside the oral appliance to demonstrate the tongue movement detection in continuous time frames. The functions of this design are verified by the presented measurement results. This design aims to increase efficiency and make it a total solution for OSA treatment. Full article

(This article belongs to the Special Issue Advanced Physiological Sensing)

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953 KiB

Open AccessArticle

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

by Shu-Tyng Lin, Wei-Hao Chen and Yuan-Hsiang Lin

Sensors 2017, 17(7), 1628; https://doi.org/10.3390/s17071628 - 14 Jul 2017

Cited by 21 | Viewed by 6660

Abstract

Heart rate is an important physiological parameter for healthcare. Among measurement methods, photoplethysmography (PPG) is an easy and convenient method for pulse rate detection. However, as the PPG signal faces the challenge of motion artifacts and is constrained by the position chosen, the [...] Read more.

Heart rate is an important physiological parameter for healthcare. Among measurement methods, photoplethysmography (PPG) is an easy and convenient method for pulse rate detection. However, as the PPG signal faces the challenge of motion artifacts and is constrained by the position chosen, the purpose of this paper is to implement a comfortable and easy-to-use multi-PPG sensor module combined with a stable and accurate real-time pulse rate detection method on a computer mouse. A weighted average method for multi-PPG sensors is used to adjust the weight of each signal channel in order to raise the accuracy and stability of the detected signal, therefore reducing the disturbance of noise under the environment of moving effectively and efficiently. According to the experiment results, the proposed method can increase the usability and probability of PPG signal detection on palms. Full article

(This article belongs to the Special Issue Advanced Physiological Sensing)

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4016 KiB

Open AccessArticle

A Real-Time Contactless Pulse Rate and Motion Status Monitoring System Based on Complexion Tracking

by Yu-Chen Lin, Nai-Kuan Chou, Guan-You Lin, Meng-Han Li and Yuan-Hsiang Lin

Sensors 2017, 17(7), 1490; https://doi.org/10.3390/s17071490 - 24 Jun 2017

Cited by 22 | Viewed by 6138

Abstract

Subject movement and a dark environment will increase the difficulty of image-based contactless pulse rate detection. In this paper, we detected the subject’s motion status based on complexion tracking and proposed a motion index (MI) to filter motion artifacts in order to increase [...] Read more.

Subject movement and a dark environment will increase the difficulty of image-based contactless pulse rate detection. In this paper, we detected the subject’s motion status based on complexion tracking and proposed a motion index (MI) to filter motion artifacts in order to increase pulse rate measurement accuracy. Additionally, we integrated the near infrared (NIR) LEDs with the adopted sensor and proposed an effective method to measure the pulse rate in a dark environment. To achieve real-time data processing, the proposed framework is constructed on a Field Programmable Gate Array (FPGA) platform. Next, the instant pulse rate and motion status are transmitted to a smartphone for remote monitoring. The experiment results showed the error of the pulse rate detection to be within −3.44 to +4.53 bpm under sufficient ambient light and −2.96 to + 4.24 bpm for night mode detection, when the moving speed is higher than 14.45 cm/s. These results demonstrate that the proposed method can improve the robustness of image-based contactless pulse rate detection despite subject movement and a dark environment. Full article

(This article belongs to the Special Issue Advanced Physiological Sensing)

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Review

Jump to: Research

17 pages, 2405 KiB

Open AccessReview

Detection-Response Task—Uses and Limitations

by Kristina Stojmenova and Jaka Sodnik

Sensors 2018, 18(2), 594; https://doi.org/10.3390/s18020594 - 14 Feb 2018

Cited by 44 | Viewed by 6259

Abstract

The Detection-Response Task is a method for assessing the attentional effects of cognitive load in a driving environment. Drivers are presented with a sensory stimulus every 3–5 s, and are asked to respond to it by pressing a button attached to their finger. [...] Read more.

The Detection-Response Task is a method for assessing the attentional effects of cognitive load in a driving environment. Drivers are presented with a sensory stimulus every 3–5 s, and are asked to respond to it by pressing a button attached to their finger. Response times and hit rates are interpreted as indicators of the attentional effect of cognitive load. The stimuli can be visual, tactile and auditory, and are chosen based on the type of in-vehicle system or device that is being evaluated. Its biggest disadvantage is that the method itself also affects the driver’s performance and secondary task completion times. Nevertheless, this is an easy to use and implement method, which allows relevant assessment and evaluation of in-vehicle systems. By following the recommendations and taking into account its limitations, researchers can obtain reliable and valuable results on the attentional effects of cognitive load on drivers. Full article

(This article belongs to the Special Issue Advanced Physiological Sensing)

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