Sensors For Physiological Signals Research Articles

Self-Powered Hybrid Motion and Health Sensing System Based on Triboelectric Nanogenerators.

Triboelectric nanogenerator (TENG) represents an effective approach for the conversion of mechanical energy into electrical energy and has been explored to combine multiple technologies in past years. Self-powered sensors are not only free from the constraints of mechanical energy in the environment but also capable of efficiently harvesting ambient energy to sustain continuous operation. In this review, the remarkable development of TENG-based human body sensing achieved in recent years is presented, with a specific focus on human health sensing solutions, such as body motion and physiological signal detection. The movements originating from different parts of the body, such as body, touch, sound, and eyes, are systematically classified, and a thorough review of sensor structures and materials is conducted. Physiological signal sensors are categorized into non-implantable and implantable biomedical sensors for discussion. Suggestions for future applications of TENG-based biomedical sensors are also indicated, highlighting the associated challenges.

Driving Fatigue Monitoring via Kernel Sparse Representation Regression With GMC Penalty

Automatic monitoring technology for fatigued driving can greatly reduce traffic accidents caused by drivers due to decreased attention. The use of monitoring algorithms based on physiological signal sensors is of great significance for objective monitoring. In this paper, a novel algorithm with excellent performance and strong generalization ability, kernel sparse representation regression based on generalized minimax-concave (GMC-KSRR), is proposed to identify fatigue physiological signals. Specifically, in the proposed algorithm, the training samples of physiological features are mapped in reproducing kernel Hilbert space (RKHS) for linear separability. Then, in RKHS, the sparse coefficients of the test samples are obtained by generalized minimax-concave (GMC) penalty-based sparse representation (SR). Different from the traditional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{{1}}$ </tex-math></inline-formula> -norm, the GMC penalty we used does not underestimate the high-amplitude component of sparsity coefficients, resulting in a more accurate regression result. Finally, the regression decision is performed in the label subspace according to the obtained sparse coefficients. Meanwhile, a multimodality algorithm based on GMC-KSRR has also been proposed that weighs each modality according to quality for robust fatigue monitoring. Eventually, competitive experimental results on the well-known SEED-VIG dataset against the state-of-the-art methods demonstrate the efficacy of the proposed algorithms in identifying fatigue physiological signals, indicating its powerful application potential in driving fatigue monitoring.

Human activity recognition from textile electrocardiograms.

Textile sensors for physiological signals bear the potential of unobtrusive and continuous application in daily life. Recently, textile electrocardiography (ECG) sensors became available which are of particular interest for physical activity monitoring due to the high effect of exercise on the heart rate. In this work, we evaluate the effectiveness of a single-lead ECG signal acquired using a non-medical-grade ECG shirt for human activity recognition (HAR). Healthy volunteers (N=10) wore the shirt during four different activities (sleeping, sitting, walking, running) in an uncontrolled environment and ECG data (256 Hz, 12 Bit) was stored, manually checked, and unusable segments (e.g. no sensor contact) were removed, resulting in a total of 228 hours of recording. Signals were split in short segments of different duration (10, 30, 60s), transformed using the Short-time Fourier Transform (STFT) to a spectrogram image and fed into a state-of-the-art convolutional neural network (CNN). The best configuration results in an F'l-Score of 73% and an accuracy of 77% on the test set. Results with leave-one-subject-out cross-validation show F'l-Scores ranging from 41 % to 80%. Thus, a single-lead, wearable-generated ECG has an informative value for HAR to a certain extent. In future work, we aim at using more sensors of the smart shirt and sensor fusion.

3D printed stretchable sensor based on silver nanowires-polydimethylsiloxane

As a new rapid additive manufacturing technology that has emerged in recent years, 3D printing technology can realize the precise manufacturing of complex and flexible sensor structures. In this study, a sensor was fabricated by injecting silver nanowires (AgNWs) ethanol solution into stretchable polydimethylsiloxane (PDMS). The substrate was used in two design configurations through a 3D printing template method, i.e. “straight” and “wave”. Compared to the straight sensor, the structural design of the wave sensor could increase the stretch range and sensitivity. In particular, the stretch range increased by 26.1% and the sensitivity improved by 96.0%. The stretchable sensor was successfully applied in pronunciation recognition and gait detection. Therefore, the stretchable sensor is also expected to be further used in fields such as foldable phones and wearable physiological signal sensors.

A 1.06-$\mu$ W Smart ECG Processor in 65-nm CMOS for Real-Time Biometric Authentication and Personal Cardiac Monitoring

Many wearable devices employ the sensors for physiological signals (e.g., electrocardiogram or ECG) to continuously monitor personal health (e.g., cardiac monitoring). Considering private medical data storage, secure access to such wearable devices becomes a crucial necessity. Exploiting the ECG sensors present on wearable devices, we investigate the possibility of using ECG as the individually unique source for device authentication. In particular, we propose to use ECG features toward both cardiac monitoring and neural-network-based biometric authentication. For such complex functionalities to be seamlessly integrated in wearable devices, an accurate algorithm must be implemented with ultralow power and a small form factor. In this paper, a smart ECG processor is presented for ECG-based authentication as well as cardiac monitoring. Data-driven Lasso regression and low-precision techniques are developed to compress neural networks for feature extraction by 24.4 $\times $ . The 65-nm testchip consumes 1.06 $\mu \text{W}$ at 0.55 V for real-time ECG authentication. For authentication, equal error rates of 1.70%/2.18%/2.48% (best/average/worst) are achieved on the in-house 645-subject database. For cardiac monitoring, 93.13% arrhythmia detection sensitivity and 89.78% specificity are achieved for 42 subjects in the MIT-BIH arrhythmia database.

Design of User-Customized Negative Emotion Classifier Based on Feature Selection Using Physiological Signal Sensors.

First, the Likert scale and self-assessment manikin are used to provide emotion analogies, but they have limits for reflecting subjective factors. To solve this problem, we use physiological signals that show objective responses from cognitive status. The physiological signals used are electrocardiogram, skin temperature, and electrodermal activity (EDA). Second, the degree of emotion felt, and the related physiological signals, vary according to the individual. KLD calculates the difference in probability distribution shape patterns between two classes. Therefore, it is possible to analyze the relationship between physiological signals and emotion. As the result, features from EDA are important for distinguishing negative emotion in all subjects. In addition, the proposed feature selection algorithm showed an average accuracy of 92.5% and made it possible to improve the accuracy of negative emotion recognition.

Physiological Signals based Day-Dependence Analysis with Metric Multidimensional Scaling for Sentiment Classification in Wearable Sensors

The interaction of the affective has emerged in implicit human-computer interaction. Given the physiological signals in the recognition process of the affective, the different positions by which the physiological signal sensors are installed in the body, along with the daily habits and moods of human beings, influence the affective physiological signals. The scalar product matrix was calculated in this study based on metric multidimensional scaling with dissimilarity matrix. Subsequently, the matrix of individual attribute reconstructs was obtained using the principal component factor. The method proposed in this study eliminates day dependence, reduces the effect of time in the physiological signals of the affective, and improves the accuracy of affection classification.

Heart Rate and Respiratory Rate Measurement Using Body-Sound

Daily stress management is essential for preventing mental illness. We have therefore been developing physiological signal sensors for measurement of multiple stress markers. The present study focused on “body-sound,” i.e., sound generated inside the human body, which included high amounts of information on physical condition. The prototype body-sound sensor we developed consisted of stethoscopes, amplifier microphones, an A/D converter and a PC. We measured body-sound in the neck and trunk. Then, we selected heart rate and respiratory rate as common stress markers. By comparing body-sound at different measurement positions, we developed new sensing system simultaneously measuring heart and respiratory rate with a body-sound sensor at the neck. Finally, we evaluated proposal performance by comparing body-sound and the electrocardiogram and respiratory waveform, confirming that our proposal could measure both heart and respiratory rate.