Mental Stress Detection Using a Wearable In-Ear Plethysmography.

This study presents an ear-mounted photoplethysmography (PPG) system that is designed to detect mental stress. Mental stress is a prevalent condition that can negatively impact an individual's health and well-being. Early detection and treatment of mental stress are crucial for preventing related illnesses and maintaining overall wellness. The study used data from 14 participants that were collected in a controlled environment. The participants were subjected to stress-inducing tasks such as the Stroop color-word test and mathematical calculations. The raw PPG signal was then preprocessed and transformed into scalograms using continuous wavelet transform (CWT). A convolutional neural network classifier was then used to classify the transformed signals as stressed or non-stressed. The results of the study show that the PPG system achieved high levels of accuracy (92.04%) and F1-score (90.8%). Furthermore, by adding white Gaussian noise to the raw PPG signals, the results were improved even more, with an accuracy of 96.02% and an F1-score of 95.24%. The proposed ear-mounted device shows great promise as a reliable tool for the early detection and treatment of mental stress, potentially revolutionizing the field of mental health and well-being.

Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review.

Drowsiness is not only a core challenge to safe driving in traditional driving conditions but also a serious obstacle for the wide acceptance of added services of self-driving cars (because drowsiness is, in fact, one of the most representative early-stage symptoms of self-driving carsickness). In view of the importance of detecting drivers' drowsiness, this paper reviews the algorithms of electroencephalogram (EEG)-based drivers' drowsiness detection (DDD). To facilitate the review, the EEG-based DDD approaches are organized into a tree structure taxonomy, having two main categories, namely "detection only (open-loop)" and "management (closed-loop)", both aimed at designing better DDD systems that ensure early detection, reliability and practical utility. To achieve this goal, we addressed seven questions, the answers of which helped in developing an EEG-based DDD system that is superior to the existing ones. A basic assumption in this review article is that although driver drowsiness and carsickness-induced drowsiness are caused by different factors, the brain network that regulates drowsiness is the same.

Smart Wearables with Sensor Fusion for Fall Detection in Firefighting.

During the past decade, falling has been one of the top three causes of death amongst firefighters in China. Even though there are many studies on fall-detection systems (FDSs), the majority use a single motion sensor. Furthermore, few existing studies have considered the impact sensor placement and positioning have on fall-detection performance; most are targeted toward fall detection of the elderly. Unfortunately, floor cracks and unstable building structures in the fireground increase the difficulty of detecting the fall of a firefighter. In particular, the movement activities of firefighters are more varied; hence, distinguishing fall-like activities from actual falls is a significant challenge. This study proposed a smart wearable FDS for firefighter fall detection by integrating motion sensors into the firefighter's personal protective clothing on the chest, elbows, wrists, thighs, and ankles. The firefighter's fall activities are detected by the proposed multisensory recurrent neural network, and the performances of different combinations of inertial measurement units (IMUs) on different body parts were also investigated. The results indicated that the sensor fusion of IMUs from all five proposed body parts achieved performances of 94.10%, 92.25%, and 94.59% in accuracy, sensitivity, and specificity, respectively.

Affective Computing on Machine Learning-Based Emotion Recognition Using a Self-Made EEG Device.

In this research, we develop an affective computing method based on machine learning for emotion recognition using a wireless protocol and a wearable electroencephalography (EEG) custom-designed device. The system collects EEG signals using an eight-electrode placement on the scalp; two of these electrodes were placed in the frontal lobe, and the other six electrodes were placed in the temporal lobe. We performed experiments on eight subjects while they watched emotive videos. Six entropy measures were employed for extracting suitable features from the EEG signals. Next, we evaluated our proposed models using three popular classifiers: a support vector machine (SVM), multi-layer perceptron (MLP), and one-dimensional convolutional neural network (1D-CNN) for emotion classification; both subject-dependent and subject-independent strategies were used. Our experiment results showed that the highest average accuracies achieved in the subject-dependent and subject-independent cases were 85.81% and 78.52%, respectively; these accuracies were achieved using a combination of the sample entropy measure and 1D-CNN. Moreover, our study investigates the T8 position (above the right ear) in the temporal lobe as the most critical channel among the proposed measurement positions for emotion classification through electrode selection. Our results prove the feasibility and efficiency of our proposed EEG-based affective computing method for emotion recognition in real-world applications.

Artificial Intelligence-Based Optimal Grasping Control.

A new tactile sensing module was proposed to sense the contact force and location of an object on a robot hand, which was attached on the robot finger. Three air pressure sensors are installed at the tip of the finger to detect the contacting force at the points. To obtain a nominal contact force at the finger from data from the three air pressure sensors, a force estimation was developed based upon the learning of a deep neural network. The data from the three air pressure sensors were utilized as inputs to estimate the contact force at the finger. In the tactile module, the arrival time of the air pressure sensor data has been utilized to recognize the contact point of the robot finger against an object. Using the three air pressure sensors and arrival time, the finger location can be divided into 3 × 3 block locations. The resolution of the contact point recognition was improved to 6 × 4 block locations on the finger using an artificial neural network. The accuracy and effectiveness of the tactile module were verified using real grasping experiments. With this stable grasping, an optimal grasping force was estimated empirically with fuzzy rules for a given object.

Sensor Fusion of Motion-Based Sign Language Interpretation with Deep Learning.

Sign language was designed to allow hearing-impaired people to interact with others. Nonetheless, knowledge of sign language is uncommon in society, which leads to a communication barrier with the hearing-impaired community. Many studies of sign language recognition utilizing computer vision (CV) have been conducted worldwide to reduce such barriers. However, this approach is restricted by the visual angle and highly affected by environmental factors. In addition, CV usually involves the use of machine learning, which requires collaboration of a team of experts and utilization of high-cost hardware utilities; this increases the application cost in real-world situations. Thus, this study aims to design and implement a smart wearable American Sign Language (ASL) interpretation system using deep learning, which applies sensor fusion that "fuses" six inertial measurement units (IMUs). The IMUs are attached to all fingertips and the back of the hand to recognize sign language gestures; thus, the proposed method is not restricted by the field of view. The study reveals that this model achieves an average recognition rate of 99.81% for dynamic ASL gestures. Moreover, the proposed ASL recognition system can be further integrated with ICT and IoT technology to provide a feasible solution to assist hearing-impaired people in communicating with others and improve their quality of life.

Monitoring Transepidermal Water Loss and Skin Wettedness Factor with Battery-Free NFC Sensor.

The transepidermal water loss (TEWL) and the skin wettedness factor (SWF) are considered parts of a key perspective related to skincare. The former is used to determine the loss of water content from the stratum corneum (SC), while the latter is used to determine the human skin comfort level. Herein, we developed two novel approaches: (1) determination of the TEWL and the SWF based on a battery-free humidity sensor, and (2) the design of a battery-free smart skincare sensor device tag that can harvest energy from a near field communication (NFC)-enabled smartphone, making it a battery-free design approach. The designed skincare device tag has a diameter of 2.6 cm and could harvest energy (~3 V) from the NFC-enabled smartphone. A series of experimental tests involving the participation of eight and six subjects were conducted in vivo for the indoor and outdoor environments, respectively. During the experimental analysis, the skin moisture content level was measured at different times of the day using an android smartphone. The TEWL and SWF values were calculated based on these sensor readings. For the TEWL case: if the skin moisture is high, the TEWL is high, and if the skin moisture is low, the TEWL is low, ensuring that the skin moisture and the TEWL follow the same trend. Our smart skincare device is enclosed in a 3D flexible design print, and it is battery-free with an android application interface that is more convenient to carry outside than other commercially available battery-based devices.

Battery-Free and Noninvasive Estimation of Food pH and CO2 Concentration for Food Monitoring Based on Pressure Measurement.

In this paper, we developed a battery-free system that can be used to estimate food pH level and carbon dioxide (CO2) concentration in a food package from headspace pressure measurement. While being stored, food quality degrades gradually as a function of time and storage conditions. A food monitoring system is, therefore, essential to prevent the detrimental problems of food waste and eating spoilt food. Since conventional works that invasively measure food pH level and CO2 concentration in food packages have shown several disadvantages in terms of power consumption, system size, cost, and reliability, our study proposes a system utilizing package headspace pressure to accurately and noninvasively extract food pH level and CO2 concentration, which reflection food quality. To read pressure data in the food container, a 2.5 cm × 2.5 cm smart sensor tag was designed and integrated with near-field communication (NFC)-based energy harvesting technology for battery-free operation. To validate the reliability of the proposed extraction method, various experiments were conducted with different foods, such as pork, chicken, and fish, in two storage environments. The experimental results show that the designed system can operate in a fully passive mode to communicate with an NFC-enabled smartphone. High correlation coefficients of the headspace pressure with the food pH level and the headspace CO2 concentration were observed in all experiments, demonstrating the ability of the proposed system to estimate food pH level and CO2 concentration with high accuracy. A linear regression model was then trained to linearly fit the sensor data. To display the estimated results, we also developed an Android mobile application with an easy-to-use interface.

A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals.

The goal of this study was to develop and validate a hybrid brain-computer interface (BCI) system for home automation control. Over the past decade, BCIs represent a promising possibility in the field of medical (e.g., neuronal rehabilitation), educational, mind reading, and remote communication. However, BCI is still difficult to use in daily life because of the challenges of the unfriendly head device, lower classification accuracy, high cost, and complex operation. In this study, we propose a hybrid BCI system for home automation control with two brain signals acquiring electrodes and simple tasks, which only requires the subject to focus on the stimulus and eye blink. The stimulus is utilized to select commands by generating steady-state visually evoked potential (SSVEP). The single eye blinks (i.e., confirm the selection) and double eye blinks (i.e., deny and re-selection) are employed to calibrate the SSVEP command. Besides that, the short-time Fourier transform and convolution neural network algorithms are utilized for feature extraction and classification, respectively. The results show that the proposed system could provide 38 control commands with a 2 s time window and a good accuracy (i.e., 96.92%) using one bipolar electroencephalogram (EEG) channel. This work presents a novel BCI approach for the home automation application based on SSVEP and eye blink signals, which could be useful for the disabled. In addition, the provided strategy of this study-a friendly channel configuration (i.e., one bipolar EEG channel), high accuracy, multiple commands, and short response time-might also offer a reference for the other BCI controlled applications.

Lightweight multi-hop VLC using compression and data-dependent multiple pulse modulation.

Particulate matter (PM) has a diameter of few micrometers, which causes different illnesses. We used visible light communication (VLC) to transfer PM data to a user monitoring terminal in real-time. To reduce the time and power required for communication, we compressed the PM data. Subsequently, these compressed data were transmitted using a modulation technique called data-dependent multiple pulse position modulation (DDMPPM). We evaluate the performance of DDMPPM for multi-hop communication in VLC through practical experiments. For the same data set, DDMPPM utilizes a lesser frame to transfer PM data. Using DDMPPM, we achieved a total communication distance of 48 m.

Pressure Measurement-Based Method for Battery-Free Food Monitoring Powered by NFC Energy Harvesting.

A novel approach for battery-free food freshness monitoring is proposed and demonstrated in this study. The aim is to track the freshness of different sorts of food such as pork, chicken, and fish during storage. To eliminate the drawbacks of conventional food monitoring methods, which are normally based on measuring gas concentration emitted from food in a container, this approach measures the gradual increase in air pressure caused by the gas emission during storage. Additionally, we aim to design a smart sensor tag that can operate in fully passive mode without an external power source. To achieve this goal, near-field communication (NFC)-based energy harvesting is utilized in this work to achieve a self-powered operation of the sensor tag. To demonstrate the feasibility of the proposed method, experiments with the above-mentioned food were tested at room and refrigerated temperatures in 2 and 4 days, respectively. For each experiment, 200 g of the target food was placed in a 2-L container with the smart sensor tag. The experiments were conducted with both rigid and flexible containers to consider real food packaging environments. The air pressure inside the container was monitored as an indicator of food freshness by a sensitive pressure sensor on the smart sensor tag. The experimental results showed a remarkable increase in air pressure, which was able to be detected with high accuracy by the pressure sensor. The fabricated battery-free smart sensor tag is small (2.5 cm × 2.5 cm) and is capable of less than 1 mW of power consumption, which is ultra-low relative to other ordinary approaches that have a power consumption that normally surpasses 10 mW. The pressure value was used to classify food freshness into different levels on a mobile display to provide food freshness status using an NFC-enabled smartphone.

Fine particulate matter monitoring via a visible light communication in DCT-based optical OFDM.

An electromagnetic interference (EMI)-free wide-range indoor dust monitoring system that employs the optical orthogonal frequency-division multiplexing (OFDM)-based visible-light communication (VLC) is proposed. For the long-term transmission of dust information, VLC can be utilized even in EMI-restricted areas, such as medical centers, emergency rooms, and nursing homes. Discrete cosine transform-based optical OFDM is adopted to transmit a large amount of dust information. For robust light detection from eliminate ambient light and low-frequency noise, an average voltage-tracking technique is utilized and as a result LED illumination is detected over 18 m distance with reliable error rate. Wide-range dust information from multiple dust sensors are clearly displayed through the designed user interface. Users can then monitor the air quality in real-time, improving the environmental awareness of individuals.

Detection of Driver Braking Intention Using EEG Signals During Simulated Driving.

In this work, we developed a novel system to detect the braking intention of drivers in emergency situations using electroencephalogram (EEG) signals. The system acquired eight-channel EEG and motion-sensing data from a custom-designed EEG headset during simulated driving. A novel method for accurately labeling the training data during an extremely short period after the onset of an emergency stimulus was introduced. Two types of features, including EEG band power-based and autoregressive (AR)-based, were investigated. It turned out that the AR-based feature in combination with artificial neural network classifier provided better detection accuracy of the system. Experimental results for ten subjects indicated that the proposed system could detect the emergency braking intention approximately 600 ms before the onset of the executed braking event, with high accuracy of 91%. Thus, the proposed system demonstrated the feasibility of developing a brain-controlled vehicle for real-world applications.

Multimodal Wrist Biosensor for Wearable Cuff-less Blood Pressure Monitoring System.

We propose a multimodal biosensor for use in continuous blood pressure (BP) monitoring system. Our proposed novel configuration measures photo-plethysmography (PPG) and impedance plethysmography (IPG) signals simultaneously from the subject wrist. The proposed biosensor system enables a fully non-intrusive system that is cuff-less, also utilize a single measurement site for maximum wearability and convenience of the patients. The efficacy of the proposed technique was evaluated on 10 young healthy subjects. Experimental results indicate that the pulse transit time (PTT)-based features calculated from an IPG peak and PPG maximum second derivative (f14) had a relatively high correlation coefficient (r) to the reference BP, with -0.81 ± 0.08 and -0.78 ± 0.09 for systolic BP (SBP) and diastolic BP (DBP), respectively. Moreover, here we proposed two BP estimation models that utilize six- and one-point calibration models. The six-point model is based on the PTT, whereas the one-point model is based on the combined PTT and radial impedance (Z). Thus, in both models, we observed an adequate root-mean-square-error estimation performance, with 4.20 ± 1.66 and 2.90 ± 0.90 for SBP and DBP, respectively, with the PTT BP model; and 6.86 ± 1.65 and 6.67 ± 1.75 for SBP and DBP, respectively, with the PTT-Z BP model. This study suggests the possibility of estimating a subject's BP from only wrist bio-signals. Thus, the six- and one-point PTT-Z calibration models offer adequate performance for practical applications.

An Enhanced Multiplication of RF Energy Harvesting Efficiency Using Relay Resonator for Food Monitoring.

Recently, radio frequency (RF) energy harvesting (RFEH) has become a promising technology for a battery-less sensor module. The ambient RF radiation from the available sources is captured by receiver antennas and converted to electrical energy, which is used to supply smart sensor modules. In this paper, an enhanced method to improve the efficiency of the RFEH system using strongly coupled electromagnetic resonance technology was proposed. A relay resonator was added between the reader and tag antennas to improve the wireless power transmission efficiency to the sensor module. The design of the relay resonator was based on the resonant technique and near-field magnetic coupling concept to improve the communication distance and the power supply for a sensor module. It was designed such that the self-resonant frequencies of the reader antenna, tag antenna, and the relay resonator are synchronous at the HF frequency (13.56MHz). The proposed method was analyzed using Thevenin equivalent circuit, simulated and experimental validated to evaluate its performance. The experimental results showed that the proposed harvesting method is able to generate a great higher power up to 10 times than that provided by conventional harvesting methods without a relay resonator. Moreover, as an empirical feasibility test of the proposed RF energy harvesting device, a smart sensor module which is placed inside a meat box was developed. It was utilized to collect vital data, including temperature, relative humidity and gas concentration, to monitor the freshness of meat. Overall, by exploiting relay resonator, the proposed smart sensor tag could continuously monitor meat freshness without any batteries at the innovative maximum distance of approximately 50 cm.

High-accuracy VLC-based indoor positioning system using multi-level modulation.

In this study, a novel high-precision positioning algorithm was proposed by using Visible Light Communication (VLC) with only a simple and single receiver. The received voltage-level difference with multi-level modulation was adopted as the input variable, in order to minimize the negative consequences of noise. Then, the relationship between the received voltage-level difference, noise, and position was developed based on the optical propagation model. The minimum mean squares error algorithm and extended-Kalman filter was employed in order to improve the accuracy of the optical model and achieve high performance. Using the developed algorithm, high-accuracy results with a 0.9 cm average position error in the simulation and 2.56 cm average position error in the practical experiments were obtained.

Compressive Sensing of Cuff-less Biosensor for Energy-Efficient Blood Pressure Monitoring.

Compressive Sensing (CS) is an emerging technique in Internet of Medical Things (IoMT) application especially for smart wearable devices to prolong the sensor lifetime, and enable a continuous healthcare monitoring system. This paper describes the performance of CS on our wrist-based cuff-less biosensor for estimating blood pressure (BP) continuously. The proposed biosensor offers a novel BP estimation method by only using the biosignal from subject wrist. A CS technique is implemented to encrypt and reduce the data transmission load of the dual biosignal, which include impedance plethysmography (IPG) and photo plethysmography (PPG). Therefore, multiple compression ratio (CR) were tested to the original signal. We further compare the CS-based extracted features called pulse transit times (PTTs). Based on our experiments, CS-based PTT value that calculated from the IPG peak point to the PPG max2 point (F2), achieved the best correlation coefficient (R) of -0.85 and -0.43 to the systolic BP and diastolic BP, respectively. These results suggest that the implementation of CS on our proposed wrist biosensor is suitable for non-intrusive, yet long-term continuous BP monitoring.

Noninvasive Cuffless Blood Pressure Estimation Using Pulse Transit Time and Impedance Plethysmography.

OBJECTIVE: To demonstrate the feasibility of everaging impedance plethysmography (IPG) for detection of pulse transit time (PTT) and estimation of blood pressure (BP). METHODS: We first established the relationship between BP, PTT, and arterial impedance (i.e., the IPG observations). The IPG sensor was placed on the wrist while the photoplethysmography sensor was attached to the index finger to measure the PTT. With a cuff-based BP monitoring system placed on the upper arm as a reference, our proposed methodology was evaluated on 15 young, healthy human subjects leveraging handgrip exercises to manipulate BP/PTT and compared to several conventional PTT models to assess the efficacy of PTT/BP detections. RESULTS: The proposed model correlated with BP fairly well with group average correlation coefficients of [Formula: see text] for systolic BP (SBP) and [Formula: see text] for diastolic BP (DBP). In comparison with the other PTT methods, PTT-IPG-based BP estimation provided a lower root-mean-squared-error of [Formula: see text] and [Formula: see text] for SBP and DBP, respectively. CONCLUSION: We conclude that the measurement of arterial impedance via IPG methods is an adequate indicator to estimate BP. The proposed method appears to offer superiority compared to the conventional PTT estimation approaches. SIGNIFICANCE: Using impedance magnitude to estimate PTT offers promise to realize wearable and cuffless BP devices.

A Robust Bioimpedance Structure for Smartwatch-Based Blood Pressure Monitoring.

One potential method to estimate noninvasive cuffless blood pressure (BP) is pulse wave velocity (PWV), which can be calculated by using the distance and the transit time of the blood between two arterial sites. To obtain the pulse waveform, bioimpedance (BI) measurement is a promising approach because it continuously reflects the change in BP through the change in the arterial cross-sectional area. Many studies have investigated BI channels in a vertical direction with electrodes located along the wrist and the finger to calculate PWV and convert to BP; however, the measurement systems were relatively large in size. In order to reduce the total device size for use in a PWV-based BP smartwatch, this study proposed and examined a robust horizontal BI structure. The BI device was also designed to apply in a very small body area. The proposed structure was based on two sets of four electrodes attached around the wrist. Our model was evaluated on 15 human subjects; the PWV values were obtained with various distances between two BI channels to assess the efficacy. The results showed that the designed BI system can monitor pulse rate efficiently in only a 0.5 × 1.75 cm² area of the body. The correlation of pulse rate from the proposed design against the reference was 0.98 ± 0.07 (p < 0.001). Our structure yielded higher detection ratios for PWV measurements of 99.0 ± 2.2%, 99.0 ± 2.1%, and 94.8 ± 3.7% at 1, 2, and 3 cm between two BI channels, respectively. The measured PWVs correlated well with the BP standard device at 0.81 ± 0.08 and 0.84 ± 0.07 with low root-mean-squared-errors at 7.47 ± 2.15 mmHg and 5.17 ± 1.81 mmHg for SBP and DBP, respectively. The result demonstrates the potential of a new wearable BP smartwatch structure.

Single cell three-channel wavelength division multiplexing in visible light communication.

This paper examines the design of a prototype of a single cell three-channel visible light communication (VLC) based on wavelength division multiplexing for a radio frequency (RF)-free indoor healthcare. For a low complexity application, a single red green blue (RGB)-type white light-emitting diode (LED) and a single color sensor are adopted. An active low pass filter is utilized for robust light detection to eliminate ambient light and low frequency noise. The incoming tri-color lights are separated by an adopted color sensor and simultaneously demodulated by a receiver processor. Then, the collected data are monitored in real-time and analyzed to provide the necessary medical attention to the concerned patient.