Efficient video-based breathing pattern and respiration rate monitoring for remote health monitoring

Flexible Chitosan-Based Capacitive Humidity Sensors for Respiratory Monitoring

As one of the most important human health indicators, respiratory status is an important basis for the diagnosis of many diseases. However, the high cost of respiratory monitoring makes its use uncommon. This study introduces a low-cost, wearable, flexible humidity sensor for respiratory monitoring. Solution-processed chitosan (CS) placed on a polyethylene terephthalate substrate was used as the sensing layer. An Arduino circuit board was used to read humidity-sensitive voltage changes. The CS-based sensor demonstrated capacitive humidity sensitivity, whereby the capacitance instantly increased from 10-2 to 30 nF when the environmental humidity changed from 43% to 97%. The capacitance logarithm sensitivity and response voltage change was 35.9 pF/%RH and 0.8 V in the RH range from 56% to 97%. And the voltage variation between inhalation and exhalation was ~0.5 V during normal breathing. A rapid response time of ~0.7 s and a recovery time of ~2 s were achieved during respiration testing. Breathing modes (i.e., normal breathing, rest breathing, deep breathing, and fast breathing) and tonal changes during speech could be clearly distinguished. Therefore, such sensors provide a means for economical and convenient wearable respiratory monitoring, and they have the potential to be used for daily health examinations and professional medical diagnoses.

Face-Free Chest Detection Using Convolutional Neural Networks for Non-Contact Respiration Monitoring

Non-contact methods for monitoring respiration face limitations when it comes to selecting the chest region of interest. The semi-automatic method, which requires the user to select the chest region in the first frame, is not suitable for real-time applications. The automatic method, which tracks the face first and then detects the chest region based on the face's position, can be inaccurate if the face is not visible or is rotated. Moreover, using the face region to track the chest region can under-utilize camera pixels since the face is not essential for monitoring respiration. This approach may adversely affect the quality of the respiration signal being measured. To address these issues, we propose a face-free chest detection model based on Convolutional Neural Networks. Our model enhances the measured non-contact respiration signal quality and utilizes more pixels for the chest region alone. In our quantitative study, we demonstrate that our method outperforms traditional methods that require the presence of the face. This approach offers potential benefits for real-time, non-contact respiration monitoring applicationsClinical relevance- This work enhances the performance of non-contact respiration monitoring techniques by precisely detecting the chest region without the need of face in it through a CNN-based model. The use of the CNN-based chest detection model also enhances the real-time monitoring capabilities of non-contact respiration monitoring techniques.

A BLE-Connected Piezoresistive and Inertial Chest Band for Remote Monitoring of the Respiratory Activity by an Android Application: Hardware Design and Software Optimization

Breathing is essential for human life. Issues related to respiration can be an indicator of problems related to the cardiorespiratory system; thus, accurate breathing monitoring is fundamental for establishing the patient’s condition. This paper presents a ready-to-use and discreet chest band for monitoring the respiratory parameters based on the piezoresistive transduction mechanism. In detail, it relies on a strain sensor realized with a pressure-sensitive fabric (EeonTex LTT-SLPA-20K) for monitoring the chest movements induced by respiration. In addition, the band includes an Inertial Measurement Unit (IMU), which is used to remove the motion artefacts from the acquired signal, thereby improving the measurement reliability. Moreover, the band comprises a low-power conditioning and acquisition section that processes the signal from sensors, providing a reliable measurement of the respiration rate (RR), in addition to other breathing parameters, such as inhalation (TI) and exhalation (TE) times, inhalation-to-exhalation ratio (IER), and flow rate (V). The device wirelessly transmits the extracted parameters to a host device, where a custom mobile application displays them. Different test campaigns were carried out to evaluate the performance of the designed chest band in measuring the RR, by comparing the measurements provided by the chest band with those obtained by breath count. In detail, six users, of different genders, ages, and physical constitutions, were involved in the tests. The obtained results demonstrated the effectiveness of the proposed approach in detecting the RR. The achieved performance was in line with that of other RR monitoring systems based on piezoresistive textiles, but which use more powerful acquisition systems or have low wearability. In particular, the inertia-assisted piezoresistive chest band obtained a Pearson correlation coefficient with respect to the measurements based on breath count of 0.96 when the user was seated. Finally, Bland–Altman analysis demonstrated that the developed system obtained 0.68 Breaths Per Minute (BrPM) mean difference (MD), and Limits of Agreement (LoAs) of +3.20 and −1.75 BrPM when the user was seated.

Deploying Machine Learning Techniques for Human Emotion Detection

Emotion recognition is one of the trending research fields. It is involved in several applications. Its most interesting applications include robotic vision and interactive robotic communication. Human emotions can be detected using both speech and visual modalities. Facial expressions can be considered as ideal means for detecting the persons' emotions. This paper presents a real-time approach for implementing emotion detection and deploying it in the robotic vision applications. The proposed approach consists of four phases: preprocessing, key point generation, key point selection and angular encoding, and classification. The main idea is to generate key points using MediaPipe face mesh algorithm, which is based on real-time deep learning. In addition, the generated key points are encoded using a sequence of carefully designed mesh generator and angular encoding modules. Furthermore, feature decomposition is performed using Principal Component Analysis (PCA). This phase is deployed to enhance the accuracy of emotion detection. Finally, the decomposed features are enrolled into a Machine Learning (ML) technique that depends on a Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), or Random Forest (RF) classifier. Moreover, we deploy a Multilayer Perceptron (MLP) as an efficient deep neural network technique. The presented techniques are evaluated on different datasets with different evaluation metrics. The simulation results reveal that they achieve a superior performance with a human emotion detection accuracy of 97%, which ensures superiority among the efforts in this field.

Secure Health Monitoring Communication Systems Based on IoT and Cloud Computing for Medical Emergency Applications

Smart health surveillance technology has attracted wide attention between patients and professionals or specialists to provide early detection of critical abnormal situations without the need to be in direct contact with the patient. This paper presents a secure smart monitoring portable multivital signal system based on Internet-of-Things (IoT) technology. The implemented system is designed to measure the key health parameters: heart rate (HR), blood oxygen saturation (SpO₂), and body temperature, simultaneously. The captured physiological signals are processed and encrypted using the Advanced Encryption Standard (AES) algorithm before sending them to the cloud. An ESP8266 integrated unit is used for processing, encryption, and providing connectivity to the cloud over Wi-Fi. On the other side, trusted medical organization servers receive and decrypt the measurements and display the values on the monitoring dashboard for the authorized specialists. The proposed system measurements are compared with a number of commercial medical devices. Results demonstrate that the measurements of the proposed system are within the 95% confidence interval. Moreover, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Relative Error (MRE) for the proposed system are calculated as 1.44, 1.12, and 0.012, respectively, for HR, 1.13, 0.92, and 0.009, respectively, for SpO₂, and 0.13, 0.11, and 0.003, respectively, for body temperature. These results demonstrate the high accuracy and reliability of the proposed system.

An Overview of Wearable Piezoresistive and Inertial Sensors for Respiration Rate Monitoring

The demand for wearable devices to measure respiratory activity is constantly growing, finding applications in a wide range of scenarios (e.g., clinical environments and workplaces, outdoors for monitoring sports activities, etc.). Particularly, the respiration rate (RR) is a vital parameter since it indicates serious illness (e.g., pneumonia, emphysema, pulmonary embolism, etc.). Therefore, several solutions have been presented in the scientific literature and on the market to make RR monitoring simple, accurate, reliable and noninvasive. Among the different transduction methods, the piezoresistive and inertial ones satisfactorily meet the requirements for smart wearable devices since unobtrusive, lightweight and easy to integrate. Hence, this review paper focuses on innovative wearable devices, detection strategies and algorithms that exploit piezoresistive or inertial sensors to monitor the breathing parameters. At first, this paper presents a comprehensive overview of innovative piezoresistive wearable devices for measuring user’s respiratory variables. Later, a survey of novel piezoresistive textiles to develop wearable devices for detecting breathing movements is reported. Afterwards, the state-of-art about wearable devices to monitor the respiratory parameters, based on inertial sensors (i.e., accelerometers and gyroscopes), is presented for detecting dysfunctions or pathologies in a non-invasive and accurate way. In this field, several processing tools are employed to extract the respiratory parameters from inertial data; therefore, an overview of algorithms and methods to determine the respiratory rate from acceleration data is provided. Finally, comparative analysis for all the covered topics are reported, providing useful insights to develop the next generation of wearable sensors for monitoring respiratory parameters.

Non-Contact Video-Based Assessment of the Respiratory Function Using a RGB-D Camera

A fully automatic, non-contact method for the assessment of the respiratory function is proposed using an RGB-D camera-based technology. The proposed algorithm relies on the depth channel of the camera to estimate the movements of the body’s trunk during breathing. It solves in fixed-time complexity, O(1), as the acquisition relies on the mean depth value of the target regions only using the color channels to automatically locate them. This simplicity allows the extraction of real-time values of the respiration, as well as the synchronous assessment on multiple body parts. Two different experiments have been performed: a first one conducted on 10 users in a single region and with a fixed breathing frequency, and a second one conducted on 20 users considering a simultaneous acquisition in two regions. The breath rate has then been computed and compared with a reference measurement. The results show a non-statistically significant bias of 0.11 breaths/min and 96% limits of agreement of −2.21/2.34 breaths/min regarding the breath-by-breath assessment. The overall real-time assessment shows a RMSE of 0.21 breaths/min. We have shown that this method is suitable for applications where respiration needs to be monitored in non-ambulatory and static environments.

Optics in Africa: introduction

Africa has a long history in optics, but decades of turmoil have seen optical science in Africa advance only slowly, punching far below its weight. But a younger generation of scientists hold promise for the brighter future, addressing continental issues with photonics. In this Feature Issue on Optics in Africa we capture some of the exciting optical research from across the continent in 51 research reports, covering both fundamental and applied topics. The issue is supplemented by invited review articles that offer authoritative perspectives on the historical development of key research fields, from early advances in lasers to present-day progress in photonic materials. To encourage the exploration of new research directions, the issue has several tutorial articles that lower the entry barrier for emerging researchers, while highlighting the scope of research on the continent and its international context.