Frequent blood glucose monitoring is crucial for patients with diabetes. However, current blood glucose measurement methods are primarily invasive and, thus, cause discomfort and infection risks. Accordingly, this study developed a noninvasive real-time glucose monitoring system based on a deep learning (DL) model (comprising convolutional neural network and long short-term memory network models) and multiwavelength near-infrared (NIR) light technology. The system is equipped with a portable finger gripper with NIR light-emitting diodes emitting at three distinct wavelengths (810, 860, and 940 nm) to illuminate the finger. A triad spectroscopy sensor is then used to capture finger photoplethysmography (PPG) signals within only 6 s. The captured signals are then transmitted to a server-side DL model for glucose prediction. This DL model analyzes the three-wavelength PPG data to predict a user’s blood glucose value. The predicted glucose value is subsequently displayed on a dedicated smartphone app. In contrast to previously proposed systems relying on machine learning for feature extraction, the proposed system uses a DL model to automatically extract glucose-related features from the three-band PPG signals, ultimately leading to blood glucose predictions with a root-mean-square error of 6.62. Furthermore, the proposed system prioritizes user comfort, portability, and stability, thereby offering a convenient and accessible blood glucose monitoring experience.

Although high heels are commonly worn for their perceived aesthetic value, they can have negative effects on the health of the foot and lower limbs. Wearing excessively high heels for long periods can negatively affect the wearer's ankles, knees, and spine. In this study, a smartphone app based on the You Only Look Once (YOLO) v8 model was developed to provide users with personalized maximum height recommendations for high-heeled shoes to reduce these health risks. Specifically, YOLOv8n identified heels in videos of participants transitioning from standing flat-footed to tip-toe, and a weighting algorithm was used to identify the maximum appropriate high-heel height. Extreme values were suppressed to ensure that the predicted value was safe and sufficiently conservative. The system was optimized for rapid prediction, and strategies for ensuring accurate predictions against backgrounds of different colors were developed. The system achieved a lower mean absolute error (MAE) of 4.33 mm on white backgrounds than previous studies and is the first reported system to also achieve high accuracy for backgrounds of other colors, namely green and black backgrounds with MAEs of 5.25 and 6.84 mm, respectively. The system has excellent performance and practical applicability. Moreover, unlike our previously developed client-server system, this lightweight system can be implemented locally on a smartphone, greatly improving convenience and privacy.

Long-term poor sitting posture leads to physical injuries such as muscle soreness and waist and neck alignment problems. In this study, we proposed an intelligent sitting posture detection system that uses depth cameras fixed on a chair to capture depth images of the user's sitting posture, and then applies a trained artificial intelligence (AI) model on an embedded Raspberry Pi board to recognize the user's sitting posture from the image data. Finally, through Bluetooth on the Raspberry Pi, the results are sent to the user's smartphone application for display and recording to achieve rapid detection of sitting posture and warning of poor sitting posture. The contribution of this study is its use of two depth cameras mounted on a chair, thereby eliminating the problem of cumbersome sensors that compromise user comfort or are prone to damage. The detection of the user's entire sitting posture was completed on an edge computing platform, which leads to power savings and offers privacy protection. Furthermore, because of the low battery power usage, the system is portable. To perform quick AI calculations, we developed a lightweight EfficientNet model and programmed it for the Raspberry Pi. The system achieved an accuracy of 99.71% and an execution speed of almost one posture result per second.

Electrocardiogram (ECG) is one of the most important information for cardiovascular diseases (CVDs) diagnosis. In recent year, several dry electrode-based smart clothes have been widely developed to improve the skin allergic reaction and gel-drying issue from conventional Ag/AgCl electrode under long-term measurement. However, most of these dry electrodes still have to contact with skin and may encounter the risk of skin irritation, and many smart clothing systems lack of automatic CVDs detection. In this article, a novel smart clothing was designed to automatically detect CVDs in daily life. Based on the technique of capacitive electrodes, the proposed smart clothing could access the bio-potential across the clothes to prevent the skin from irritation and discomfort, and could adapt to different body sizes by the specific belt mechanical design. Moreover, the CVDs detection algorithm was also designed and implemented in the field programmable gate array (FPGA) based ECG analysis module. The experiment results show that the proposed smart clothing could effectively real-time extract ECG features (P-, R-, and T-waves) and detect CVDs state via the front-end circuit, including bradycardia, tachycardia, atrial fibrillation, left ventricular hypertrophy, first-degree atrioventricular block, and hyperkalemia. The proposed FPGA architecture is also beneficial for future revisions or additions of CVD algorithms to improve more accurate diagnosis and monitoring of heart disease. It might reduce huge ECG data collected in daily life via only transmitting the abnormal ECG segment, and improve the diagnostic efficiency of CVDs in the future.

Arteriosclerosis is a cardiovascular disease that can cause calcification, sclerosis, stenosis, or obstruction of blood vessels and may further cause abnormal peripheral blood perfusion or other complications. In clinical settings, several approaches, such as computed tomography angiography and magnetic resonance angiography, can be used to evaluate arteriosclerosis status. However, these approaches are relatively expensive and require an experienced operator and often the injection of a contrast agent. In this article, a novel smart assistance system based on near-infrared spectroscopy was proposed that can noninvasively assess blood perfusion and thus indicate arteriosclerosis status. In this system, a wireless peripheral blood perfusion monitoring device simultaneously monitors changes in hemoglobin parameters and the cuff pressure applied by a sphygmomanometer. Several indexes extracted from changes in hemoglobin parameters and cuff pressure were defined and can be used to estimate blood perfusion status. A neural network model for arteriosclerosis evaluation was constructed using the proposed system. The relationship between the blood perfusion indexes and arteriosclerosis status was investigated, and the neural network model for arteriosclerosis evaluation was validated. Experimental results indicated that the differences in many blood perfusion indexes for different groups were significant and that the neural network model could effectively evaluate arteriosclerosis status (accuracy = 80.26%). By using a sphygmomanometer, the model can be employed for simple arteriosclerosis screening and blood pressure measurements. The model offers real-time noninvasive measurement, and the system is relatively inexpensive and easy to operate.

Bedside falls and pressure ulcers are crucial issues in geriatric care. Although many bedside monitoring systems have been proposed, they are limited by the computational complexity of their algorithms. Moreover, most of the data collected by the sensors of these systems must be transmitted to a back-end server for calculation. With an increase in the demand for the Internet of Things, problems such as higher cost of bandwidth and overload of server computing are faced when using the aforementioned systems. To reduce the server workload, certain computing tasks must be offloaded from cloud servers to edge computing platforms. In this study, a bedside monitoring system based on neuromorphic computing hardware was developed to detect bedside falls and sleeping posture. The artificial intelligence neural network executed on the back-end server was simplified and used on an edge computing platform. An integer 8-bit-precision neural network model was deployed on the edge computing platform to process the thermal image captured by the thermopile array sensing element to conduct sleep posture classification and bed position detection. The bounding box of the bed was then converted into the features for posture classification correction to correct the posture. In an experimental evaluation, the accuracy rate, inferencing speed, and power consumption of the developed system were 94.56%, 5.28 frames per second, and 1.5 W, respectively. All the calculations of the developed system are conducted on an edge computing platform, and the developed system only transmits fall events to the back-end server through Wi-Fi and protects user privacy.

Speech is easily affected by different background noise in real environment to reduce the speech intelligibility, in particular, for hearing impaired listeners. In order to improve the above issue, several hearing aids have been developed to enhance the speech signal in noisy environment. Most of current hearing aids were designed to enhance the component of speech and suppress the component of noise. However, it is difficult to separate other speech sources. Adaptive signal enhancement with the beamforming technique might improve the above issue. However, how to distinguish the location of the desired speaker effectively is still a difficult challenge for adaptive beamforming method. A novel concept of hearing aid was proposed in this study. Different from the beamforming-based hearing aids, which use the crosscorrelation-coefficient method to estimate time difference of arrival (TDOA), an image recognition technology was used to estimate the location of the desired speaker to obtain the more precise TDOA. An adaptive signal enhancement was also used to enhance the noisy speech sound. From the experimental results, the proposed system could provide a smaller absolute error of TDOA less than 1.25 × 10-4ms, and a clear speech sound from the target speaker who the user wants to listen to.

Diabetes may cause different foot problems, which could easily lead to infection, ulcers, and increasing risk of amputation due to nerve or vascular injury. In order to reduce the risk of amputation, Buerger's exercise is frequently used for rehabilitation to improve the blood circulation in lower limbs. However, it is difficult to evaluate the rehabilitation efficiency with Buerger's exercise objectively. In this study, a novel non-invasively optical system is developed to non-invasively monitor the change of the foot blood circulation before and after long-term Buerger's exercise. Radial basis function neural network is also used for classifying the healthy and diabetic groups from the change of relative total hemoglobin (HbT) concentration and tissue oxygen saturation (StO2) and providing an index to evaluate the rehabilitation efficiency with Buerger's exercise. Finally, the experimental results show that the relative HbT concentration and StO2 in lower limbs corresponding to different groups are significantly different and could be used as the factors for the classification of healthy subjects and diabetic foot patients. Moreover, the tendency of the relative HbT concentration and StO2 rise after the long-term rehabilitation with Buerger's exercise.

Recently, 2-D/3-D switchable displays have become the mainstream in 3-D display technologies, and people can now watch 3-D movies with a naked 2-D/3-D switchable display at home. However, some studies have indicated that people might encounter visual fatigue after enjoying a 3-D film in the theater. Although 2-D/3-D switchable technologies have been widely developed, 3-D display technologies are still lacking in ergonomic and human-care factors such as reducing visual fatigue. This study proposes a novel 2-D/3-D display autoadjustment switch system to provide biofeedback functions to reduce users' visual fatigue. In addition, the relationship between the blink rate and the visual fatigue state while watching 3-D films was investigated and quantified. In this study, liquid crystal barrier technology was used to develop a 2-D/3-D switchable display, and a wearable EOG acquisition device was also designed to monitor electro-oculography signals to estimate the blink rate. Here, the 2-D/3-D display autoadjustment criterion of the proposed system was designed according to the change in the visual fatigue state as estimated from the blink rate. Finally, the experimental results show that the proposed system could effectively reduce users' visual fatigue while watching 3-D films.

Subjects with amyotrophic lateral sclerosis (ALS) consistently experience decreasing quality of life because of this distinctive disease. Thus, a practical brain-computer interface (BCI) application can effectively help subjects with ALS to participate in communication. In practices, the noise would greatly reduce the performance of BCIs. In this study, minima controlled recursive averaging is applied to suppress noise and improve the performance of practical BCI applications. Minima controlled recursive averaging is used to correctively track the noise. To suppress these noises, a log-spectral amplitude estimator is selected as the gain function and used to effectively estimate the power spectrum of the noises. Eight subjects were asked to attend a performance test of the proposed approach and the canonical correlation analysis (CCA) was adopted to compare the proposed approach. The average recognition rates based on single channel are 69.57% and 74.63% for CCA and proposed approach, respectively. The experimental results demonstrated that our approach is able to improve performance in practice.

The article proposed dual wavelength silicon based photo detector with multi-layer structure, was fabricated and characterized with dual wavelength enhanced operating. We proposed the dual wavelength response of the silicon based bio-photo-detector. It can be enhanced by introducing thin porous silicon layer as the base region of transistor. The device process of manufacture is suitable in design of visible-light sensitive bio-photo-detectors. The experimental results showed that the dual wavelength responses in the developed devices were enhanced as compared to the silicon-based homo-junction photo-detectors in optical parameters, which indicated that the developed bio-photo-detector showed potential for practical silicon based biomedical device and system applications.

Bowel sounds is an important physiological parameter of distinguishing the gastrointestinal motility dysfunction. Auscultation of bowel sounds provides a noninvasive way for clinical diagnosis, but it is also easily affected by environmental noise. In this study, a novel higher-order-statistics (HOS)-based fractal dimension algorithm was proposed for detecting noisy bowel sounds. By using the nature of preserving non-Gaussianity for higher order statistics technique, the proposed method can effectively detect bowel sounds under different noise conditions, and its performance is insensitive to the change of noise type and noise level.

Oral-feeding disorder is common in preterm infants. It not only shows the adverse effect for growth and neurodevelopment in clinical but also becomes one of the important indicators of high-risk group for neurodevelopment delay in preterm infants. Preterm infants must coordinate the motor patterns of sucking, swallowing, and respiration skillfully to avoid choking, aspiration, oxygen desaturation, bradycardia, or apnea episodes. However, up to now, the judgment and classification severity in preterm infants are mostly subjective and phasic evaluations. Directly monitoring the coordination of sucking-swallowing-breathing during oral feeding simultaneously is difficult for preterm infants. In this study, we proposed a wireless oral-feeding monitoring system for preterm infants to quantitatively monitor the sucking pressure via a designed sucking pressure sensing device, swallowing activity via a microphone to detect swallowing sound, and diaphragmatic breathing movement via surface electromyogram. Moreover, a sucking-swallowing-breathing detection algorithm is also proposed to evaluate the events of sucking-swallowing-breathing activities. Furthermore, verification of the accuracy and rationality of oral-feeding parameters with clinical findings including sucking, swallowing, and breathing in term and preterm infants had proved the practicality and value of the proposed system.

Near-infrared spectroscopy (NIRS) is a modern measuring technology in neuroscience. It can be used to noninvasively measure the relative concentrations of oxyhemoglobin (OxyHb) and deoxyhemoglobin (DeoHb), which can reflect information related to cerebral blood volume and cerebral oxygen saturation. Therefore, it has the potential for noninvasive monitoring of cerebral ischemia. However, there is still a lack of reliable physiological information on the relationship between the concentrations of OxyHb and DeoHb in cerebral blood and the exact hypoxic state of brain cells under cerebral ischemia. In this study, we describe a wireless multichannel NIRS system, which we designed to noninvasively monitor the relative concentrations of OxyHb and DeoHb in bilateral cerebral blood before, during, and after middle cerebral artery occlusion. By comparing the results with the lactate/pyruvate ratio measured by microdialysis, we investigated the correlation between the relative concentrations of OxyHb and DeoHb in cerebral blood and the hypoxic state of brain cells. The results showed that the relationship between the concentration changes of DeoHb in cerebral blood and the hypoxic state of brain cells was significant. Therefore, by monitoring the changes in concentrations of DeoHb, the wireless NIRS can be used to estimate the hypoxic state of brain cells indirectly.

Auscultation of bowel sounds provides a noninvasive method to the diagnosis of gastrointestinal motility diseases. However, bowel sounds can be easily contaminated by background noises, and the frequency band of bowel sounds is easily overlapped with background noise. Therefore, it is difficult to enhance the noisy bowel sounds by using precise digital filters. In this study, a higher order statistics (HOS)-based radial basis function (RBF) network was proposed to enhance noisy bowel sounds. An HOS technique provides the ability of suppressing Gaussian noises and symmetrically distributed non-Gaussian noises due to their natural tolerance. Therefore, the influence of additional noises on the HOS-based learning algorithm can be reduced effectively. The simulated and experimental results show that the HOS-based RBF can exactly provide better performance for enhancing bowel sounds under stationary and nonstationary Gaussian noises. Therefore, the HOS-based RBF can be considered as a good approach for enhancing noisy bowel sounds.

In this study, a real-time wireless embedded EEG-based brain computer interface (BCI) system was developed for drowsiness detection in a realistic driving task. Accidents caused by driver's drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the driver's abilities of perception, recognition, and vehicle control abilities while sleepy. Therefore, real-time drowsiness monitoring is important to avoid traffic accidents. In this study, an embedded EEG-based BCI system which includes a wireless physiological signal acquisition module and an embedded signal processing module was designed, and a real-time drowsiness detection algorithm based on our unsupervised approach was implemented in the embedded signal processing module. EEG signal would be monitored and analyzed by the embedded signal processing module, and the warning tone would be triggered to prevent traffic accidents when the drowsiness condition occurred.

This study presents a novel wireless, ambulatory, real-time, and autoalarm intelligent telecardiology system to improve healthcare for cardiovascular disease, which is one of the most prevalent and costly health problems in the world. This system consists of a lightweight and power-saving wireless ECG device equipped with a built-in automatic warning expert system. This device is connected to a mobile and ubiquitous real-time display platform. The acquired ECG signals are instantaneously transmitted to mobile devices, such as netbooks or mobile phones through Bluetooth, and then, processed by the expert system. An alert signal is sent to the remote database server, which can be accessed by an Internet browser, once an abnormal ECG is detected. The current version of the expert system can identify five types of abnormal cardiac rhythms in real-time, including sinus tachycardia, sinus bradycardia, wide QRS complex, atrial fibrillation (AF), and cardiac asystole, which is very important for both the subjects who are being monitored and the healthcare personnel tracking cardiac-rhythm disorders. The proposed system also activates an emergency medical alarm system when problems occur. Clinical testing reveals that the proposed system is approximately 94% accurate, with high sensitivity, specificity, and positive prediction rates for ten normal subjects and 20 AF patients. We believe that in the future a business-card-like ECG device, accompanied with a mobile phone, can make universal cardiac protection service possible.

In this paper, a higher-order-statistics (HOS)-based radial basis function (RBF) network for signal enhancement is introduced. In the proposed scheme, higher order cumulants of the reference signal were used as the input of HOS-based RBF. An HOS-based supervised learning algorithm, with mean square error obtained from higher order cumulants of the desired input and the system output as the learning criterion, was used to adapt weights. The motivation is that the HOS can effectively suppress Gaussian and symmetrically distributed non-Gaussian noise. The influence of a Gaussian noise on the input of HOS-based RBF and the HOS-based learning algorithm can be mitigated. Simulated results indicate that HOS-based RBF can provide better performance for signal enhancement under different noise levels, and its performance is insensitive to the selection of learning rates. Moreover, the efficiency of HOS-based RBF under the nonstationary Gaussian noise is stable.

This paper demonstrates the design and implementation of a real-time wireless physiological monitoring system for nursing centers, whose function is to monitor online the physiological status of aged patients via wireless communication channel and wired local area network. The collected data, such as body temperature, blood pressure, and heart rate, can then be stored in the computer of a network management center to facilitate the medical staff in a nursing center to monitor in real time or analyze in batch mode the physiological changes of the patients under observation. Our proposed system is bidirectional, has low power consumption, is cost effective, is modular designed, has the capability of operating independently, and can be used to improve the service quality and reduce the workload of the staff in a nursing center.