In the clinic, the wheezing sound is usually considered as an indicator symptom to reflect the degree of airway obstruction. The auscultation approach is the most common way to diagnose wheezing ...sounds, but it subjectively depends on the experience of the physician. Several previous studies attempted to extract the features of breathing sounds to detect wheezing sounds automatically. However, there is still a lack of suitable monitoring systems for real-time wheeze detection in daily life. In this study, a wearable and wireless breathing sound monitoring system for real-time wheeze detection was proposed. Moreover, a breathing sounds analysis algorithm was designed to continuously extract and analyze the features of breathing sounds to provide the objectively quantitative information of breathing sounds to professional physicians. Here, normalized spectral integration (NSI) was also designed and applied in wheeze detection. The proposed algorithm required only short-term data of breathing sounds and lower computational complexity to perform real-time wheeze detection, and is suitable to be implemented in a commercial portable device, which contains relatively low computing power and memory. From the experimental results, the proposed system could provide good performance on wheeze detection exactly and might be a useful assisting tool for analysis of breathing sounds in clinical diagnosis.
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Stroke is an acute cerebrovascular condition causing damage to cranial nerves and requires subsequent rehabilitation treatment. In clinical practice, the effectiveness of rehabilitation is usually ...subjectively assessed by experienced physicians or using global prognostic scales. Several brain imaging techniques, such as positron emission tomography, functional magnetic resonance imaging, and computed tomography angiography, can be applied in rehabilitation effectiveness evaluation, but their complexity and long measurement times limit the activity of patients during measurement. This paper proposes an intelligent headband system based on near-infrared spectroscopy. An optical headband continuously and noninvasively monitors changes in hemoglobin parameters in the brain. The system's wearable headband and wireless transmission provide convenience of use. According to the change of hemoglobin parameters during rehabilitation exercise, several indexes were also defined to evaluate the state of cardiopulmonary function and further build the neural network model of the cardiopulmonary function evaluation. Finally, the relationship between the defined indexes and the cardiopulmonary function state were investigated and the neural network model for the cardiopulmonary function evaluation was also applied in the rehabilitation effect evaluation. The experimental results show the cardiopulmonary function state could reflect on most of the defined indexes and the output of neural network model, and the rehabilitation therapy could also improve the cardiopulmonary function.
Visually impaired people are often unaware of dangers in front of them, even in familiar environments. Furthermore, in unfamiliar environments, such people require guidance to reduce the risk of ...colliding with obstacles. This study proposes a simple smartphone-based guiding system for solving the navigation problems for visually impaired people and achieving obstacle avoidance to enable visually impaired people to travel smoothly from a beginning point to a destination with greater awareness of their surroundings. In this study, a computer image recognition system and smartphone application were integrated to form a simple assisted guiding system. Two operating modes, online mode and offline mode, can be chosen depending on network availability. When the system begins to operate, the smartphone captures the scene in front of the user and sends the captured images to the backend server to be processed. The backend server uses the faster region convolutional neural network algorithm or the you only look once algorithm to recognize multiple obstacles in every image, and it subsequently sends the results back to the smartphone. The results of obstacle recognition in this study reached 60%, which is sufficient for assisting visually impaired people in realizing the types and locations of obstacles around them.
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A standard 12-lead electrocardiogram (ECG) is an important tool in the diagnosis of heart diseases. Here, Ag/AgCl electrodes with conductive gels are usually used in a 12-lead ECG system to access ...biopotentials. However, using Ag/AgCl electrodes with conductive gels might be inconvenient in a prehospital setting. In previous studies, several dry electrodes have been developed to improve this issue. However, these dry electrodes have contact with the skin directly, and they might be still unsuitable for patients with wounds. In this study, a wearable 12-lead electrocardiogram monitoring system was proposed to improve the above issue. Here, novel noncontact electrodes were also designed to access biopotentials without contact with the skin directly. Moreover, by using the mechanical design, this system allows the user to easily wear and take off the device and to adjust the locations of the noncontact electrodes. The experimental results showed that the proposed system could exactly provide a good ECG signal quality even while walking and could detect the ECG features of the patients with myocardial ischemia, installation pacemaker, and ventricular premature contraction.
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This study proposed a novel assembled sensorized glove combining nine-axis inertial measurement units (IMUs) and force sensing resistors (FSRs) to simultaneously measure hand kinematics and fingertip ...force. The sensorized glove was designed to have high flexibility and extensibility. The accuracy and reliability of the hand kinematics measurements were verified using four-finger flexion-extension tasks. The results reveal that the mean absolute errors (MAEs) of the joint angles of fingers are all <; 5°, thus indicating that the glove can precisely measure hand kinematics. The MAE of the fingertip force prediction was 1.47 N, thus revealing that the FSRs can accurately measure the fingertip force. Moreover, comprehensive evaluation was conducted to prove that the sensorized glove can not only simultaneously measure the hand kinematics and fingertip force but also distinguish between the subjects with distinct hand functions. Therefore, the sensorized glove proposed in this study is reliable and has a strong potential for application in practical rehabilitation settings.
Sensory gloves convert hand postures and movements of fingers into electric signals. Different technologies can be adopted to achieve this conversion, and different approaches can be used to evaluate ...its effectiveness. In this study, we adopted two types of sensory gloves based on two types of sensors, namely the Resistive Flex Sensor (RFS) and the Inertial Measurement Unit (IMU). We evaluated the conversion effectiveness in terms of repeatability, reproducibility and reliability of quasi-static measurements. In particular, to take into account the nonlinear characteristics of sensors, we propose an improvement of the usually adopted measurement test protocol used for assessing the performance of sensory gloves. According to our results, the two sensory gloves have similar reliability. However, the IMU-based glove provides better repeatability, and the RFS-based glove provides better reproducibility. Overall, the average range ± standard deviation and intraclass correlation coefficient of the RFS-based glove were 5.66° ± 2.22° and 0.73 ± 0.17, respectively, and those of the IMU-based glove were 7.80° ± 2.47° and 0.76 ± 0.14, respectively. All in all, the novelty of this work concerns the comparison of two types of sensory gloves in terms of quasi-static measurement reproducibility and reliability and the improvement of an existing standard protocol for sensory glove assessment aimed at providing a more comprehensive analysis.
Sensory gloves are devices capable of measuring finger movements and are useful in numerous applications, many of which require real-time data acquisition. However, the procedures explored in the ...literature to assess measurement repeatability and reliability mainly rely on static or quasi-static conditions. To overcome this limitation, here we propose a testing procedure for assessing measurements under dynamic conditions (slow, medium, and rapid finger joint movements). To this aim, we used two sensory gloves, one based on resistive flex sensors (RFSs) and another based on inertial measurement units (IMUs)-as two of the most adopted types. Our study demonstrated the feasibility of measuring dynamic finger movements and the differences in dynamic measurement repeatability and reliability between RFS- and IMU-based gloves when considering the angles (in degrees) of each finger joint. The RFS-based glove scored with an average range ± standard deviation (SD) of 6.84° ± 2.77° and an intraclass correlation coefficient (ICC) of 0.77 ± 0.14, whereas the IMU-based glove scored with an average range of 8.49° ± 2.72° and an ICC of 0.75 ± 0.14. Both gloves exhibited better repeatability and reliability at the slowest speed, with the RFS-based glove having a higher repeatability than the IMU-based one (<inline-formula> <tex-math notation="LaTeX">p < 0.001 </tex-math></inline-formula>). Moreover, when compared to previous studies, the results (in terms of reliability and repeatability) here obtained under dynamic conditions are comparable to those obtained under static or quasi-static conditions. In summary, our results indicate that both proposed sensory gloves are suitable for most applications that require dynamic interactions.
Capturing hand motions for hand function evaluations is essential in the medical field. Various data gloves have been developed for rehabilitation and manual dexterity assessments. This study ...proposed a modular data glove with 9-axis inertial measurement units (IMUs) to obtain static and dynamic parameters during hand function evaluation. A sensor fusion algorithm is used to calculate the range of motion of joints. The data glove is designed to have low cost, easy wearability, and high reliability. Owing to the modular design, the IMU board is independent and extensible and can be used with various microcontrollers to realize more medical applications. This design greatly enhances the stability and maintainability of the glove.
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Patients with diabetes mellitus (DM) may experience problems such as peripheral tissue necrosis and gradual hardening of blood vessels; these may prevent normal metabolic behavior and may even ...require amputation under severe conditions. Therefore, evaluating the peripheral blood circulation state of patients with DM is crucial to enabling physicians to perform timely interventional therapy to prevent symptoms from worsening. To improve the general problems of treatment, such as high cost, the infection risk or misjudgment of specific groups, a smart blood perfusion monitoring system is proposed to noninvasively evaluate patients' peripheral blood circulation state. This system uses near-infrared spectroscopy to noninvasively monitor real-time changes in peripheral blood perfusion with force is applied on the arm. On the basis of changes in peripheral blood perfusion with pressure, several indexes related to blood circulation state are proposed. Finally, a neural network technique was successfully applied to classify patients' blood circulation state. From the experimental results, F-measure, sensitivity, positive predictive value and accuracy are 82.75%, 80.00%, 85.71% and 83.33%, respectively. The experimental results show that the proposed indexes (Indexes I-IV) are significantly related to blood circulation state and can be used to effectively evaluate peripheral blood circulation.
A 12-lead electrocardiogram (ECG) is one of the most commonly used tools for evaluating cardiovascular diseases (CVDs). In clinical, the conventional Ag/AgCl electrodes with conductive gel are ...usually used to monitor ECG, but they may encounter gel drying and skin allergic reaction problems for long-term ECG monitoring. In order to improve the above issue, several dry electrodes, made by conductive materials, was proposed in the design of wearable devices, but they still encounter the problems of electrode shifting and stretching, and the risk of skin irritation. Capacitive electrode can access biopotential without contacting skin directly and can completely avoid skin irritation. However, for capacitive electrode, how to maintain a good electrode-skin contacting condition is still a challenge due to electrode fixation problem, in particular in multichannel ECG measurement. In this study, a 12-lead ECG smart clothing based on capacitive sensing technology was proposed to monitor ECG in daily life. To improve the electrode-skin contacting condition, elastic conductive foams were used as the electrode plate to effectively fit the rough body contours. Moreover, a specific wearable belt mechanism was also designed to improve the convenience of use, and it could also provide good friction and proper pressure to reduce the electrode slippage and maintain a good electrode-skin contact condition. The experimental results showed that the proposed smart clothing could stably access the good ECG signal quality under different motion levels, and the specific ECG features corresponding to different CVDs could also be reflected on the measured ECG signal in clinical experiment.
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