Figure 4 - available via license: Creative Commons Attribution 4.0 International
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Respiration measurement. (a) Human respiratory pattern detection. Various breathing patterns such as slow and light breathing, rapid and heavy breathing, and continuous coughing (inset, five times) were recorded by the WPAT2. (b) Human expiratory airflow monitoring. Comparison of the breath-by-breath exhalation recorded by the TSD117 and the WPAT2 when light and short breathing (upper), normal breathing (middle), and heavy and long breathing (bottom). (c) Human lung volume measurement. Lung volumes were measured by the TSD117 and the PRAT4 at different airflow rates, ∼4 L/s (left), ∼5 L/s (middle), and ∼9 L/s (right). The legends show the measured air volumes and Pearson correlation coefficients.
Source publication
Despite the importance of respiration and metabolism measurement in daily life, they are not widely available to ordinary people because of sophisticated and expensive equipment. Here, we first report a straightforward and economical approach to monitoring respiratory function and metabolic rate using a wearable piezoelectric airflow transducer (WP...
Contexts in source publication
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... on this, we proposed a unique design concept of double-layered PLLA BS (PLLA2BS) via face-toface stacking of two piezoelectric PLLA films with cutting angles of 45 and −45°. As two PLLA films of the PLLA2BS receive opposite forces when bent, the PLLA2BS exhibits an increased piezoelectric bending response than a conventional single-layered PLLA BS ( Figure S4). Nevertheless, the singlelayered PLLA BS is much more flexible than the doublelayered PLLA2BS. ...
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... modifying the coefficients of the calibration equations based on their average accuracy, both WPATs exhibit increased accuracy. The WPAT2 has a measurement error of 2.6 ± 1.1%, and the WPAT4 presents a 3.3 ± 2.0% error (Figures S14 and S15). ...
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... respiratory patterns were recorded by blowing on the open side of the WPAT2 under static conditions. As shown in Figure 4a, for slow and light breathing, a relatively constant peak to peak signal amplitude (V p−p ) of ∼0.7 V with a peak to peak time interval (T p−p ) of ∼1.5 s is produced, whereas under rapid and heavy breathing, a higher V p−p (∼1.4 V) with a shorter T p−p (∼0.5 s) is measured. It is interesting to find that the WPAT2 can identify coughing. ...
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... is interesting to find that the WPAT2 can identify coughing. The continuous coughing (five times, inset of Figure 4a) detected by the WPAT2 presents asymmetric waveforms with a very dense form. This indicates that the WPAT2 has sufficient capability to detect and differentiate breathing patterns such as the rate and depth of breathing and coughing. ...
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... expiratory flow rate and volume monitoring were carried out by providing several exhaled breaths of air into the TSD117 and the WPAT2 together (see Figure S18) for ∼30 s under static conditions. Figure 4b displays two exhaled airflow rates measured by the TSD117 and the WPAT2 at various breathing patterns, such as light breathing (Figure 4b, top), normal breathing (Figure 4b, middle), and deep breathing (Figure 4b, bottom). The two signals' Pearson correlation coefficient values are over 0.99, proving that the WPAT2 has comparable performance to the TSD117 in the respiratory flow and volume measurement. ...
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... expiratory flow rate and volume monitoring were carried out by providing several exhaled breaths of air into the TSD117 and the WPAT2 together (see Figure S18) for ∼30 s under static conditions. Figure 4b displays two exhaled airflow rates measured by the TSD117 and the WPAT2 at various breathing patterns, such as light breathing (Figure 4b, top), normal breathing (Figure 4b, middle), and deep breathing (Figure 4b, bottom). The two signals' Pearson correlation coefficient values are over 0.99, proving that the WPAT2 has comparable performance to the TSD117 in the respiratory flow and volume measurement. ...
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... expiratory flow rate and volume monitoring were carried out by providing several exhaled breaths of air into the TSD117 and the WPAT2 together (see Figure S18) for ∼30 s under static conditions. Figure 4b displays two exhaled airflow rates measured by the TSD117 and the WPAT2 at various breathing patterns, such as light breathing (Figure 4b, top), normal breathing (Figure 4b, middle), and deep breathing (Figure 4b, bottom). The two signals' Pearson correlation coefficient values are over 0.99, proving that the WPAT2 has comparable performance to the TSD117 in the respiratory flow and volume measurement. ...
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... expiratory flow rate and volume monitoring were carried out by providing several exhaled breaths of air into the TSD117 and the WPAT2 together (see Figure S18) for ∼30 s under static conditions. Figure 4b displays two exhaled airflow rates measured by the TSD117 and the WPAT2 at various breathing patterns, such as light breathing (Figure 4b, top), normal breathing (Figure 4b, middle), and deep breathing (Figure 4b, bottom). The two signals' Pearson correlation coefficient values are over 0.99, proving that the WPAT2 has comparable performance to the TSD117 in the respiratory flow and volume measurement. ...
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... the lung volume measurement was performed by blowing one sequence of expiration into the TSD117 and the WPAT4 together from a fully inhaled lung to a completely exhaled condition. Figure 4c compares two lung volumes and the integral values of the exhaled airflow rates, measured by the TSD117 and the PRAT4 at different airflow rates. Again, two airflow transducers show a similar tendency (their Pearson correlation coefficients > 0.99), indicating that PRAT4 has comparable performance to the TSD117 in the human lung volume measurement. ...
Citations
Neuromorphic olfactory systems have been actively studied in recent years owing to their considerable potential in electronic noses, robotics, and neuromorphic data processing systems. However, conventional gas sensors typically have the ability to detect hazardous gas levels but lack synaptic functions such as memory and recognition of gas accumulation, which are essential for realizing human‐like neuromorphic sensory system. In this study, a seamless architecture for a neuromorphic olfactory system capable of detecting and memorizing the present level and accumulation status of nitrogen dioxide (NO2) during continuous gas exposure, regulating a self‐alarm implementation triggered after 147 and 85 s at a continuous gas exposure of 20 and 40 ppm, respectively. Thin‐film‐transistor type gas sensors utilizing carbon nanotube semiconductors detect NO2 gas molecules through carrier trapping and exhibit long‐term retention properties, which are compatible with neuromorphic excitatory applications. Additionally, the neuromorphic inhibitory performance is also characterized via gas desorption with programmable ultraviolet light exposure, demonstrating homeostasis recovery. These results provide a promising strategy for developing a facile artificial olfactory system that demonstrates complicated biological synaptic functions with a seamless and simplified system architecture.
With the development of the internet of things technology, sensors have played an important role in aerospace, industry, agriculture, medical and other fields. Especially in the aspect of wearable respiratory monitoring, real-time monitoring of multiple respiratory physiological parameters through intelligent sensors enable people to achieve early prevention and timely diagnosis of respiratory diseases. However, most of the humidity sensors have the disadvantages of poor permeability, long response time and narrow detection range. Therefore, this paper introduced a flexible humidity sensor with a wide detection range and fast response time for respiratory monitoring. The sensor used PEDOT:PSS-GO composite material as humidity sensitive material. Due to the addition of GO, the sensor had a wide linear detection range of humidity (35–80%RH) and a short response/recovery time (0.53/0.8 s). Moreover, the sensor used the micromesh structure of PI as the substrate, which had better flexibility and permeability and the sensor was easy to attach to complex surfaces such as masks or human skin. Based on these excellent sensing characteristics, the humidity sensor was connected with a miniature wireless communication component for human respiratory detection. The humidity sensor can clearly show the difference between normal and abnormal respiratory signals and this provided a new idea for the intelligent healthcare technology.
We propose a flexible wearable respiratory sensor based on microspheres coupling, where the sensing element is a microfiber embedded in a polydimethylsiloxane (PDMS) film doped with 5μm diameter silica microspheres. In this study, PDMS doped with microspheres were used to coat microfibers for the first time, and the enhancement of the evanescent field on the surface of microfibers was observed. Theoretically and experimentally, it is found that the light transmitted in the optical fiber core is effectively dragged by the microsphere, which significantly enhances the evanescent field and thus improves the sensitivity of the sensing element. During the respiratory monitoring, the pressure generated by the respiratory airflow causes the sensing element to bend, and the self-mixing interference is used to detect the power change of reflected light to reconstruct the respiratory signal. The empirical findings demonstrate a peak in sensor sensitivity at a microsphere doping concentration of 0.1
g/mL
while a subsequent augmentation in microspheres doping concentration inversely correlates with sensor sensitivity. Notably, the sensor developed with a 0.1
g/mL
microspheres doping concentration exhibits an exceptional capacity for continuous, real-time differentiation among diverse respiratory signals. This innovation is characterized by its elevated sensitivity and responsiveness, evidenced by an impressively short 28
ms
response time. To validate the sensor’s effectiveness, we employed the Bland-Altman statistical analysis test to assess the accuracy of respiration rate measurements using the collected data from the test subjects. The favorable outcomes we obtained offer a promising avenue for advancing research in the realm of non-invasive vital signs monitoring.
The application of biodegradable films based on the piezoelectric effect of poly‐l‐lactic acid (PLLA) in sensors, transducers, actuators, and so on has gradually become one of the emerging research focuses. In this work, a flexible PLLA sensor with UV‐curable acrylate film packaging is proposed for human behavior recognition. The acrylic films with different proportions have different mechanical and surface properties, showing good flexibility, ductility, and skin adhesion. The morphology and crystal structure of the PLLA films and UV curable substrate obtained under the various postprocessing conditions and different proportions are examined by scanning electron microscopy, X‐ray diffraction, Raman, and ATR‐FTIR spectroscopy. In addition, by comparing six groups of PLLA films with different treatment conditions, the device made on film stretched four times and annealed for 8 h has the best performance. This sensor has good response ability to impact, vibration, and bending strain, and can be applied to human behavior recognition, such as touch, wrist bending, and swallowing.
