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BACKGROUND: This feasibility study aimed to detect respiratory waveforms from thoracic movements and evaluate if postoperative complications could be predicted using a carbon nanotube sensor. METHODS: Fifty patients who underwent lung resection for lung tumors were enrolled. The lung monitoring system of the carbon nanotube sensor was placed on bilateral chest walls across the 6(th)–9(th) ribs to measure chest wall motion. We examined the respiratory waveform in relation to surgical findings, postoperative course, and complications using Hilbert transform and Fast Fourier Transform (FFT). RESULTS: Of 50 patients (37 males, 13 females), 22 were included in the normal lung function group and 28 were included in the low lung function group. The respiratory rate and waveform indicated a regular pattern in the normal lung function group and the respiratory rate could be detected. Conversely, irregular respiratory pattern was detected in 70% of patients in the low lung function group. There was no significant different overall envelope peak value between operated side and non-operated side (0.195±0.05 and 0.18±0.06). In contrast, there was significantly high peak value in the presence of postoperative complications (P

Respiratory monitoring is a significant issue to reduce patient risks and medical staff labor in postoperative care and epidemic infection, particularly after the COVID-19 pandemic. Oximetry is widely used for respiration monitoring in the clinic, but it sometimes fails to capture a low-functional respiratory condition even though a patient has breathing difficulty. Another approach is breathing-sound monitoring, but this is unstable due to the indirect measurement of lung volume. Kobayashi in our team is developing a sensor measuring temporal changes in lung volume with a displacement sensor attached across the sixth and eighth ribs. For processing these respiratory signals, we propose the combination of complex-valued wavelet transform and the correlation among spectrum sequences. We present the processing results and discuss its feasibility to detect a low-functional condition in respiration. The result for detecting low-functional respiration showed good performance with a sensitivity of 0.88 and specificity of 0.88 to 1 in its receiver operating characteristic (ROC) curve.

We studied the use of carbon-nanotube- (CNT)-based strain sensors as components of a textile-based, wearable sensing system for real-time motion detection. In the stretchable sensor, millimeter-long multiwalled CNTs (MWCNTs) are unidirectionally aligned and sandwiched between elastomer layers. We synthesized urethane resin to make the elastomer, which exhibits low elasticity and an affinity for human skin. The aligned CNT layer was formed by stacking CNT webs drawn from a spinnable CNT forest. The stretchable sensor can be stretched up to 200% and exhibits a short sensing delay of less than 15 ms. The gauge factor exceeds 10, which indicates high sensitivity. Moreover, the device is thin and as soft as human skin. The demonstrated flexibility and conformable nature make this material ideally suited for wearable sensors, specifically for a textile-based, wearable, real-time, human body motion-sensing application.