A novel automatic cough frequency monitoring system combining a triaxial accelerometer and a stretchable strain sensor

Abstract

Objective evaluations of cough frequency are considered important for assessing the clinical state of patients with respiratory diseases. However, cough monitors with audio recordings are rarely used in clinical settings. Issues regarding privacy and background noise with audio recordings are barriers to the wide use of these monitors; to solve these problems, we developed a novel automatic cough frequency monitoring system combining a triaxial accelerator and a stretchable strain sensor. Eleven healthy adult volunteers and 10 adult patients with cough were enrolled. The participants wore two devices for 30 min for the cough measurements. An accelerator was attached to the epigastric region, and a stretchable strain sensor was worn around their neck. When the subjects coughed, these devices displayed specific waveforms. The data from all the participants were categorized into a training dataset and a test dataset. Using a variational autoencoder, a machine learning algorithm with deep learning, the components of the test dataset were automatically judged as being a “cough unit” or “non-cough unit”. The sensitivity and specificity in detecting coughs were 92% and 96%, respectively. Our cough monitoring system has the potential to be widely used in clinical settings without any concerns regarding privacy or background noise.

Cough recognition in pneumoconiosis patients based on a flexible patch with an embedded ACC sensor for remote monitoring

“…In this study, the classi er models trained solely on acceleration signals obtained from a exible chest patch exhibited superior classi cation outcomes. For example, the index of Precision and Recall achieved by the optimized classi er in the group of patients and the combined group of patients and healthy adults are better than those reported systems employing audio and motion data [10], and employing both a triaxial ACC and a strain sensor [11]. Furthermore, the study delved into the application of acceleration sensors in cough monitoring and explored the potential of the exible patch to enhance the accuracy of recognition.…”

“…The use of acceleration signals for cough recognition has been widely studied. In certain studies, accelerometers have been ranked second only to audio microphones in importance and have been widely used in sensors such as electrocardiograms, thermistors, contact microphones, and chest straps [11]. Doddabasappla et al [12] utilized the spectrum of acceleration signals collected by smartphones combined with machine learning to improve the accuracy of low-and medium-intensity cough detection to between 95.2% and 98.2%.…”

An alternative approach to determination of Covid-19 personal risk index by using fuzzy logic

“…This satisfies an evaluation that considers the symptoms and X-ray images together. On the other hand, in the studies [ 56 ] and [ 57 ], a novel automatic cough frequency monitoring system combining a triaxial accelerator and a stretchable strain sensor AND the development of a machine learning-based analysis framework to connect multimodal wearable sensor data are presented for the cough and fatigue respectively. Based on these researches, we can obtain more measured and improved responses for the severity of fatigue and cough especially in the first FIS.…”

Chest‐Laminated and Sweat‐Permeable E‐Skin for Speech and Motion Artifact‐Insensitive Cough Detection

“…From the waveforms displayed in Figure 6J when the volunteer speaks or walks slowly, it can be concluded that the developed sensor is insensitive to motion and speech signals. Based on the above measurement data, the immunity of the sensor to speech and motion artifact is better in cough detection than that of the previous reported sensors attached on neck [11,16,17] and mechanomyographic sensor on chest. [38] Even when experienced observers perform cough counts, small discrepancies may occur between counts either due to observer fatigue or sounds that are borderline between cough and throat clearing.…”

“…[10] With an accelerator attached to the epigastric region and a stretchable strain sensor around the neck, specific waveforms are captured when the volunteer coughs. [11] However, the motion artifacts caused by daily activities affect detection precision. Furthermore, chest belts, electrocardiography, electromyography, and nasal thermocouple sensors have been investigated individually or in combination for cough detection.…”