Deep learning enabled classification of real-time respiration signals acquired by MoSSe quantum dot-based flexible sensors

Bokka, Naveen; Karhade, Jay; Sahatiya, Parikshit

doi:10.1039/d1tb01237a

Cited by 12 publications

(9 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To improve the comprehensive performance of the sensor, researchers have carried out a lot of research on conductive materials, flexible substrates, and sensor structures. − Among them, conductive materials mainly include zero-dimensional materials , (precious metal particles, polymer particles, etc. ), one-dimensional materials , (carbon nanotubes, metal wires, nanofibers, etc.…”

Section: Introductionmentioning

confidence: 99%

In Situ Polymerized MXene/Polypyrrole/Hydroxyethyl Cellulose-Based Flexible Strain Sensor Enabled by Machine Learning for Handwriting Recognition

Yang

Zhang

Wang

et al. 2023

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

Flexible strain sensors have significant progress in the fields of human–computer interaction, medical monitoring, and handwriting recognition, but they also face many challenges such as the capture of weak signals, comprehensive acquisition of the information, and accurate recognition. Flexible strain sensors can sense externally applied deformations, accurately measure human motion and physiological signals, and record signal characteristics of handwritten text. Herein, we prepare a sandwich-structured flexible strain sensor based on an MXene/polypyrrole/hydroxyethyl cellulose (MXene/PPy/HEC) conductive material and a PDMS flexible substrate. The sensor features a wide linear strain detection range (0–94%), high sensitivity (gauge factor 357.5), reliable repeatability (>1300 cycles), ultrafast response–recovery time (300 ms), and other excellent sensing properties. The MXene/PPy/HEC sensor can detect human physiological activities, exhibiting excellent performance in measuring external strain changes and real-time motion detection. In addition, the signals of English words, Arabic numerals, and Chinese characters handwritten by volunteers measured by the MXene/PPy/HEC sensor have unique characteristics. Through machine learning technology, different handwritten characters are successfully identified, and the recognition accuracy is higher than 96%. The results show that the MXene/PPy/HEC sensor has a significant impact in the fields of human motion detection, medical and health monitoring, and handwriting recognition.

show abstract

Section: Introductionmentioning

confidence: 99%

In Situ Polymerized MXene/Polypyrrole/Hydroxyethyl Cellulose-Based Flexible Strain Sensor Enabled by Machine Learning for Handwriting Recognition

Yang

Zhang

Wang

et al. 2023

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

show abstract

“… 42 Recently, Bokka et al fabricated a water-soluble MoSSe quantum dots/poly(vinyl alcohol) (PVA) film as a humidity sensor that was capable of discerning different breathing patterns. 43 The breathing data were analyzed using a neural network algorithm for classifying them into different breathing patterns. Liu et al recently fabricated a fabric-based humidity sensor using a hydrophobic-hydrophilic patterned polyester-spandex fabric as a substrate that was subsequently treated with polydopamine-acid carbon nanotubes (aCNTs) that were quick and sensitive to a large humidity range.…”

Section: Introductionmentioning

confidence: 99%

“…In this aspect, Rauf et al reported a smart textile containing a MOF film grown using the Langmuir–Blodgett (LB) technique resulting in a sensor capable of detecting water vapor in the presence of several volatile organic compounds . Recently, Bokka et al fabricated a water-soluble MoSSe quantum dots/poly(vinyl alcohol) (PVA) film as a humidity sensor that was capable of discerning different breathing patterns . The breathing data were analyzed using a neural network algorithm for classifying them into different breathing patterns.…”

Section: Introductionmentioning

confidence: 99%

Toward Continuous Breath Monitoring on a Mobile Phone Using a Frugal Conducting Cloth-Based Smart Mask

et al. 2022

View full text Add to dashboard Cite

A frugal humidity sensor that can detect changes in the humidity of exhaled breath of individuals has been fabricated. The sensor comprises a humiditysensitive conducting polymer that is in situ formed on a cloth that acts as a substrate. Interdigitated silver electrodes were screen-printed on the modified cloth, and conducting threads connected the electrodes to the measurement circuit. The sensor's response to changing humidity was measured as a voltage drop across the sensor using a microcontroller. The sensor was capable of discerning between fast, normal, and slow breathing based on the response time. A response time of ∼1.3 s was observed for fast breathing. An Android-based mobile application was designed to collect sensor data via Bluetooth for analysis. A time series classification algorithm was implemented to analyze patterns in breathing. The sensor was later stitched onto a face mask, transforming it into a smart mask that can monitor changes in the breathing pattern at work, play, and sleep.

show abstract

“…40 With the development and popularity of smart wearable devices, machine learning has received growing interest in the area of health and motion monitoring. 41 In particular, the convolutional neural networks (CNNs)-assisted deep learning approach, which is structured as a series of convolutional layers and pooling operation, shows promise for improving the accuracy and reliability of processing complex signals such as physiological signals and complex human activities. 42 In this work, a fully integrated patch for wireless intelligent respiratory monitoring is proposed, and its schematic layout is shown in Figure 1a.…”

mentioning

confidence: 99%

Fully Integrated Patch Based on Lamellar Porous Film Assisted GaN Optopairs for Wireless Intelligent Respiratory Monitoring

Liu,

Su,

Zhou

et al. 2023

Nano Lett.

View full text Add to dashboard Cite

Respiratory pattern is one of the most crucial indicators for accessing human health, but there has been limited success in implementing fast-responsive, affordable, and miniaturized platforms with the capability for smart recognition. Herein, a fully integrated and flexible patch for wireless intelligent respiratory monitoring based on a lamellar porous film functionalized GaN optoelectronic chip with a desirable response to relative humidity (RH) variation is reported. The submillimeter-sized GaN device exhibits a high sensitivity of 13.2 nA/%RH at 2−70%RH and 61.5 nA/%RH at 70−90%RH, and a fast response/recovery time of 12.5 s/6 s. With the integration of a wireless data transmission module and the assistance of machine learning based on 1-D convolutional neural networks, seven breathing patterns are identified with an overall classification accuracy of >96%. This integrated and flexible on-mask sensing platform successfully demonstrates real-time and intelligent respiratory monitoring capability, showing great promise for practical healthcare applications.

show abstract

Deep learning enabled classification of real-time respiration signals acquired by MoSSe quantum dot-based flexible sensors

Abstract: : Respiration rate is a vital parameter which is useful for earlier identification of diseases. In this context, various types of devices have been fabricated and developed to monitor different...

Cited by 12 publications

References 54 publications

In Situ Polymerized MXene/Polypyrrole/Hydroxyethyl Cellulose-Based Flexible Strain Sensor Enabled by Machine Learning for Handwriting Recognition

In Situ Polymerized MXene/Polypyrrole/Hydroxyethyl Cellulose-Based Flexible Strain Sensor Enabled by Machine Learning for Handwriting Recognition

Toward Continuous Breath Monitoring on a Mobile Phone Using a Frugal Conducting Cloth-Based Smart Mask

Fully Integrated Patch Based on Lamellar Porous Film Assisted GaN Optopairs for Wireless Intelligent Respiratory Monitoring

Contact Info

Product

Resources

About