Classification of Breathing Signals According to Human Motions by Combining 1D Convolutional Neural Network and Embroidered Textile Sensor

Kim, Jiseon; Kim, Jooyong

doi:10.3390/s23125736

Cited by 4 publications

(2 citation statements)

References 45 publications

(76 reference statements)

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“…Unlike previous works that involved complex fabrication processes, this sensor employs a simple and cost-effective technique, with a microporous PU foam serving as the dielectric material [59]. A compression sensor is illustrated in Figure 5a, where the porous Ecoflex dielectric is placed between embroidered electrodes and changes in capacitance were measured to assess respiration [60].…”

Section: Capacitive Sensorsmentioning

confidence: 99%

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Wearable Sensors for Respiration Monitoring: A Review

Hussain,

Ullah,

Fernández-García

et al. 2023

Sensors

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This paper provides an overview of flexible and wearable respiration sensors with emphasis on their significance in healthcare applications. The paper classifies these sensors based on their operating frequency distinguishing between high-frequency sensors, which operate above 10 MHz, and low-frequency sensors, which operate below this level. The operating principles of breathing sensors as well as the materials and fabrication techniques employed in their design are addressed. The existing research highlights the need for robust and flexible materials to enable the development of reliable and comfortable sensors. Finally, the paper presents potential research directions and proposes research challenges in the field of flexible and wearable respiration sensors. By identifying emerging trends and gaps in knowledge, this review can encourage further advancements and innovation in the rapidly evolving domain of flexible and wearable sensors.

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Section: Capacitive Sensorsmentioning

confidence: 99%

“…Figure 5. Capacitive sensors utilizing: (a) porous Ecoflex polymer[60]; (b) embroidery on face mask[61]; (c) PDMS composites, reprinted with permission from ref [64]…”

mentioning

confidence: 99%

Wearable Sensors for Respiration Monitoring: A Review

Hussain,

Ullah,

Fernández-García

et al. 2023

Sensors

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Detection of human movement by combining supervised machine learning and an embroidered textile capacitance sensor

Kim,

Kim

2024

Textile Research Journal

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This study contributes to respiratory pattern detection by introducing a fabric sensor utilizing capacitance measurement and a semi-supervised machine learning algorithm known as an AI-based autoencoder. The sensor, consisting of two embroidered electrodes composed of silver-coated conductive nylon filaments, leverages the body as a dielectric material. In the research, a garment-type respiratory sensor was employed to continuously monitor respiratory data during both static (standing) and dynamic (walking, brisk walking, running) actions. The sparse autoencoder algorithm was particularly employed for individual static and dynamic actions, effectively distinguishing respiratory patterns corresponding to various movements. In addition, the sparse autoencoder helps prevent overfitting, fundamentally minimizing errors between the compression and reconstruction of signals. The maximum number of epochs was set to 2000, and the target error was set at 0.005. All data were compared against the static walking as the training baseline. Ultimately, the root mean squared error (RMSE) between static postures averaged 0.1, while the RMSEs between dynamic actions of walking, brisk walking, and running were 0.61, 0.91, and 2.78, respectively. These results suggest that movement detection through error detection is practically feasible and possesses discernible capabilities.

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Flexible Wearable Sensors for Respiration Monitoring: A Review

Hussain,

Ullah,

Fernández-García

et al. 2023

Preprint

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This paper provides an overview of flexible and wearable respiration sensors with emphasis on their significance in healthcare applications. The paper classifies these sensors based on their operating frequency distinguishing between high-frequency sensors which operate above 10 MHz and low-frequency sensors. The operating principles of breathing sensors as well as the materials and fabrication techniques employed in their design are addressed. The research highlights the need for robust and flexible materials to enable the development of reliable and comfortable sensors. Finally, the paper presents potential research directions and proposes research challenges in the field of flexible and wearable respiration sensors. Thus, identifying emerging trends and gaps in knowledge, it encourages further advancements and innovation in the rapidly evolving domain of flexible and wearable sensors.

show abstract

Classification of Breathing Signals According to Human Motions by Combining 1D Convolutional Neural Network and Embroidered Textile Sensor

Cited by 4 publications

References 45 publications

Wearable Sensors for Respiration Monitoring: A Review

Wearable Sensors for Respiration Monitoring: A Review

Detection of human movement by combining supervised machine learning and an embroidered textile capacitance sensor

Flexible Wearable Sensors for Respiration Monitoring: A Review

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