Sensors for Wearable Systems

Scilingo, Enzo Pasquale; Lanatà, Antonio; Tognetti, Alessandro

doi:10.1007/978-1-4419-7384-9_1

Cited by 11 publications

(11 citation statements)

References 40 publications

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“…7 using KVL and KCL. Equation (20), shown at the bottom of the page, shows the R matrix in the case where the number of conductive stitches is four.…”

Section: Equivalent Electrical Model Of the Knitted Sensormentioning

confidence: 99%

“…Since the unit length resistance of the conductive yarn is constant, (14)- (20) can be used to calculate the equivalent electrical resistance at any given strain, providing the sensor remains within elastic region. However, applied tensile stress will have a different level of separation effects on the sinker loop contact points and the head contact points at given dynamic strain levels.…”

Section: Equivalent Electrical Model Of the Knitted Sensormentioning

confidence: 99%

“…Impedance plethysmography, inductive plethysmography, pneumography based on piezoresistive sensing or plethysmography based on piezoelectric sensing are current methods [20]. Impedance plethysmography is a technique which utilize change in impedance of the thorax during breathing [21].…”

Section: Introductionmentioning

confidence: 99%

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Weft-Knitted Strain Sensor for Monitoring Respiratory Rate and Its Electro-Mechanical Modeling

2015

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In this paper, a textile-based strain sensor has been developed to create a respiration belt. The constituent materials and the knitted structure of the textile sensor have been specifically selected and tailored for this application. Electromechanical modeling has been developed by exploiting Peirce's loop model in order to describe the fabric geometry under static and dynamic conditions. Kirchhoff's node and loop equations have been employed to create a generalized solution for the equivalent electrical resistance of the textile sensor for a given knitted loop geometry and for a specified number of loops. A laboratory test setup was built to characterize the prototype sensor and the resulting equivalent resistance under strain levels up to 40%, and consistent resistance response levels have been obtained from the sensor which correlate well with the modelled data. Production of the respiration belt was realized by bringing together knitted sensor and a relatively inelastic textile strap. Both machine simulations and real-time measurements on a human subject have been performed in order to calculate average breathing frequencies under different static and dynamic conditions. Also, different scenarios have been performed, such as slow breathing and rapid breathing. The sensory belt was located in either the chest area or in the abdominal area during the experimental measurements and the sensor yielded a good response under both static and dynamic conditions. However, body motion artefacts affected the signal quality under dynamic conditions and an additional signal-processing step was added to eliminate unwanted interference from the breathing signal.Index Terms-Breathing frequency, conductive yarn, knitted sensor, respiration belt, silver-coated nylon yarn, textile-based strain sensor, electro-mechanical modelling.

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“…7 using KVL and KCL. Equation (20), shown at the bottom of the page, shows the R matrix in the case where the number of conductive stitches is four.…”

Section: Equivalent Electrical Model Of the Knitted Sensormentioning

confidence: 99%

Section: Equivalent Electrical Model Of the Knitted Sensormentioning

confidence: 99%

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Weft-Knitted Strain Sensor for Monitoring Respiratory Rate and Its Electro-Mechanical Modeling

2015

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“…When the pair of electrodes is placed over the torso, the output signal varies in direct proportion to the volume of air inspired and expired from the lungs during breathing. This signal thus has a significant correlation with the output from a spirometer which consists of a mouthpiece that directly measures the airflow in the lungs during inspiration and expiration [18]. Fig.…”

Section: Salient Characteristics Of Spirometry and Eipmentioning

confidence: 99%

Virtual Spirometry and Activity Monitoring Using Multichannel Electrical Impedance Plethysmographs in Ambulatory Settings

Khan

Gore

Ashe

et al. 2017

IEEE Trans. Biomed. Circuits Syst.

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Continuous monitoring of respiratory patterns and physical activity levels can be useful for remote health management of patients with conditions such as heart disease and chronic obstructive pulmonary disease (COPD). In a clinical setting, spirometers serve as the gold-standard for monitoring respiratory patterns such as breathing-rate and changes in lung-volume. However, direct measurements using a spirometer requires placement of a sensor in the patient’s airway and is thus infeasible for continuous monitoring in non-clinical, ambulatory settings. Under these conditions, indirect respiration monitoring using electrical impedance plethysmographs (EIP) is more suitable but are susceptible to motion artifacts. In this paper we investigate whether multi-channel EIP can be used to perform virtual spirometry under ambulatory settings. The experiments presented in this paper are based on preliminary data collected from 19 adult human subjects under realistic ambulatory and non-ambulatory settings. We first highlight the salient features of the signal acquired from a standard spirometer. We then compare the performance of different bio-signal processing algorithms in estimating the spirometer signal using multiple EIP sensors and in the presence of motion artifacts and real-world interferences. We demonstrate that in addition to reliably determining different respiratory patterns and states, multi-channel EIP could also be used to reliably extract information regarding different patient physical activity states like bending or stretching.

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“…The physiological and biomechanical parameters extracted from the measurement and processing of biosignals can be used to estimate the health condition of the user [17], [33]. Some examples of biosignals, and the sensors used to monitor them are -heart rate (the frequency of the cardiac cycle) is measured using pulse oximeter or skin electrodes;…”

Section: (A) Wearable Sensorsmentioning

confidence: 99%

Vision-based patient wellness monitoring using facial cues

Sathyanarayana¹

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I would like to thank all my friends at NTU, who have been there for me, in all ways possible. I would like to thank Dr. Aruna Sathyanarayana and Dr. Kshipra Nandakumar for their valuable inputs as medical doctors. I would like to thank Mr. Jeremiah Chua for all the technical support throughout. I would like to acknowledge School of Computer Science and Engineering, Nanyang Technological University for providing this opportunity to me for carrying out this research work. I would like to express my gratitude to my parents, my parents-in law and my extended family in Singapore, for their inexplicable support in this journey. My deepest gratitude to my spiritual master, Guru Paramahamsa Sri Nithyananda, who has been the guiding light from within, and the source of strength.

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Sensors for Wearable Systems

Cited by 11 publications

References 40 publications

Weft-Knitted Strain Sensor for Monitoring Respiratory Rate and Its Electro-Mechanical Modeling

Weft-Knitted Strain Sensor for Monitoring Respiratory Rate and Its Electro-Mechanical Modeling

Virtual Spirometry and Activity Monitoring Using Multichannel Electrical Impedance Plethysmographs in Ambulatory Settings

Vision-based patient wellness monitoring using facial cues

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