Characterization of Coated Piezo-resistive Fabric for Respiration Sensing

Arja, Sami El; Jayarathna, Titus; Ulloa, Felipe; Breen, Paul P.

doi:10.1109/iceerp49088.2019.8956989

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…However, the stretch length vs resistance is not linear, as shown in Figure 2e. The non-linearity of the fabric sensor is studied in details in [21,22].…”

Section: Sensor Designmentioning

confidence: 99%

Electrodeless Heart and Respiratory Rate Estimation During Sleep Using a Single Fabric Band and Event-Based Edge Processing

Jayarathna¹,

Lui²,

Breen³

2022

Preprint

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Heart rate (HR) and respiratory rate (RR) are two vital parameters of the body medically used for diagnosing short/long term illness. Out-of-the-body, non-contact HR/RR measurement remains a challenge due to imprecise readings. “Invisible” wearables integrated into day-to-day garments has the potential to produce precise readings with comfortable user experience. Sleep studies and patient monitoring benefit from “Invisibles” due to longer wearability without significant discomfort. This paper suggests a novel method to reduce the footprint of sleep monitoring devices. We use a single silver-coated nylon fabric band integrated into a substrate of standard cotton/nylon garment as a resistive elastomer sensor to measure air and blood volume change across the chest. We introduce a novel event-based architecture to process data at the edge device and describe two algorithms to calculate real-time HR/RR on ARM Cortex-M3 and Cortex-M4F microcontrollers. RR estimations show a sensitivity of 99.03% and a precision of 99.03% for identifying individual respiratory peaks. The two algorithms used for HR calculation show a mean absolute error of 0.81±0.97 and 0.86±0.61 beats/minute compared to a gold standard ECG-based HR. The event-based algorithm converts the respiratory/pulse waveform into instantaneous events, therefore, reducing the data size by 40-140 times and requires 33% less power to process and transfer data. Further, we show that events hold enough information to reconstruct the original waveform, retaining pulse, and respiratory activity. We suggest fabric sensors and event-based algorithms would drastically reduce the device footprint and increase the performance for HR/RR estimations during sleep studies providing better user experience.

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“…However, the stretch length vs resistance is not linear, as shown in Figure 2e. The non-linearity of the fabric sensor is studied in details in [21,22].…”

Section: Sensor Designmentioning

confidence: 99%

Electrodeless Heart and Respiratory Rate Estimation During Sleep Using a Single Fabric Band and Event-Based Edge Processing

Jayarathna¹,

Lui²,

Breen³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The proposed sensor was previously compared with an ECG and an electro resistive band (ERB) [40][41][42] to demonstrate its effectiveness in detecting both cardiac and respiratory events, which are necessary to perform triage assessment in EDs. ECG was selected as benchmark for two main reasons, the primary being that it is the healthcare standard for non-invasive measurement of cardiac function and for cardiac monitoring in triage, and the secondary being that it also carries information on respiration that can be used to extract the respiratory rate [43].…”

Section: Analysis Of Proposed Sensor Performances In Cardiac and Respiratory Monitoring 221 Data Collectionmentioning

confidence: 99%

“…ECG was selected as benchmark for two main reasons, the primary being that it is the healthcare standard for non-invasive measurement of cardiac function and for cardiac monitoring in triage, and the secondary being that it also carries information on respiration that can be used to extract the respiratory rate [43]. Although respiratory rate can be extracted from ECG, the ERB was considered as a more reliable benchmark for respiratory monitoring, as it proved capable of accurately monitor the variations of tidal volume of the lungs during respiration [33,[40][41][42].…”

Section: Analysis Of Proposed Sensor Performances In Cardiac and Respiratory Monitoring 221 Data Collectionmentioning

confidence: 99%

Wearable Bluetooth Triage Healthcare Monitoring System

Polley

Jayarathna

Gunawardana

et al. 2021

Sensors

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Triage is the first interaction between a patient and a nurse/paramedic. This assessment, usually performed at Emergency departments, is a highly dynamic process and there are international grading systems that according to the patient condition initiate the patient journey. Triage requires an initial rapid assessment followed by routine checks of the patients’ vitals, including respiratory rate, temperature, and pulse rate. Ideally, these checks should be performed continuously and remotely to reduce the workload on triage nurses; optimizing tools and monitoring systems can be introduced and include a wearable patient monitoring system that is not at the expense of the patient’s comfort and can be remotely monitored through wireless connectivity. In this study, we assessed the suitability of a small ceramic piezoelectric disk submerged in a skin-safe silicone dome that enhances contact with skin, to detect wirelessly both respiration and cardiac events at several positions on the human body. For the purposes of this evaluation, we fitted the sensor with a respiratory belt as well as a single lead ECG, all acquired simultaneously. To complete Triage parameter collection, we also included a medical-grade contact thermometer. Performances of cardiac and respiratory events detection were assessed. The instantaneous heart and respiratory rates provided by the proposed sensor, the ECG and the respiratory belt were compared via statistical analyses. In all considered sensor positions, very high performances were achieved for the detection of both cardiac and respiratory events, except for the wrist, which provided lower performances for respiratory rates. These promising yet preliminary results suggest the proposed wireless sensor could be used as a wearable, hands-free monitoring device for triage assessment within emergency departments. Further tests are foreseen to assess sensor performances in real operating environments.

show abstract

Electrodeless Heart and Respiratory Rate Estimation during Sleep Using a Single Fabric Band and Event-Based Edge Processing

Jayarathna

Lui

Breen

2022

Sensors

View full text Add to dashboard Cite

Heart rate (HR) and respiratory rate (RR) are two vital parameters of the body medically used for diagnosing short/long-term illness. Out-of-the-body, non-skin-contact HR/RR measurement remains a challenge due to imprecise readings. “Invisible” wearables integrated into day-to-day garments have the potential to produce precise readings with a comfortable user experience. Sleep studies and patient monitoring benefit from “Invisibles” due to longer wearability without significant discomfort. This paper suggests a novel method to reduce the footprint of sleep monitoring devices. We use a single silver-coated nylon fabric band integrated into a substrate of a standard cotton/nylon garment as a resistive elastomer sensor to measure air and blood volume change across the chest. We introduce a novel event-based architecture to process data at the edge device and describe two algorithms to calculate real-time HR/RR on ARM Cortex-M3 and Cortex-M4F microcontrollers. RR estimations show a sensitivity of 99.03% and a precision of 99.03% for identifying individual respiratory peaks. The two algorithms used for HR calculation show a mean absolute error of 0.81 ± 0.97 and 0.86±0.61 beats/min compared with a gold standard ECG-based HR. The event-based algorithm converts the respiratory/pulse waveform into instantaneous events, therefore reducing the data size by 40–140 times and requiring 33% less power to process and transfer data. Furthermore, we show that events hold enough information to reconstruct the original waveform, retaining pulse and respiratory activity. We suggest fabric sensors and event-based algorithms would drastically reduce the device footprint and increase the performance for HR/RR estimations during sleep studies, providing a better user experience.

show abstract

Characterization of Coated Piezo-resistive Fabric for Respiration Sensing

Cited by 3 publications

References 9 publications

Electrodeless Heart and Respiratory Rate Estimation During Sleep Using a Single Fabric Band and Event-Based Edge Processing

Electrodeless Heart and Respiratory Rate Estimation During Sleep Using a Single Fabric Band and Event-Based Edge Processing

Wearable Bluetooth Triage Healthcare Monitoring System

Electrodeless Heart and Respiratory Rate Estimation during Sleep Using a Single Fabric Band and Event-Based Edge Processing

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