Low-Power Wearable Healthcare Sensors

Sherratt, R. Simon; Dey, Nilanjan

doi:10.3390/electronics9060892

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Table 1. Root function coefficients with all participants' data (inclusive) for Equations (7) and (8).…”

Section: Physical Model Fittingmentioning

confidence: 99%

“…Wearable healthcare devices [ 7 ] provide the possibility to monitor vital signals and health-related activities in people’s daily lives, which benefits both peoples awareness towards their own health and the healthcare providers’ understanding of the patients’ progress. Personal wearable devices can also reduce the chance of the cross-infection of respiratory diseases, which may be a concern of shared medical devices [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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Accurate Spirometry with Integrated Barometric Sensors in Face-Worn Garments

Zhou

Costa

Lukowicz

2020

Sensors

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Cardiorespiratory (CR) signals are crucial vital signs for fitness condition tracking, medical diagnosis, and athlete performance evaluation. Monitoring such signals in real-life settings is among the most widespread applications of wearable computing. We investigate how miniaturized barometers can be used to perform accurate spirometry in a wearable system that is built on off-the-shelf training masks often used by athletes as a training aid. We perform an evaluation where differential barometric pressure sensors are compared concurrently with a digital spirometer, during an experimental setting of clinical forced vital capacity (FVC) test procedures with 20 participants. The relationship between the two instruments is derived by mathematical modeling first, then by various regression methods from experiment data. The results show that the error of FVC vital values between the two instruments can be as low as 2∼3%. Beyond clinical tests, the method can also measure continuous tidal breathing air volumes with a 1∼3% error margin. Overall, we conclude that barometers with millimeter footprints embedded in face mask apparel can perform similarly to a digital spirometer to monitor breathing airflow and volume in pulmonary function tests.

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“…Table 1. Root function coefficients with all participants' data (inclusive) for Equations (7) and (8).…”

Section: Physical Model Fittingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accurate Spirometry with Integrated Barometric Sensors in Face-Worn Garments

Zhou

Costa

Lukowicz

2020

Sensors

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“…Stretchable and flexible w-BFCs with built-in electrolytes and fuels are ideal power sources to power low-energy consumption wearable electronics, such as low power wearable sensors, [100] light emitting diodes [101] and Bluetooth Low Energy (BLE). [102] With artificial electrolytes containing fuels such as glucose, ethanol and fructose, these w-BFCs are able to work in a stable and green manner.…”

Section: Artificial Electrolyte For W-bfcsmentioning

confidence: 99%

Wearable Biofuel Cells: Advances from Fabrication to Application

Zhang

Kjøniksen

et al. 2021

Adv Funct Materials

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The booming field of wearable devices has nourished progress in developing multifunctional wearable energy sources that can withstand deformations while maintaining their electrochemical functions. Unlike energy storage systems such as rechargeable batteries and supercapacitors, wearable biofuel cells (w-BFCs) generate green electricity from energy-dense carbon-neutral fuels via highly efficient bioelectrochemical reactions, delivering excellent biocompatibility, remarkable environmental sustainability, and exceptional capability of miniaturization. These desirable merits give w-BFCs great potential in the field of wearable applications. Moreover, emerging studies of w-BFCs in self-powered biosensing, controlled drug delivery, and wound dressings have greatly expanded their possible fields of application. Recent progress and strategies to accomplish flexible and stretchable w-BFCs are summarized here. Novel materials and configurations with tailored features that can be employed to fabricate w-BFCs are elaborated and discussed. Current applications and near-future applications of w-BFCs in health-monitoring and medical treatment fields are outlined. Furthermore, challenges and perspectives regarding this emerging field of materials science and engineering are also emphasized.

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“…There are evidences of versatile use of wearable devices for emerging applications in healthcare monitoring [24][25][26]. Various new sensors for healthcare tracking have been devised in the recent past for different purposes [27][28][29][30][31] and these devices have been used for various applications [32,33]. The commonly acknowledged applications of these wearable devices include mental health assessment [34] and sleep monitoring [35].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Data from Wearable Sensors for Sleep Quality Estimation and Prediction Using Deep Learning

2020

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Wearable devices such as smartwatches, wristbands, GPS shoes are increasingly used for fitness and wellness as they allow users to monitor their daily health. These devices have sensors for accumulating user activity data. Clinical actigraph devices fall in the category of wearable devices worn on the wrist determined to estimate sleep parameters by recording movements during sleep. This study aims to predict sleep quality from wearable sensors using deep learning techniques. Three sleep indicators are proposed which are calculated using the data collected automatically from wearable devices. These sleep indicators are Daily Sleep Quality, Weekly Sleep Quality, and Sleep Consistency. Two deep learning models namely Convolution Neural Network (CNN) and Multilayer Perceptron (MLP) have been implemented to predict sleep quality on the basis of the proposed indicators. Two datasets have been used to validate the work proposed in this study which include a dataset comprising sleep parameters using commercial wearable devices and another dataset consisting of sleep data using clinical actigraph device. Systematic Minority Oversampling Technique has been applied for data augmentation with the intent to increase data instances and to alleviate class imbalance. CNN is observed to outperform MLP in predicting sleep quality with the highest accuracy of 97.30%. This study also evaluates the worth of each sleep attribute using Information Gain algorithm in order to identify the most important attributes which contribute to the sleep quality. It has been concluded that in bed awake percentage contributes maximum to the Daily Sleep Quality, average sleep efficiency contributes maximum to the Weekly Sleep Quality and standard deviation of midpoint of in bed and out of bed times contributes maximum to the Sleep Consistency.

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Low-Power Wearable Healthcare Sensors

Abstract: Medical science has taken great steps to enable us to live longer and healthier lives [...]

Cited by 5 publications

References 7 publications

Accurate Spirometry with Integrated Barometric Sensors in Face-Worn Garments

Accurate Spirometry with Integrated Barometric Sensors in Face-Worn Garments

Wearable Biofuel Cells: Advances from Fabrication to Application

Analysis of Data from Wearable Sensors for Sleep Quality Estimation and Prediction Using Deep Learning

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