Advanced Wearable Microfluidic Sensors for Healthcare Monitoring

Li, Sheng; Ma, Zhong; Cao, Zhonglin; Pan, Lijia; Shi, Yi

doi:10.1002/smll.201903822

Cited by 118 publications

(74 citation statements)

References 121 publications

(179 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unpowered sensors have recently attracted attention in wearable electronic devices for their inherent advantages of flexibility and comfortability, especially when interfacing with wearable layers in contact with the skin without any electrical components [71][72][73][74]. Unpowered biosensors on electronic contact lenses are widely used for pressure, strain, glucose, pH, and temperature sensing.…”

Section: A Unpowered Sensorsmentioning

confidence: 99%

Wireless Communication and Power Harvesting in Wearable Contact Lens Sensors

et al. 2021

View full text Add to dashboard Cite

The human eye contains multiple biomarkers related to various diseases, making electronic contact lens an ideal non-invasive platform for their diagnosis and treatment. Recent advances in technology have enabled the monitoring and diagnosis of glaucoma from Intraocular Pressure (IOP) detection, diabetes from glucose concentration detections, and other biosensors for pH and temperature sensing. Different sensor designs have led to distinct power transfer techniques, among which inductively coupled power transfer is considered most favourable for electronic contact lenses power delivery applications. Therefore, loop antenna, spiral shape antenna, and antenna with nanomaterials such as graphene and hybrid silver nanofibers have been explored under Industrial, Scientific, and Medical (ISM) frequency bands for both Wireless Power Transfer (WPT) and data communication. Notably, spiral antennas are also considered as the component of IOP sensing using capacitive sensors to detect the changes in frequency caused by pressure. This article reviews the stateof-the-art technologies in electronic contact lens sensors and their power delivery techniques. Herein, diverse sensing methods, materials, and power transfer techniques and the promising future trends and challenges in electronic contact lenses have been presented.

show abstract

Section: A Unpowered Sensorsmentioning

confidence: 99%

Wireless Communication and Power Harvesting in Wearable Contact Lens Sensors

et al. 2021

View full text Add to dashboard Cite

show abstract

“…[11,160,[208][209][210] F-FET-based biochemical sensors could provide in situ monitoring of the sweat compositions in a noninvasive way. [211] Considering the complexity of sweat compositions, numerous sensors have been reported toward different ingredients, including pH sensors, [47,147,210,[212][213][214][215][216][217] ion sensors, [47,147,[218][219][220][221][222][223] glucose sensors, [21,45,48,159,[224][225][226] lactate sensors, [227] etc. Based on WO 3 NP, Santos et al [212] reported a representative F-FET-based pH sensor for in vivo applications (Figure 12e).…”

Section: F-fet Sensors Toward Health Monitoringmentioning

confidence: 99%

Recent Advances in Flexible Field‐Effect Transistors toward Wearable Sensors

Han

Zhou

2020

Advanced Intelligent Systems

View full text Add to dashboard Cite

The introduction of the "Internet of Things" (IoTs) concept has spawned a series of research and development of wearable sensors. Flexible field-effect transistors (FETs) are considered to be potential sensing devices due to the variety of material utilization and the self-amplifying function on electrical signals. FETs have demonstrated the ability of detecting different kinds of external stimuli and continuous monitoring functionalities. Herein, the recent progress achieved by the academia in wearable sensors based on flexible FETs, including pressure, temperature, chemical, and biological analytes, which are vital for the manufacturing of smart wearable devices, is summarized. The sensing mechanism for different sensors is introduced and an in-depth discussion is presented, including material engineering, problems at the current stage, and future challenges.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Advanced Wearable Microfluidic Sensors for Healthcare Monitoring

Cited by 118 publications

References 121 publications

Wireless Communication and Power Harvesting in Wearable Contact Lens Sensors

Wireless Communication and Power Harvesting in Wearable Contact Lens Sensors

Recent Advances in Flexible Field‐Effect Transistors toward Wearable Sensors

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

Contact Info

Product

Resources

About