A facile approach is proposed for superior conformation and adhesion of wearable sensors to dry and wet skin Bioinspired skin-adhesive films are composed of elastomeric microfibers decorated with conformal and mushroom-shaped vinylsiloxane tips Strong skin adhesion is achieved by crosslinking the viscous vinylsiloxane tips directly on the skin surface Furthermore, composite microfibrillar adhesive films possess a high adhesion strength of 18 kPa due to the excellent shape adaptation of the vinylsiloxane tips to the multiscale roughness of the skin As a utility of the skin-adhesive films in wearable-device applications, they are integrated with wearable strain sensors for respiratory and heart-rate monitoring The signal-to-noise ratio of the strain sensor is significantly improved to 597 because of the considerable signal amplification of microfibrillar skin-adhesive films

Bioinspired Composite Microfibers for Skin Adhesion and Signal Amplification of Wearable Sensors.

Low-cost consumer radar integrated circuits combined with recent advances in machine learning have opened up a range of new possibilities in smart sensing. In this paper, we use a miniature radar sensor to capture Doppler signatures of 14 different hand gestures and train a deep convolutional neural network (DCNN) to classify these captured gestures. We utilize two receiving antennas of a continuous-wave Doppler radar capable of producing the in-phase and quadrature components of the beat signals. We map these two beat signals into three input channels of a DCNN as two spectrograms and an angle of arrival matrix. The classification results of the proposed architecture show a gesture classification accuracy exceeding 95% and a very low confusion between different gestures. This is almost 10% improvement over the single-channel Doppler methods reported in the literature.

Hand-Gesture Recognition Using Two-Antenna Doppler Radar With Deep Convolutional Neural Networks

Quality of sleep is an important indicator of health and well being. Recent developments in the field of in-home sleep monitoring have the potential to enhance a person's sleeping experience and contribute to an overall sense of well being. Existing in-home sleep monitoring devices either fail to provide adequate sleep information or are obtrusive to use. To overcome these obstacles, a noncontact and cost-effective sleep monitoring system, named SleepSense, is proposed for continuous recognition of the sleep status, including on-bed movement, bed exit, and breathing section. SleepSense consists of three parts: a Doppler radar-based sensor, a robust automated radar demodulation module, and a sleep status recognition framework. Herein, several time-domain and frequency-domain features are extracted for the sleep recognition framework. A prototype of SleepSense is presented and evaluated using two sets of experiments. In the short-term controlled experiment, the SleepSense achieves an overall 95.1% accuracy rate in identifying various sleep status. In the 75-minute sleep study, SleepSense demonstrates wide usability in real life. The error rate for breathing rate extraction in this study is only 6.65%. These experimental results indicate that SleepSense is an effective and promising solution for in-home sleep monitoring.

SleepSense: A Noncontact and Cost-Effective Sleep Monitoring System

Recent advancement in wearable technologies, particularly smart watches embedded with powerful processors, memory subsystems with various built-in sensors such as ac-celerometer, gyroscope and optical sensor in one single package has opened a whole new application space. One of the main applications of interest is the monitoring of movement patterns, heart rate, ECG and PPG particularly for longer duration's in natural environments. In this study, we conducted a performance evaluation on the optical heart rate sensor of the smartwatch with respect to the commonly used ECG and PPG devices. Results have shown that the heart rate acquired from the smartwatch is reasonably accurate with a high degree of correlation. Further, we conducted a preliminary exerise to evaluate sleep quality using the heart rate readings and accelerometer readings captured from the smartwatch and compared with a commercially available and clinically used non-contact sleep sensor, RESMED S+.

Smartwatch: Performance evaluation for long-term heart rate monitoring

The blockchain technology is taking the world by storm. Blockchain with its decentralized, transparent and secure nature has emerged as a disruptive technology for the next generation of numerous industrial applications. One of them is Cloud of Things enabled by the combination of cloud computing and Internet of Things. In this context, blockchain provides innovative solutions to address challenges in Cloud of Things in terms of decentralization, data privacy and network security, while Cloud of Things offer elasticity and scalability functionalities to improve the efficiency of blockchain operations. Therefore, a novel paradigm of blockchain and Cloud of Things integration, called BCoT, has been widely regarded as a promising enabler for a wide range of application scenarios. In this article, we present a state-of-the-art review on the BCoT integration to provide general readers with an overview of the BCoT in various aspects, including background knowledge, motivation, and integrated architecture. Particularly, we also provide an in-depth survey of BCoT applications in different use-case domains such as smart healthcare, smart city, smart transportation and smart industry. Then, we review the recent BCoT developments with the emerging blockchain and cloud platforms, services, and research projects. Finally, some important research challenges and future directions are highlighted to spur further research in this promising area.

Integration of Blockchain and Cloud of Things: Architecture, Applications and Challenges

Noncontact detection characteristic of Doppler radar provides an unobtrusive means of respiration detection and monitoring. This avoids additional preparations, such as physical sensor attachment or special clothing, which can be useful for certain healthcare applications. Furthermore, robustness of Doppler radar against environmental factors, such as light, ambient temperature, interference from other signals occupying the same bandwidth, fading effects, reduce environmental constraints and strengthens the possibility of employing Doppler radar in long-term respiration detection, and monitoring applications such as sleep studies. This paper presents an evaluation in the of use of microwave Doppler radar for capturing different dynamics of breathing patterns in addition to the respiration rate. Although finding the respiration rate is essential, identifying abnormal breathing patterns in real-time could be used to gain further insights into respiratory disorders and refine diagnostic procedures. Several known breathing disorders were professionally role played and captured in a real-time laboratory environment using a noncontact Doppler radar to evaluate the feasibility of this noncontact form of measurement in capturing breathing patterns under different conditions associated with certain breathing disorders. In addition to that, inhalation and exhalation flow patterns under different breathing scenarios were investigated to further support the feasibility of Doppler radar to accurately estimate the tidal volume. The results obtained for both experiments were compared with the gold standard measurement schemes, such as respiration belt and spirometry readings, yielding significant correlations with the Doppler radar-based information. In summary, Doppler radar is highlighted as an alternative approach not only for determining respiration rates, but also for identifying breathing patterns and tidal volumes as a preferred nonwearable alternative to the conventional contact sensing methods.

Monitoring and Analysis of Respiratory Patterns Using Microwave Doppler Radar

Real-time respiratory measurement with Doppler Radar has an important advantage in the monitoring of certain conditions such as sleep apnoea, sudden infant death syndrome (SIDS), and many other general clinical uses requiring fast nonwearable and non-contact measurement of the respiratory function. In this paper, we demonstrate the feasibility of using Doppler Radar in measuring the basic respiratory frequencies (via fast Fourier transform) for four different types of breathing scenarios: normal breathing, rapid breathing, slow inhalation-fast exhalation, and fast inhalation-slow exhalation conducted in a laboratory environment. A high correlation factor was achieved between the Doppler Radar-based measurements and the conventional measurement device, a respiration strap. We also extended this work from basic signal acquisition to extracting detailed features of breathing function (I : E ratio). This facilitated additional insights into breathing activity and is likely to trigger a number of new applications in respiratory medicine.

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Noncontact Detection and Analysis of Respiratory Function Using Microwave Doppler Radar

This paper presents the preliminary results of our work in detecting respiration using Doppler Radar in the 2.7 GHz operating band. We demonstrate the capability of Doppler Radar in capturing breathing patterns under various breathing forms such as normal breathing, fast breathing, as well as different rate of inhale and exhale. From the captured signals, respiration rate was obtained using Fast Fourier Transform and validated. The proposed approach could potentially be used in number of applications involving breathing rate and breathing pattern analysis via non-contact methods.

Doppler radar in respiratory monitoring: Detection and analysis

Recent years have witnessed a surge in telerehabilitation and remote healthcare systems blessed by the emerging low-cost wearable devices to monitor biological and biokinematic aspects of human beings. Although such telerehabilitation systems utilise cloud computing features and provide automatic biofeedback and performance evaluation, there are demands for overall optimisation to enable these systems to operate with low battery consumption and low computational power and even with weak or no network connections. This paper proposes a novel multilevel data encoding scheme satisfying these requirements in mobile cloud computing applications, particularly in the field of telerehabilitation. We introduce architecture for telerehabilitation platform utilising the proposed encoding scheme integrated with various types of sensors. The platform is usable not only for patients to experience telerehabilitation services but also for therapists to acquire essential support from analysis oriented decision support system (AODSS) for more thorough analysis and making further decisions on treatment.

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A Mobile Cloud Computing Framework Integrating Multilevel Encoding for Performance Monitoring in Telerehabilitation

This paper presents an evaluation of microwave Doppler radar used for capturing different types of breathing patterns in addition to the respiration rate Finding the respiration rate is equally important as identifying abnormal breathing patterns which it could be used to gain a better insight into respiratory disorders Various known breathing disorders were role played and captured using a non-contact microwave Doppler radar which further supports the feasibility of Doppler radar in obtaining an accurate detection of different types of breathing patterns The results obtained for all the experiments were compared with a standard measurement apparatus, respiration strap, yielding a good correlations with the Doppler radar signals In a nutshell, Doppler radar can be potentially used as an alternative approach, not only for finding the respiration rates, but also for identifying respiration patterns replacing the conventional contact methods

Yee Siong Lee

Papers

Monitoring and Analysis of Respiratory Patterns Using Microwave Doppler Radar

Noncontact Detection and Analysis of Respiratory Function Using Microwave Doppler Radar

Doppler radar in respiratory monitoring: Detection and analysis

A Mobile Cloud Computing Framework Integrating Multilevel Encoding for Performance Monitoring in Telerehabilitation

Respiration rate and breathing patterns from Doppler radar measurements