Wireless Wearable Devices Research Articles

D23. A Wearable Wireless Device Monitors Sacral Pressure during Operative Cases

D23. A Wearable Wireless Device Monitors Sacral Pressure during Operative Cases

Wireless wearable devices for continuous monitoring of body sounds and motions.

Wireless wearable devices for continuous monitoring of body sounds and motions.

Cyclodextrin incorporated textile supercapacitor/piezo-triboelectric nanogenerator hybrid system for versatile temperature dependent wearable wireless devices

The problem of wearable textile electronics having no power source can be greatly assisted at low-temperature environments by the demand for flexible supercapacitor sustaining self-charging system. We explore a typical piezo-triboelectric nanogenerators (PTNGs) textile fabric integrated flexible supercapacitors (SCs) face substantial difficulty in maintaining their ability to quickly charge in colder environments for various wearable wireless sensing devices. PTNGs using composite electrodes frequently experience quick performance degradation at low energy levels as a result of the poor ion mobility besides charge transfer in the electrolyte. In this article, we offer a SC-coupled self-chargeable power generator with wide operating temperature range (25 ∼ −80 °C). A composite system of sulfonated β-cyclodextrin/polyvinylpyrrolidone/Ni@MnCO3 on cotton fabric with polyvinylidene fluoride and polydimethylsiloxane were used to build the smart textile SC, which produced 19.6 V at −80 °C and exhibited a gravimetric capacitance of 94.7 Fg−1 with excellent cyclic stability (>50,000). The biocompatibility studies reveal good results (∼95 % of cell viability) for the wearable textile SCs. In addition, the possibility of application to a sensing device was successfully demonstrated by connecting the proposed self-driving energy storage complex to a device designed as a wireless circuit. This system can work for seamlessly working for wireless signal transmittance as power source resulting and this system may be able to meet the urgent need for wearable electronics that implanted in the space suit and/or winter clothing needed in harsh climates.

A 2m-Range 711μW Body Channel Communication Transceiver Featuring Dynamically-Sampling Bias-Free Interface Front End.

Body Channel Communication (BCC) utilizes the body surface as a low-loss signal transmission medium, reducing the power consumption of wireless wearable devices. However, the effective communication range on the human body is limited in the state-of-the-art BCC transceivers, where the signal loss between the body surface and the BCC receiver remains one of the main bottlenecks. To reduce the interface loss, a high input impedance is desired by the BCC receiver, but the DC-biasing circuits decrease the input impedance. In this work, a dynamically-sampling IFE is proposed to eliminate the DC voltage bias, resulting in a 90kΩ high input impedance and a 94dB RF-IF conversion gain to reduce the interface loss in long-range BCC applications. The BCC transceiver chip is fabricated in 55nm CMOS process, taking a die area of 0.123mm2. Measured results show that the chip extends the BCC range to 2m for both the forward and backward paths, where the transmitter and receiver consume 711μW power in total.

Wireless Wearable Devices and Recent Applications in Health Monitoring and Clinical Diagnosis

Wireless Wearable Devices and Recent Applications in Health Monitoring and Clinical Diagnosis

Deformation Mechanisms of Inorganic Thermoelectric Materials with Plasticity

Flexible inorganic thermoelectric (TE) materials are beneficial to the development of wireless wearable devices due to their plasticity and high performance. Recently, many novel inorganic semiconductors with plasticity, such as ZnS single crystal, Ag2S‐based alloys, as well as single‐crystalline InSe and SnSe2, attract great attention. They are supposed to break the predicament between power output and mechanical deformability for TE systems with plasticity. To better understand the deformation of TE materials with plasticity and explore new systems, different deformation mechanisms for these plastically deformable inorganic TE materials are summarized. First, the concepts and requirements for inorganic TEs are presented with plasticity in wearable devices. Afterward, their mechanical properties and deformation mechanisms are elaborated including dislocation glide, interlayer slippage of atomic stacks, dual slippage of atomic stacks and dislocations, as well as phase transformation. Finally, a general outlook from previous research is suggested for forecasting the future directions. It is anticipated that this review can guide the design of inorganic TE materials with plasticity and provide insight into the wider application of plastically deformable inorganic semiconductors in electronic materials and devices.

Miniaturized 3‐Lead Electrocardiogram System Based on Einthoven Triangle for Wireless Cardiac Care Monitoring

Monitoring heart health typically requires a 12‐lead electrocardiogram (ECG) system with a sampling frequency of ≥400 Hz with high power requirements. This allows the detection of clinically relevant cardiovascular events using P, Q, R, S, and T signature peaks in ECG. These requirements limit the scope of ECG use, especially in proactive monitoring, cardiac event prevention, and wireless wearable devices. Herein, a skin‐mountable 3‐lead wireless sensor patch based on the Einthoven triangle is experimentally demonstrated. It allows us to gather vital cardiac information in place of a conventional 12‐lead ECG system. The patch uses the industry's lowest‐power Bluetooth low‐energy system on chip which requires 50 Hz sampling frequency to detect all critical events. A firmware for the chip is developed to transmit data wirelessly at extremely low power (−19 dB), resulting in uninterrupted operation for ≈4 days using a simple CR1632 battery. An Android app records and plots ECG data on a smartphone for real‐time visual monitoring and transmits the data to the cloud‐connected interface for remote monitoring. The demonstration with low‐power operation and integration to the cloud‐connected interface is a significant step for nonassisted home‐based caring to enable preventive health management without being widely constrained by battery and tethered operation.

Regulating long-range travelling electrons for simultaneous electromagnetic absorption and interference shielding of Co@C nanofibers

Multifunctional electromagnetic nanofibers are potential in wearable wireless devices. Manipulating the electromagnetic response of a material is a reasonable strategy for developing diversified electromagnetic functions. Herein, a multifunctional electromagnetic nanofiber (Co@C) is proposed. Due to the intrinsic local π-bonds, residual defects/groups, heterogeneous interfaces, and magnetic multiresonance, Co@C nanofibers show excellent electromagnetic attenuation performance. More importantly, a long-rang travelling electron regulation strategy is proposed to switch the electromagnetic function of the Co@C composites between electromagnetic absorption and interference shielding. The optimal electromagnetic wave absorption performance reaches −48.79 dB, and the maximum average electromagnetic interference shielding performance reaches 30 dB. As the simultaneous acquisition of electromagnetic absorption and shielding functions is of great significance for practical applications, patterned design is performed on the surface of the Co@C composites. A perfect electromagnetic resonant absorption band with an absorption coefficient near 1 appears. The patterned Co@C composite features frequency-selective absorption by customizing the geometric parameters of the pattern. This work breaks through the limitations of the single function of traditional electromagnetic nanomaterials and inspires the development of electromagnetic materials towards multiple functions.

Fetal Health Assessment Through Remote Fetal Phonocardiography and Electrohysterography: Wearable Wireless Device Using Shakti Processor

Pregnant women and neonates are the most affected demographic group by the shortfall of accessible healthcare. In this work, we aimed to improve obstetric facilities in rural India by proposing a remote maternal health monitoring device in which pregnant women can avail healthcare services without long-distance travel. We designed and developed a prototype of the proposed device using the open-source Shakti processor to provide remote real-time monitoring of the fetal heart rate and uterine contractions for the detection of any abnormalities. Fetal phonocardiography signal was picked up using an acoustic sensor while the electrohysterography signal was acquired using surface electrodes placed on a pregnant woman. The placement of the sensor and electrodes was referred by an obstetrician. The system was placed on 10 pregnant women in their last trimester with their informed consent and the monitored parameters were validated by a clinician. The obtained fetal heart rate and uterine contractions were compared with that from standard clinical devices and the results from the proposed device were observed to be comparable with that of the standard devices.

Maintaining soldier musculoskeletal health using personalised digital humans, wearables and/or computer vision.

The physical demands of military service place soldiers at risk of musculoskeletal injuries and are major concerns for military capability. This paper outlines the development new training technologies to prevent and manage these injuries. Narrative review. Technologies suitable for integration into next-generation training devices were examined. We considered the capability of technologies to target tissue level mechanics, provide appropriate real-time feedback, and their useability in-the-field. Musculoskeletal tissues' health depends on their functional mechanical environment experienced in military activities, training and rehabilitation. These environments result from the interactions between tissue motion, loading, biology, and morphology. Maintaining health of and/or repairing joint tissues requires targeting the "ideal" in vivo tissue mechanics (i.e., loading and strain), which may be enabled by real-time biofeedback. Recent research has shown that these biofeedback technologies are possible by integrating a patient's personalised digital twin and wireless wearable devices. Personalised digital twins are personalised neuromusculoskeletal rigid body and finite element models that work in real-time by code optimisation and artificial intelligence. Model personalisation is crucial in obtaining physically and physiologically valid predictions. Recent work has shown that laboratory-quality biomechanical measurements and modelling can be performed outside the laboratory with a small number of wearable sensors or computer vision methods. The next stage is to combine these technologies into well-designed easy to use products.

A data security scheme based on EEG characteristics for body area networks.

Body area network (BAN) is a body-centered network of wireless wearable devices. As the basic technology of telemedicine service, BAN has aroused an immense interest in academia and the industry and provides a new technical method to solve the problems that exist in the field of medicine. However, guaranteeing full proof security of BAN during practical applications has become a technical issue that hinders the further development of BAN technology. In this article, we propose a data encryption method based on electroencephalogram (EEG) characteristic values and linear feedback shift register (LFSR) to solve the problem of data security in BAN. First, the characteristics of human EEG signals were extracted based on the wavelet packet transform method and as the MD5 input data to ensure its randomness. Then, an LFSR stream key generator was adopted. The 128-bit initial key obtained through the message-digest algorithm 5 (MD5) was used to generate the stream key for BAN data encryption. Finally, the effectiveness of the proposed security scheme was verified by various experimental evaluations. The experimental results showed that the correlation coefficient of data before and after encryption was very low, and it was difficult for the attacker to obtain the statistical features of the plaintext. Therefore, the EEG-based security scheme proposed in this article presents the advantages of high randomness and low computational complexity for BAN systems.

Machine Learning Assisted Wearable Wireless Device for Sleep Apnea Syndrome Diagnosis

Sleep apnea syndrome (SAS) is a common but underdiagnosed health problem related to impaired quality of life and increased cardiovascular risk. In order to solve the problem of complicated and expensive operation procedures for clinical diagnosis of sleep apnea, here we propose a small and low-cost wearable apnea diagnostic system. The system uses a photoplethysmography (PPG) optical sensor to collect human pulse wave signals and blood oxygen saturation synchronously. Then multiscale entropy and random forest algorithms are used to process the PPG signal for analysis and diagnosis of sleep apnea. The SAS determination is based on the comprehensive diagnosis of the PPG signal and blood oxygen saturation signal, and the blood oxygen is used to exclude the error induced by non-pathological factors. The performance of the system is compared with the Compumedics Grael PSG (Polysomnography) sleep monitoring system. This simple diagnostic system provides a feasible technical solution for portable and low-cost screening and diagnosis of SAS patients with a high accuracy of over 85%.

Context-aware electromagnetic design for continuously wearable biosymbiotic devices

Imperceptible wireless wearable devices are critical to advance digital medicine with the goal to capture clinical-grade biosignals continuously. Design of these systems is complex because of unique interdependent electromagnetic, mechanic and system level considerations that directly influence performance. Typically, approaches consider body location, related mechanical loads, and desired sensing capabilities, however, design for real world application context is not formulated. Wireless power casting eliminates user interaction and the need to recharge batteries, however, implementation is challenging because the use case influences performance. To facilitate a data-driven approach to design, we demonstrate a method for personalized, context-aware antenna, rectifier and wireless electronics design that considers human behavioral patterns and physiology to optimize electromagnetic and mechanical features for best performance across an average day of the target user group. Implementation of these methods result in devices that enable continuous recording of high-fidelity biosignals over weeks without the need for human interaction.

Self-Powered Wireless Flexible Ionogel Wearable Devices.

Ionogels are promising soft materials for flexible wearable devices because of their unique features such as ionic conductivity and thermal stability. Ionogels reported to date show excellent sensing sensitivity; however, they suffer from a complicated external power supply. Herein, we report a self-powered wearable device based on an ionogel incorporating poly(vinylidene fluoride) (PVDF). The three-dimensional (3D) printed PVDF-ionogel exhibits amazing stretchability (1500%), high conductivity (0.36 S/m at 105 Hz), and an extremely low glass transition temperature (-84 °C). Moreover, the flexible wearable devices assembled from the PVDF-ionogel can precisely detect physiological signals (e.g., wrist, gesture, running, etc.) with a self-powered supply. Most significantly, a self-powered wireless flexible wearable device based on our PVDF-ionogel achieves monitoring healthcare of a human by transmitting obtained signals with a Bluetooth module timely and accurately. This work provides a facile and efficient method for fabricating cost-effective wireless wearable devices with a self-powered supply, enabling their potential applications for healthcare, motion detection, human-machine interfaces, etc.

A Self-Powered Wearable Sensor for Continuous Wireless Sweat Monitoring.

Wireless wearable sweat analysis devices can monitor biomarkers at the molecular level continuously and in situ, which is highly desired for personalized health care. The miniaturization, integration, and wireless operation of sweat sensors improve the comfort and convenience while also bringing forward new challenges for power supply technology. Herein, a wireless self-powered wearable sweat analysis system (SWSAS) is designed that effectively converts the mechanical energy of human motion into electricity through hybrid nanogenerator modules (HNGMs). The HNGM shows stable output characteristics at low frequency with a current of 15mA and a voltage of 60V. Through real-time on-body sweat analysis powered by HNGM, the SWSAS is demonstrated to selectively monitor biomarkers (Na+ and K+ ) in sweat and wirelessly transmit the sensing data to the user interface via Bluetooth.

Applications of Artificial Intelligence in Automatic Detection of Epileptic Seizures Using EEG Signals: A Review

Correctly interpreting an Electroencephalography (EEG) signal with high accuracy is a tedious and time-consuming task that may take several years of manual training due to its complexity, noisy, non-stationarity, and nonlinear nature. To deal with the vast amount of data and recent challenges of meeting the requirements to develop low cost, high speed, low complexity smart internet of medical things (IoMT) computer-aided devices (CAD), artificial intelligence (AI) techniques which consist of machine learning and deep learning plays a vital role in achieving the stated goals. Over the years, machine learning techniques have been developed to detect and classify epileptic seizures. But until recently, deep learning techniques have been applied in various applications such as image processing and computer visions. However, several research studies have turned their attention to exploring the efficacy of deep learning to overcome some challenges associated with conventional automatic seizure detection techniques. This paper endeavors to review and investigate the fundamentals, applications, and progress of AI-based techniques applied in CAD system for epileptic seizure detection and characterisation. It would help in actualising and realising smart wireless wearable medical devices so that patients can monitor seizures before their occurrence and help doctors diagnose and treat them. The work reveals that the recent application of deep learning algorithms improves the realisation and implementation of mobile health in a clinical environment.

The Impact of Volunteering in Festival on Life Rhythm: Taking Wireless Network Communication and Wearable Devices as Tools

It is important to understand the impact of volunteering in festival on life rhythm of a volunteer. Little attention has been paid to the impact of volunteering on volunteers’ behavior. This study is aimed at exploring volunteers’ life rhythm and makes a contrast with that of everyday life by wireless devices. Related theories include volunteers’ behavior, life rhythm of festival volunteers, and wearable devices. Data was collected with a wearable device for the advantage of mobility, flexibility, and instant feedback though there are obstacles such as limited bandwich, signal attenuation, and network congestion. The results show that sleep rhythm interacts with activity rhythm, and volunteering has an impact on the interaction. Volunteering takes the place of work or relaxation, so that the rhythm of sleep is different from that of daily life and is affected by both the circadian rhythm and social factors. But the waking time is advanced by social factors. Compared with other types of days, volunteer day presents the rhythm of early getting up and more time-consuming walking. The schedule in volunteering is obviously different from that of ordinary day, and the walking mileage is much higher than ordinary day. Volunteering has an overflowing effect on daily life and reduces the choice of outdoor activities after being a volunteer.

NUMERICAL EVALUATION OF SPECIFIC ABSORPTION RATE IN HUMAN HEAD AND TORSO FOR WEARABLE WIRELESS DEVICES IN UNDERGROUND MINE SCENARIOS.

The radiofrequency field levels may increase in confined environments, such as underground mines, due to reflections from inner boundaries (walls and arches). This study investigated the specific absorption rate (SAR) in the head and torso of a miner wearing a wireless device in underground mine scenarios. Two high-resolution voxel models were used to analyse the effects of a tunnel structure, a metal arch and a safety helmet at 2.4GHz and 868MHz. The results indicated that the SAR increase is modest for all simulated underground mine scenarios and was maximum in the presence of a metal arch. At 868MHz, some observed that the SAR was greater in deep tissues of the head and torso as compared to SAR at 2.4GHz. Also, the torso is a better site for mounting wearable devices on the body to mitigate exposure.

Driver vigilance detection for high-speed rail using fusion of multiple physiological signals and deep learning

With the popularity of high-speed rail in China and with faster operation speeds, the safety of railways has also received more attention. This paper proposes a vigilance detection method for high-speed rail drivers based on wireless wearable multi-physiological signals fusion and deep learning. The intended method consists of three components: wireless wearable signal acquisition equipment, physiological signal preprocessing and driver vigilance detection. In the initial stage, a wireless wearable device based on open source brain–computer interfaces was used to collect electroencephalogram, electrocardiogram and electromyogram signals in the high-speed rail simulation environment. Secondly, linear filtering, fast independent component analysis and wavelet filtering are performed on the three kinds of signals, and reasonable slicing is performed to make a dataset. Finally, a convolutional recurrent neural network with channel attention mechanism and memory ability is proposed. Multiple physiological signals from wireless wearable devices are used to train the network. This network improves the ability to recognize the vigilance of drivers and verifies the effectiveness of the combination of squeeze-and-excitation block and long short-term memory with convolutional neural network. Furthermore, the vigilance detection effectiveness was evaluated under different signal combinations; the testing set verified the accuracy of the network as 98.11%. Results prove the feasibility of the high-speed rail driver vigilance detection method based on multiple physiological signals and deep learning, which can help to avoid high-speed rail accidents.

A Personalized Compression Method for Steady-State Visual Evoked Potential EEG Signals

As an informative electroencephalogram (EEG) signal, steady-state visual evoked potential (SSVEP) stands out from many paradigms for application in wireless wearable devices. However, its data are usually enormous, occupy too many bandwidth sources and require immense power when transmitted in the raw data form, so it is necessary to compress the signal. This paper proposes a personalized EEG compression and reconstruction algorithm for the SSVEP application. In the algorithm, to realize personalization, a primary artificial neural network (ANN) model is first pre-trained with the open benchmark database towards BCI application (BETA). Then, an adaptive ANN model is generated with incremental learning for each subject to compress their individual data. Additionally, a personalized, non-uniform quantization method is proposed to reduce the errors caused by compression. The recognition accuracy only decreases by 3.79% when the compression rate is 12.7 times, and is tested on BETA. The proposed algorithm can reduce signal loss by from 50.43% to 81.08% in the accuracy test compared to the case without ANN and uniform quantization.