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Body sensor networks (BSN) have emerged as an active field of research to connect and operate sensors within, on or at close proximity to the human body. BSN have unique roles in health applications, particularly to support real-time decision making and therapeutic treatments. Nevertheless, challenges remain in designing BSN nodes with antennas tha...
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... Ideally, nodes of BSN should operate reliably for long periods of time (i.e., days and months) without recharging/replacing its battery, but yet miniature in size. Since the communication range of the BSN nodes commonly targets to be less than 2 m, gateway devices are often needed to connect them to remote stations and data repositories. Fig. 1 shows a body control unit (BCU) (e.g., a smart phone) that is used as a gateway to transmit the collected data from sensor nodes to remote stations (e.g., a remote PC) using a second wireless link. Through a gateway device, sensor devices worn or implanted in the human body can be connected to the ...
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... BSN sensor, the most effective way to reduce power consumption of the sensor is to implement the signal processing and pattern recognition routines on the sensor node. The amount of energy required to transmit the data (which is about 60 mW when using a Texas Instrument CC2420) or store the data on an EEPROM (which is about 75 mW when writing to Fig. 10. Typical AR process for inferring activities from BSN sensors. To infer the activity, the raw sensor data will be preprocessed to remove noise and artefacts, and features will be extracted and selected/projected prior to ...
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... from identifying Activity of Daily Living and physical ex- ercises for wellbeing applications, AR is often used to estimate the context in which the measurement is made [75]. A typical AR process mainly consists of four stages of processing, namely preprocessing, feature extraction, feature selection/projection, and classification, as shown in Fig. 10 [76]- ...
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... charging or using energy-harvesting methods to provide a continuous power source will enable a complete self- sustaining body sensor device for medical monitoring. For ex- ample, WISP sensor node in Table I is an RFID-based sensing mechanism operating without a battery [132]. Fig. 11 shows a general block diagram representing a body sensor with energy harvesting. The power is recovered from the external ambient such as solar and RF or vibration and motion from the human body and then regulated or stored to provide power supply for the wireless body sensor ...
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... For implantable medical sensors, the en- ergy is provided to the body sensor device wirelessly using inductive links. The charging transmitter should be very close to the patient's skin to charge or energize the body sensor device [133]. In these systems, a low-frequency transmission has been attractive to eliminate the absorption of more energy. Fig. 12 shows a wireless body sensor prototype with energy- harvesting unit placed on the arms for distributed biometric monitoring [52]. The system is based on a solar energy source to store the harvested energy in a super capacitor using photo- voltaic (PV) panels for powering the wireless sensor node. The work demonstrates that the wearable ...
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Self-powered or autonomously driven wearable devices are touted to revolutionize the personalized healthcare industry, promising sustainable medical care for a large population of healthcare seekers. Current wearable devices rely on batteries for providing the necessary energy to the various electronic components. However, to ensure continuous and...
Citations
... This system provides a robust platform for applying the trained SVM classifier to accurately classify EEG signals based on attentional states (Poon et al., 2015). Our results demonstrate the effectiveness of our method in distinguishing These waves, each characterised by different frequency ranges, serve as indicators of different cognitive states and processes. ...
Brain-computer interface (BCI) systems are highly valued for their applications in medicine and biology, where accurate analysis of attention levels and electroencephalography (EEG) data is crucial for the success rate of BCI systems. To improve this aspect, we propose to perform attention level determination prior to the execution of the BCI system. To accurately determine the level of attention, we propose a novel feature extraction approach that involves multidimensional feature extraction across multiple frequency bands of EEG data. The multi frequency band waveforms extracted using this method can be cross validated, thereby increasing the robustness of our research. We used publicly available datasets to train a Support Vector Machine (SVM) classifier to develop an efficient attention detection system, and developed a system for collecting EEG data to validate attention levels. The effectiveness and accuracy of the multidimensional feature extraction method was validated by classifying the data collected by the attention detection system. This study highlights the potential of integrating attention detection into BCI systems, pathways for advances in brain science research.
... is important for implantable applications, including WCE, as it allows simultaneous transmission of data and reception on separate frequency bands. This simultaneous dual-band operation enhances communication reliability and flexibility within the human body, optimizing the overall performance of implantable devices [7], [8]. In WCE, a dual-band antenna is crucial to enable simultaneous data transmission and wireless power transfer. ...
This study introduces a small dual-band implantable antenna designed for Wireless Capsule Endoscopy (WCE). This antenna operates in the Wireless Medical Telemetry Service (WMTS) 1.4 GHz as well as the Industrial, Scientific, and Medical (ISM) 2.45 GHz bands. The designed antenna achieves miniaturization by employing open-ended slots, shorting pins, and arc-shaped slots on the radiating patch. The final structure has a compact size of 8.2 x 8.2 x 0.635 mm
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. Homogeneous muscle and heterogeneous human phantoms are used to analyze the antenna’s behavior. The measurements are conducted by implanting the WCE device within minced pork meat. Considering the capsule’s deep location, the proposed antenna achieves gain values of -29.4 dBi at 1.4 GHz and -30.4 dBi at 2.45 GHz, respectively. The measured impedance bandwidths are 7.2 % and 4.2 % at 1.4 GHz and 2.45 GHz, respectively. Results suggest that the proposed antenna can reliably establish wireless communication at distances greater than 10 meters with a 10 dB margin for both frequencies. The 10-g specific absorption rate (SAR) values are 4.77 W/kg and 6.07 W/kg at 1.4 GHz and 2.45 GHz, respectively.
... Given increasingly specialized workplaces, which can only be depicted by fewer and fewer people, human resources are increasingly important in this context [144,145]. In addition, monitoring during an assignment or operation is a tool with which management tasks can be better supported, especially when deciding whether an individual or a group can still carry out an assignment [146][147][148]. In addition, monitoring can be used to recognize dangerous situations in time and to derive adequate action [149]. ...
Collecting and recording physiological parameters and forwarding this data via telemedicine or remote surveys may significantly increase the health and safety of people working under extreme environmental conditions, like firefighters, mountain rescuers, disaster control employees, military personnel, etc. First, ruggedized systems, as small, lightweight, and flexible as possible, are needed to optimize the goal of increasing occupational health and safety of persons at risk and maximizing their performance during missions. Previous systems could only record a minimal number of parameters, and a few could forward the data online to return information to the user. Therefore, an extension of existing systems is required to record several meaningful, valid, and reliable physiological parameters synchronized. This review highlights this problem and gives an overview of the historical development and the actual status of the physiological systems available. In the end, a proven and promising solution is presented that can solve the problem and allow people to work more safely in extreme environments in the future.
... The era of big data has put a new veil on the old traditional literary research and created a new myth for the study of English and American literature, in which scholars can easily cross time and space, seek unique information and eliminate the geographical limitations, as if 'hypertext' breaks free from the limitations of printed length [6][7]. Therefore, only by combining relevant informational tools to understand the thoughts and emotions behind the language art hidden by the creators of the works the research effect and research value of the language art of English and American literary works can be enhanced. ...
This paper first analyzes the emotional features of English and American literary works by combining semiotics of interactivity, language class emotional context and thinking operation model. Then, a B-Feature-BP text emotion feature construction model is constructed on the basis of the BRET model combined with the BP neural network, and the emotion features of English and American literary works are constructed by combining semiotics and the thought operation model. Then, based on the multi-task learning method in deep learning, a multi-task MT-GSU model is proposed to classify and recognize the emotional features of constructed text. Finally, the performance of constructing, classifying and recognizing the emotional features of English and American literary works in this paper is analyzed so as to analyze the emotional features of English and American literary works. The results show that the performance of the constructed emotional features is all greater than 0.8, and the emotional features of the characters, the environment, and the whole of the English and American literary works are above 0.87, and the classification time is between [0.338,0.721]s. The intensity of tendency of the characteristics of the emotions of the works of English and American literature is [0.68,0.78], the intensity of stability is [0.6,0.74], the intensity of profundity is [0.71,0.79], and the intensity of efficacy is [0.72,0.82]. This study has a positive impact on the appreciation and translation of English and American literary works.
... According to World Health Organization (WHO), the number of people who are older than 65 is estimated 524 million in 2010, and this number will reach 1.5 billion in 2050. Therefore, more hospitals and care equipment must care for the elderly and the sick in the future [1]. On the other hand, the costs of hospitalization and nursing of these patients in traditional and existing methods are very high. ...
... Wireless Body Area Networks (WBANs) are an efficient solution that can play a key role for remote care of patients [1]. WBANs consist of a set of tiny sensors that are placed inside or on the patient body. ...
Wireless Body Area Networks (WBANs) are one of the essential remedies for patient and elderly care. Due to the ageing population in the coming years and the occurrence of unknown and epidemic diseases in the future, the need for a mechanism such as WBANs to manage the limitation of hospital resources is felt more than ever. However, WBANs seriously face energy limitations, and the battery nodes in these networks have limited energy. Unlike other electronic equipment, replacing or charging the batteries of WBANs is not easily possible. Therefore, it is necessary to reduce the energy consumption in these networks and extend their lifetime. Although several methods have been proposed to reduce energy consumption, the critical point that has not been considered in previous methods is the similarity of the data. In this regard, in this paper first, a comprehensive study is done on the real data of MIMIC dataset to inspect data similarity in transferred data of WBANs. Then, an efficient method called Shorted Repeating Code Modulation (SRCM) based on data similarity is proposed. SRCM has four different steps of sampling, quantization, encoding and compression and uses the similarity of data to reduce energy consumption. This is done by reducing the amount of data sent during the sampling step. Data similarity is recognized by cross-correlation process to prevent transferring similar data. Simulations using MATLAB show that the use of SRCM has minimized the energy consumption of the network by 85.7%.Simulations using MATLAB show that the use of SRCM has minimised the energy consumption of the network by 85.7%.
... Therefore, these networks save a lot of medical costs and even make life easier for patients and the elderly [1]. But Wireless Body Area Networks (WBANs) face a number of limitations, including sensor processing power and sensor memory limitations, as well as battery limitations [2]. ...
... One of the main problems with WBANs is that these sensors are embedded in the body and must work for a long time without replacing or charging the battery, so the energy consumption of these sensors must be reduced as much as possible to extend the life of the sensors and allow them to operate for longer without replacing the battery [2]. For this purpose, several methods have been proposed to increase the lifespan of these networks, methods such as data compression and adaptive sampling and the use of more optimal network topology and so on. ...
... Several soft exoskeletons are already being used to address gait related disorders. Wearables should also take care of energy efficiency and enhanced battery life [121,122]. The energy-efficient wearables are a current focus of research [123]. ...
... IoT, coupled with artificial intelligence and machine-learning algorithms, can play a significant role in the healthcare industry [1]. IoT technologies can help build fully automated medical equipment and systems for tracing, tracking, alerting, identification, responding on time, etc., in modern healthcare applications [2]. Telemedicine in healthcare can be improved by incorporating IoT technologies in hospitals and nursing homes [3]. ...
IoT-based wearable devices are significantly contributing to personalized and pervasive modern healthcare. Traditional healthcare systems are handicapped by several factors, including shortages of physicians, nurses, healthcare devices, hospital beds, healthcare cost, etc. Conventional therapy is carried out either at a hospital or at home by certified therapists which is not affordable for many in developing nations. In this research work, we present IoT-based monitoring and an evaluation of key parameters and indicators of a wearable device used during the rehabilitation process in stroke patients’ hand therapy which can be operated by the users at home without the need for therapists. Sensors along with a controller board are used for signal acquisition, processing, and monitoring. The efficiency of therapy can be increased through real-time follow-up and feedback from therapists. Three different control methods are proposed and studied: smartphone-based speech, smartphone-based touch, and Internet of Things (IoT)-based dashboard. In addition, four different architectures, including: therapist therapy wearable, dual therapy wearable, user therapy wearable, and multiuser therapy wearable architectures are discussed. A rehabilitation therapy-based case study using the proposed wearable device with multiple volunteers was conducted. Therapists can remotely operate the device and train the users. Users can benefit and save on costs without the need to visit hospitals or therapy centers or hire therapists. Based on the results of the experiments with volunteers, we can confidently say that the proposed IoT-based wearable device can enhance the quality of life and well-being of the users.
... Additional components (harmonics) in the frequency spectrum suggest a loose prosthesis. In a variety of applications, including implants and other biomedical applications, there is a need for a sensor that can operate passively and wirelessly [33,34]. The implanted temperature sensor requires an external regulator to maintain a voltage below 5 V, as high-power consumption might affect temperature readings owing to the heat created by the telemetry circuit and eddy currents. ...
... The impulse response h(t) for a SAW sensor can be defined in the time domain as follows, and the corresponding Fourier transformation of the impulse response is shown as H(f ) [32,34]: ...
In this paper, a surface acoustic wave (SAW) sensor for hip implant geometry was proposed for the application of total hip replacement. A two-port SAW device was numerically investigated for implementation with an operating frequency of 872 MHz that can be used in more common radio frequency interrogator units. A finite element analysis of the device was developed for a lithium niobate (LiNBO3) substrate with a Rayleigh velocity of 3488 m/s on COMSOL Multiphysics. The Multiphysics loading and frequency results highlighted a good uniformity with numerical results. Afterwards, a hip implant geometry was developed. The SAW sensor was mounted at two locations on the implant corresponding to two regions along the shaft of the femur bone. Three discrete conditions were studied for the feasibility of the implant with upper-and lower-body loading. The loading simulations highlighted that the stresses experienced do not exceed the yield strengths. The voltage output results indicated that the SAW sensor can be implanted in the hip implant for hip implant-loosening detection applications.
... Increasing evidence shows that the human brain is a nonlinear dynamic system [30], and the EEG signals measured from it are nonlinear and time varying; thus, it is challenging to determine its complete statistical characteristics in the time or frequency domain [31;33]. Few studies considered the nonlinear characteristics of EEG. ...
Detecting the mental state of a driver using electroencephalography (EEG) signals can reduce the probability of traffic accidents. However, EEG signals are unstable and nonlinear and fatigue detection based on one-dimensional features may provide insufficient information, resulting in low recognition efficiency. To resolve these challenges, this paper proposes an EEG-based multi-dimensional feature selection and fusion method to recognise mental fatigue in drivers. First, the EEG signals were decomposed into α, β and θ bands, and then the corresponding time domain, frequency domain and nonlinear features were generated respectively. Furthermore, a three-layer feature-selection method based on Logistic Regression, one-way Analysis of Variance and Recursive Feature Elimination (logistic-ARFE) was proposed to solve the feature redundancy. Logistic-ARFE is designed to automatically select the optimal subset of mental fatigue features. Principal component analysis was used to fuse the optimal feature subset from different dimensions to obtain the fusion feature at a cumulative contribution ratio of 90%, which was used as the final feature to express the recognition accuracy of eight conventional machine learning models. A publicly available EEG dataset for driver fatigue was used to validate the proposed method. The final results show that six of the eight models achieve high recognition accuracy, which indicates that the Logistic-ARFE feature selection algorithm has applicability widely. In particular, compared with other studies using the same dataset, the Gaussian SVM proposed in this study based on time–frequency-nonlinear fusion features achieves the highest recognition accuracy, which is improved by 6.32% and 6.11% respectively.
