This paper discussed a frequency-dependent distributed beamforming technique to improve the reliability in implant multiple-input multiple-output (MIMO)-ultra wideband (UWB) communications. To realize distributed beamforming for implant MIMO-UWB systems, we propose an optimization method to theoretically determine the weight coefficients optimized for the implant UWB communication. To evaluate the performance improvement, the propagation characteristics of the implant channel in the low band UWB (3.4-4.8 GHz) were analyzed by the finite difference time domain (FDTD) method with a numerical human model. Subsequently, this study conducted computer simulations based on the derived propagation channel model. The evaluation results demonstrate that the proposed beamforming effectively improved the [Formula: see text] performance by 5 dB in a capsule endoscopy scenario, compared to the conventional method, which can improve implantable medical devices in the future.

It is important to determine efficient transmit power for ultra-wideband (UWB) communications, especially in implantable medical devices, to overcome large signal attenuation from a human body and to ensure reliable high-speed transmission. This paper investigates the optimization of transmit pulse waveform to improve the communication characteristics under two regulations: extremely low power (ELP) regulation and specific absorption rate (SAR). Furthermore, a sparse control method is introduced to further optimize the balance between the performance and system complexity for medical implant applications. The proposed system achieved an average throughput of 40.3 Mbps as compared with the average throughput of 5.7 Mbps of the conventional UWB communication system.

As a technical approach to safely and securely benefit from healthcare and medical care at home, ingestible and implantable medical and healthcare devices has been being actively studied. In particular, a cybernetic avatar (CA) inside a human body (in-body CA), which routinely monitors health status and performs medical examinations and treatments, is a crucial concept for the future of medicine and healthcare. While existing sensors and ingestible medical devices have distinctive features in sensing and therapeutic effects, the structuralization of the resulting human body internal environment information critically depends on location data of the in-body CA. This paper proposed a method for high-precision location estimation using quasi-static magnetic fields employed for wireless transmission signals from the in-body CA. Electromagnetic propagation inside a human body generally exhibits unstable propagation characteristics due to the dielectric properties of biological tissues. By utilizing quasi-static magnetic fields, the in-body CA localization becomes independent of the tissue dielectric properties and requires no calibration. Based on this principle, we introduced machine learning approaches essential for advanced localization. The findings of this study contributed to optimal selection of machine learning algorithms and training data reduction required for constructing the estimation model. The evaluation results with several kinds of machine learning algorithms demonstrated that Extra Trees Regressor achieved the best RMSE performance, accomplishing an accuracy of approximately 4.5mm. Furthermore, we demonstrated that localization with an error of less than 10 mm was achievable for coordinates with an accuracy of greater than 90% when using appropriately pre-measured magnetic field maps and selected machine learning algorithms.

In this paper, we propose a user mobility control method that improves data application throughput by relocating the Body Area Network (BAN) node to an optimal position where the throughput of intra-BAN (On-Body) and inter-BAN (Inter-Sink) communication topologies are compatible to the BAN mesh network. The effectiveness of the proposed user mobility control method was validated through evaluation, showing significant performance improvements. The technology that connects multiple BAN nodes to Wi-Fi mesh networks has been proposed to provide excellent communication quality at a low cost and in real-time. The user mobility control method can improve communication quality by leveraging users' mobility, which holds promise for enhancing communication quality. Various heuristic methods for user mobility control have been proposed, but their throughput performance is likely to be poor because these methods cannot decide where to move considering both On-body and Inter-Sink communications in a BAN mesh network. In BAN mesh networks, On-Body communication and Inter-Sink communication have a trade-off relationship depending on distance. Therefore, mobility control must consider both types of communication simultaneously. Our proposed method considers On-Body communication and Inter-Sink communication independently and moves them to where the amount of overflow between their optimal positions is minimized. Using a spring model, the movement destination is whereby the springs are balanced based on the amount of traffic overflow optimization. The proposed method was evaluated for the initial position of the BAN nodes in terms of three variables: the volume of traffic, the branching angle theta with the root node and the distance d from the root node. In our evaluation, there was an improvement of 15.7% when d and theta were small. With small d and larger theta , there was an improvement of 54.8%. Furthermore, when d is large, the method showed a 12.3% improvement for small theta, and a 39.2% improvement for large theta.

In recent years, remote monitoring (RM) where stored pacemaker’s information is sent from home to hospitals, has become widespread. This leads to reduced burden of outpatient visits, and it is also possible to obtain useful information by analysing the large amounts of data stored with RM. However, two issues exists; the data format for RM is provided in different types of document data, which is time consuming to extract and aggregate the data manually by the clinical staff. In addition, RM reports are done once a month, resulting in only 12 reports a year, so as they are scarce, we need to unify reports for various types of patients. However, the data aggregation would require excessive workload for the healthcare professionals, therefore it is difficult to unify different types of patient data with the current RM.To solve these critical problems for the pacemaker, this study developed a pacemaker battery level estimation using a large language model (LLM) and remote monitoring historical data. With the aid of LLM, no coding is required as a relational database could be run fully automated, which should be a strong merit for non-engineers, namely, doctors and clinical staff. Based on the interactions through the LLM-based system, we can find similar patient data from the relational database easier, without the need for any technical skills. For evaluating the developed system, RM files were created for 30 people, simulating the discharge characteristics of pacemakers, and a high-order approximation curve of the characteristics was calculated from the RM history information using the OpenAI’s generative pre-trained transformers (GPTs). The optimal order approximation curve was then selected using the Akaike information criterion (AIC), and a pacemaker battery remaining capacity prediction system was created. As a result, gpt-4o-latest showed the lowest root mean square error (RMSE), with a median value of 0.0124mV, which demonstrated that data analysis integrating RM and LLMs could become feasible in future pacemaker condition monitoring.

Peripheral circulatory failure refers to a condition in which the blood flow through superficial capillaries is markedly reduced or completely occluded. In clinical practice, nurses strictly adhere to regular repositioning protocols to prevent peripheral circulatory failure, during which the skin condition is evaluated visually. In this study, skin colour changes resulting from pressure application were continuously captured using a camera, and supervised machine learning was employed to classify the data into two categories: before and after pressure. The evaluation of practical colour space components revealed that the h component of the JCh colour space demonstrated the highest discriminative performance (Area Under the Curve (AUC) = 0.88), followed by the a* component of the CIELAB colour space (AUC = 0.84) and the H component of the HSV colour space (AUC = 0.83). These findings demonstrate that it is feasible to quantitatively evaluate skin colour changes associated with pressure, suggesting that this approach can serve as a valuable indicator for dimensionality reduction in feature extraction for machine learning and is potentially an effective method for preventing pressure-induced skin injuries.

The low-cost passive intelligent reflecting surfaces inkjet-printed on paper substrates are proposed to shape electromagnetic wavefronts and control reflected wave directions in the millimeter-wave (mmWave) and sub-terahertz (sub-THz) bands. Unlike conventional IRSs with circuit elements, an FPGA, and a DC supply, the passive IRSs use inexpensive paper substrates and conductive nanoparticle silver ink, achieving a phase gradient over a 2 pi period. Simulations and measurements show that these passive IRSs steer incoming mmWave and sub-THz signals to anomalous reflection angles. For a 0 degrees incidence angle (theta i), a 28-GHz-band IRS reflects an incident wave to a 30 degrees anomalous reflection angle, approximate to 12 dB higher than that of copper plate. Another 100-GHz prototype shows an 8 dB higher anomalous reflection than a copper plate. IRS beamforming efficiency is related to printing resolution and substrate thickness, crucial in realistic wireless communication environments. These passive IRSs can direct communication signals to non-line-of-sight locations, extending coverage for current and next-generation wireless networks. The low-cost, simple fabrication process supports potential mass production, facilitating widespread adoption in future wireless communication systems like the internet of things and 6 G.

In this study, we propose and evaluate a centralized mobility control method that determines the optimal positions of BAN (Body Area Network) nodes to maximize the network throughput in BAN ad hoc networks. Recently, BAN ad hoc networks using Wi-Fi connections between BAN nodes have gained attention as a low-cost, high-throughput solution. To enhance communication quality, a user mobility control method has been proposed that considers the trade-off between communication within each user’s BAN and that between different BAN users, both affected by distances between involved BAN nodes. However, the previous method is decentralized, where each user independently determines its optimal position without considering other users, thereby preventing global optimization of the network and resulting in limited performance. To address this issue, our method adopts a centralized scheme that determines the destinations of all nodes collectively. Specifically, a spring model is used in which two types of virtual springs-each corresponding to the above two communication types-are placed between BAN nodes. The points where the spring forces are balanced are then used to determine the optimal positions of the nodes. In this spring model, changes in trade-off factors caused by node movement are reflected as forces through the springs, allowing the system to collectively determine the globally optimal positions of all users under the assumption that all participants are involved in the control. In numerical evaluations, the proposed method achieved about twice the throughput of the previous method in dense scenarios and showed even larger gains in sparse ones.

Recently, the measurement of daily using wearable devices has been attracting attention due to increasing health awareness. The importance of vital data may vary depending on the type of data generated. Daily body temperature or physical activity data are less important, while urgent data such as seizures and falls are more important. By prioritizing data according to their importance, improving the quality of communication of highly important data is necessary. In this paper, we propose a communication scheme that improves on the priority-controlled communication scheme specified as Multi-use Channel Access (MCA) mode in SmartBAN, which is standardized by ETSI and IEC. We also demonstrate the usefulness of the proposed scheme by implementing the SmartBAN scheme and the proposed scheme on actual devices and experimentally evaluating their characteristics.

Hemodialysis therapy is an extracorporeal circulation treatment that serves as a substitute for renal function. In Japan, patients receive this efficient four-hour treatment, three times per week, allowing them to maintain a social life nearly equivalent to that of healthy individuals. Before the treatment, two punctures are performed to establish extracorporeal circulation, and a high blood flow rate is essential to ensure efficient therapy. Specialized blood vessels created through arteriovenous fistula (AVF) surgery are utilized to achieve high blood flow rates. Although the AVF allows safe and efficient dialysis treatment, AVF stenosis leads to a serious problem in dialysis. To early detect this abnormal blood flow, auscultation and palpation methods are widely used in hospitals. However, these methods can only provide qualitative judgment of the AVF condition, so the results cannot be shared among other doctors and staff. Additionally, since the conventional methods require contact with the skin, some issues require consideration regarding infection and low reproducibility. In our previous study, we proposed an alternative method for auscultation using non-contact optical imaging technology. This study aims to construct a reliable AVF stenosis detection method using Thrill waveform analysis based on the developed non-contact device to solve the problem with the contact palpation method. This paper demonstrates the performance validation of the non-contact imaging in the normal AVF group (206 total data, 75 patients, mean age: 69.1 years) and in the treatable stenosis group (107 total data, 17 patients, mean age: 70.1 years). The experimental results of the Mann-Whitney U test showed a significant difference (p=0.0002) between the normal and abnormal groups, which indicated the effectiveness of the proposed method as a new possible alternative to palpation.

To improve the quality of medical treatment using implantable devices, accurate location information for implantable devices must be acquired and reliable implant communications for vital data transmission must be ensured. This paper focuses on an electromagnetic (EM) imaging-based method using scattered EM fields, which enables implant device localization without requiring any information regarding the human body structure. However, in conventional EM imaging-based methods, it is difficult to accurately estimate the capsule location at high frequencies because of significant signal attenuation. Herein, we propose an EM imaging-based localization method enhanced by peak-formed incident electric fields generated by overlaying multiple EM waves. Our proposed method utilizes information regarding scattered electric fields by sweeping the peak location without increasing the measurement points. The performance improvement by the proposed method was evaluated in two- and three-dimensional computer simulations such that the proposed EM imaging-based localization can be further optimized to achieve precise estimation accuracy at a high frequency of 2.4 GHz.

The Japanese and Finnish healthcare systems have several longstanding challenges from the scattered data in storing databases due to location sensitivity and sometimes unequal services for their users. In addition to the data itself, location plays another role for the citizens living in urban or rural areas. They suffer from different well-being outcomes as stress and sedentary lifestyles have presented negative impacts on the urban dwellers. As remote work and technological solutions have become more common, in this conceptual research, we explore the general healthcare and living area challenges and how to make services more equal to everyone. We also discuss the possible telehealth solutions and how, for example, wearable body sensors' use could offer improvements to the availability and accessibility of healthcare services.

Managing medication status solves related complications and prevents increases in medical costs due to the improper management of prescriptions. An ingestible sensor can be used to confirm a patient’s real-time medical status by measuring the electromagnetic waves transmitted from an ingested medication from outside of the human body. However, concerns about costs of delivery arise, as it would be necessary to attach a sensor to each ingested medication. In this study, we focused on using an electromagnetic (EM) imaging method which can estimate the internal structure of various objects using a scattered electric field. With this method we can detect medication as it does not require the installation of a sensor. At first we performed an electromagnetic field simulation and based on the results we experimentally measured the external electric field, which changes with the medicine. Then, we evaluated the accuracy of the detection method by calculating the difference between the detection rate with the proposed detection method against a more conventional method. The results indicate the possibility of achieving a more than 20% higher accuracy than the conventional detection method with our proposed method using electromagnetic waves.

Hemodialysis therapy is an extracorporeal circulatory therapy that substitutes kidney function. At the beginning of therapy, special vessels called arterio-venous fistula (AVF) should be created, which puncture the dilated veins and allow for stable treatment. However, as they are prone to stenosis, a stethoscope is usually used to confirm the procedure before treatment. Although this method is simple and reliable, there are several critical issues to be resolved, such as missed stenosis sounds and difficulties in quantification. In our previous study, we proposed a new non-contact method to quantitatively confirm the AVF state using optical technology. In this study, the same method was used to represent the process from normal state to intense stenosis state in one hemodialysis patient using luminance and grayscale transformation to express the state of blood flow in colour, whose feasibility was verified through multiple comparison tests with ultrasound equipment. The results showed that in the multiple comparison test with Honest Significant Difference, all pairs without significant differences; however, there was a predominant difference between normal and intense stenosis on June 29 (II) and July 25 (V) (p = 0.038). On July 20, when mild stenosis was confirmed by ultrasound equipment findings, stenosis could be determined visually by the luminance and tone conversion. In the quartile evaluation by measurement date, the difference between the first and third quartiles was large on March 7 and June 29, and gradually decreased from June 6. This was considered to reflect the increase in internal pressure with the progression of stenosis and the suppression of venous wall pulsation due to the progression of vessel wall elongation. These results indicated that this method has the potential to visualise the stenosis site.

Accurately obtaining a patient’s respiratory rate is crucial for promptly identifying any sudden changes in their condition during emergencies. Typically, the respiratory rate is assessed through a combination of impedance change measurements and electrocardiography (ECG). However, impedance measurements are prone to interference from body movements. Conversely, a capnometer coupled with a ventilator offers a method of measuring the respiratory rate that is unaffected by body movements. However, capnometers are mainly used to evaluate respiration when using a ventilator or an Ambu bag by measuring the CO2 concentration at the breathing circuit, and they are not used only to measure the respiratory rate. Furthermore, capnometers are not suitable as wearable devices because they require intubation or a mask that covers the nose and mouth to prevent air leaks during the measurement. In this study, we developed a reliable system for measuring the respiratory rate utilizing a small wearable MOx sensor that is unaffected by body movements and not connected to the breathing circuit. Subsequently, we conducted experimental assessments to gauge the accuracy of the rate estimation achieved by the system. In order to avoid the effects of abnormal states on the estimation accuracy, we also evaluated the classification performance for distinguishing between normal and abnormal respiration using a one-class SVM-based approach. The developed system achieved 80% for both true positive and true negative rates. Our experimental findings reveal that the respiratory rate can be precisely determined without being influenced by body movements.

In this review, we envisioned how the new ETSI SmartBAN wireless technology for smart body area networks can be utilised in implant and in-body communications. The use cases can be links, for example, between in-body medical devices (IMDs) such as wireless capsule endoscopes and their on-body counterpart, or between smart implants, in general. First, we briefly introduced the current trends of implant and in-body communications and showed some application examples of IMDs utilised in in-body wireless communications. The advantage of in-body ultra wideband communications was then discussed to highlight the ETSI SmartBAN approach for in-body communications standardisation.

In recent years, demand for healthcare IoT including daily healthcare monitoring has been increasing. Body area network (BAN) is expected to be a core technology to realize such healthcare IoT. In particular, SmartBAN, a BAN technology standardized by the European Telecommunications Standards Institute (ETSI) and the International Electrotechnical Commission (IEC), is attracting attention. SmartBAN uses the 2.4 GHz ISM band, which may receive interference from various electronic devices such as wireless LAN and Bluetooth. In this paper, the influence of radio wave interference during the initial connection process of SmartBAN is experimentally evaluated by generating radio interference using devices that simulate an adaptive frequency hopping for Bluetooth. We measured the initial connection time when changing the hopping frequency and transmission timing of the interference wave sources and evaluated the effects of interference on the initial connection procedure of SmartBAN. The evaluation results revealed that although the connection time when there is interference may become slightly longer, it does not significantly affect the establishment of the initial connection.

Remote and home care, 24/7 monitoring, and other new procedures are modernizing the processes of professional care as well as enabling better self-monitoring capabilities for health and wellbeing enthusiastic. Due to the aging, societies all around the world are coming to lack of decent healthcare services. Thus, any relief in nursing work is preferred. Wireless technology will be an enabler to accelerate this paradigm change. Monitoring person's own vital signs is not tight to a specific location but data collection can be done real-time wherever and whenever. Also, numerous sensors can be used to collect appropriate vital data. Automated data collection enables continuous monitoring of health status or progress of certain symptom, but enables also efficient data post-processing and access to health data ubiquitously. European Telecommunications Standards Institute's (ETSI) SmartBAN is the newest wireless technology dedicated to body area networks to transfer vital information in reliable manner and with low power consumption. Not only used for outpatient, the SmartBAN is also available for use in different hospital, nursing home, training, etc. procedures.

A remote monitoring system that periodically transmits information stored in the pacemaker from patients' homes to a hospital is now in widespread use. However, the system requires access to the vendor's cloud server via a browser and consists of date-by-date PDF files, making the creation of aggregate data a significant burden. Since the release of commercially available systems such as ChatG PT, various large language models (LLMs) have been widely used, leading to that semantic search, which can perform searches that take into account the meaning of language, has attracted attention. In this study, we constructed an LLM - based remote monitoring system. Then, as a preliminary evaluation, we examined its effectiveness for RM operations based on the accuracy of RM data aggregation and work time.

Body Area Networks (BANs) have emerged as a crucial technology for continuous physiological data collection and communication between wearable sensors and external devices. The limitations of traditional radio frequency (RF) communications in BANs have led researchers to explore visible light communication (VLC) as a promising alternative. VLC offers non-ionizing properties, making it safe for human use, unlike RF waves at higher frequency bands. This paper highlights the potential of VLC in revolutionizing healthcare monitoring within BANs and emphasizes the importance of this technology in enhancing the performance and reliability of healthcare applications. Furthermore, it discusses the standardization activities of ETSI SmartBAN, showcasing its vision for the use of VLC technology for future healthcare and wellness applications.