Search Results (38)

Implantable telemetry biosensors have become powerful tools for continuous physiological monitoring with minimal animal perturbation. However, commercially available implants are relatively oversized for small animals such as mice and have limited transmission range, leading to concerns about animal welfare, experiment scenarios, and the reliability of the data. In this study, we designed a telemetry system that tracks the animals’ body temperature and locomotor activity in real time. The implant integrates a temperature sensor with a 3-axis accelerometer and is capable of wirelessly transmitting data over a 40 m mesh network. The implant’s temperature performance was evaluated in bench tests, showing a response rate of 0.2 °C/s, drift ≤ 0.03 °C within 31 days, and a standard deviation of 0.035 °C across three identically designed implants. Meanwhile, the in vivo implant’s locomotion recordings showed strong agreement with computer vision analysis with a correlation coefficient of r = 0.95 (p < 0.001), and their body temperature recordings were aligned to differential states of rest, exercise, or post-exercise recovery. The results demonstrate stable and highly accurate performance over the 30-day implantation period. Its ability to minimize behavioral interference while enabling long-term continuous monitoring highlights its value in both biomedical and animal behavior research. Full article

This study examines how the effective dielectric characteristics of the human torso affect the carrier-link-margin (CLM) and data-link-margin (DLM) of a biocompatible gelatin-encapsulated implantable medical device (IMD) that consists of a small implantable antenna, battery, printed circuit board (PCB), camera, and sensor operating at 2.5 GHz. The specific absorption rate (SAR) and the radio frequency (RF) link performances of the IMD are tested for ±20% changes in reference to the mean values of the effective relative permittivity, ɛ_eff, and the effective conductivity, σ_eff, of the human body model. An artificial neural network (ANN) with two inputs (ɛ_eff, σ_eff) and five outputs (SAR_1 g, SAR_10 g, fractional bandwidth, CLM, and DLM) is trained by 80% of the total scenarios and tested by 20% of them in order to provide reliable dependent analyses. The highest changes in 1 g SAR value, 10 g SAR value, fractional bandwidth, CLM, and DLM at a 4 m distance for 100 Kbps are 63%, 41.6%, 17.97%, 26.79%, and 5.89%, respectively, when compared to the reference effective electrical properties of the homogeneous human body model. This work is the first to accurately depend on the electrical analyses of the human body for the link margins of an implantable antenna system. Furthermore, the work’s uniqueness is distinguished by the application of the CLM and DLM principles in the sphere of IMD communication. Full article

For individuals with spinal cord injuries (SCIs) above the midthoracic level, a common complication is the partial or complete loss of trunk stability in the seated position. Functional neuromuscular stimulation (FNS) can restore seated posture and other motor functions after paralysis by applying small electrical currents to the peripheral motor nerves. In particular, the Networked Neuroprosthesis (NNP) is a fully implanted, modular FNS system that is also capable of capturing information from embedded accelerometers for measuring trunk tilt for feedback control of stimulation. The NNP modules containing the accelerometers are located in the body based on surgical constraints. As such, their exact orientations are generally unknown and cannot be easily assessed. In this study, a method for estimating trunk tilt that employed the Gram–Schmidt method to reorient acceleration signals to the anatomical axes of the body was developed and deployed in individuals with SCI using the implanted NNP system. An anatomically realistic model of a human trunk and five accelerometer sensors was developed to verify the accuracy of the reorientation algorithm. Correlation coefficients and root mean square errors (RMSEs) were calculated to compare target trunk tilt estimates and tilt estimates derived from simulated accelerometer signals under a variety of conditions. Simulated trunk tilt estimates with correlation coefficients above 0.92 and RMSEs below 5° were achieved. The algorithm was then applied to accelerometer signals from implanted sensors installed in three NNP recipients. Error analysis was performed by comparing the correlation coefficients and RMSEs derived from trunk tilt estimates calculated from implanted sensor signals to those calculated via motion capture data, which served as the gold standard. NNP-derived trunk tilt estimates exhibited correlation coefficients between 0.80 and 0.95 and RMSEs below 13° for both pitch and roll in most cases. These findings suggest that the algorithm is effective at estimating trunk tilt with the implanted sensors of the NNP system, which implies that the method may be appropriate for extracting feedback signals for control systems for seated stability with NNP technology for individuals who have reduced control of their trunk due to paralysis. Full article

This article presents a novel hardware-assisted distributed ledger-based solution for simultaneous device and data security in smart healthcare. This article presents a novel architecture that integrates PUF, blockchain, and Tangle for Security-by-Design (SbD) of healthcare cyber–physical systems (H-CPSs). Healthcare systems around the world have undergone massive technological transformation and have seen growing adoption with the advancement of Internet-of-Medical Things (IoMT). The technological transformation of healthcare systems to telemedicine, e-health, connected health, and remote health is being made possible with the sophisticated integration of IoMT with machine learning, big data, artificial intelligence (AI), and other technologies. As healthcare systems are becoming more accessible and advanced, security and privacy have become pivotal for the smooth integration and functioning of various systems in H-CPSs. In this work, we present a novel approach that integrates PUF with IOTA Tangle and blockchain and works by storing the PUF keys of a patient’s Body Area Network (BAN) inside blockchain to access, store, and share globally. Each patient has a network of smart wearables and a gateway to obtain the physiological sensor data securely. To facilitate communication among various stakeholders in healthcare systems, IOTA Tangle’s Masked Authentication Messaging (MAM) communication protocol has been used, which securely enables patients to communicate, share, and store data on Tangle. The MAM channel works in the restricted mode in the proposed architecture, which can be accessed using the patient’s gateway PUF key. Furthermore, the successful verification of PUF enables patients to securely send and share physiological sensor data from various wearable and implantable medical devices embedded with PUF. Finally, healthcare system entities like physicians, hospital admin networks, and remote monitoring systems can securely establish communication with patients using MAM and retrieve the patient’s BAN PUF keys from the blockchain securely. Our experimental analysis shows that the proposed approach successfully integrates three security primitives, PUF, blockchain, and Tangle, providing decentralized access control and security in H-CPS with minimal energy requirements, data storage, and response time. Full article

Wireless Body Area Networks (WBANs) are an emerging industrial technology for monitoring physiological data. These networks employ medical wearable and implanted biomedical sensors aimed at improving quality of life by providing body-oriented services through a variety of industrial sensing gadgets. The sensors collect vital data from the body and forward this information to other nodes for further services using short-range wireless communication technology. In this paper, we provide a multi-aspect review of recent advancements made in this field pertaining to cross-domain security, privacy, and trust issues. The aim is to present an overall review of WBAN research and projects based on applications, devices, and communication architecture. We examine current issues and challenges with WBAN communications and technologies, with the aim of providing insights for a future vision of remote healthcare systems. We specifically address the potential and shortcomings of various Wireless Body Area Network (WBAN) architectures and communication schemes that are proposed to maintain security, privacy, and trust within digital healthcare systems. Although current solutions and schemes aim to provide some level of security, several serious challenges remain that need to be understood and addressed. Our aim is to suggest future research directions for establishing best practices in protecting healthcare data. This includes monitoring, access control, key management, and trust management. The distinguishing feature of this survey is the combination of our review with a critical perspective on the future of WBANs. Full article

Anthropogenetic environmental deterioration and climate change caused by energy production and consumption pose a significant threat to the future of humanity. Renewable, environmentally friendly, and cost-effective energy sources are becoming increasingly important for addressing future energy demands. Mechanical power is the most common type of external energy that can be converted into useful electric power. Because of its strong electromechanical coupling ability, the piezoelectric mechanism is a far more successful technique for converting mechanics energy to electrical energy when compared to electrostatic, electromagnetic, and triboelectric transduction systems. Currently, the scientific community has maintained a strong interest in piezoelectric micro-power generators because of their great potential for powering a sensor unit in the distributed network nodes. A national network usually has a large mass of sensor units distributed in each city, and a self-powered sensor network is eagerly required. This paper presents a comprehensive review of the development of piezoelectric micro-power generators. The fundamentals of piezoelectric energy conversion, including operational modes and working mechanisms, are introduced. Current research progress in piezoelectric materials including zinc oxide, ceramics, single crystals, organics, composite, bio-inspired and foam materials are reviewed. Piezoelectric energy harvesting at the nano- and microscales, and its applications in a variety of fields such as wind, liquid flow, body movement, implantable and sensing devices are discussed. Finally, the future development of multi-field coupled, hybrid piezoelectric micropower generators and their potential applications are discussed. Full article

The Internet of Things (IoT) combines various sensors and the internet to form an expanded network, realizing the interconnection between human beings and machines anytime and anywhere. Nevertheless, the problem of energy supply limits the large-scale implementation of the IoT. Fortunately, thermoelectric generators (TEGs), which can directly convert thermal gradients into electricity, have attracted extensive attention in the IoT field due to their unique benefits, such as small sizes, long maintenance cycles, high stability, and no noise. Therefore, it is vital to integrate the significantly advanced research on TEGs into IoT. In this review, we first outline the basic principle of the thermoelectricity effect and summarize the common preparation methods for thermoelectric functional parts in TEGs. Then, we elaborate on the application of TEG-powered sensors in the human body, including wearable and implantable medical electronic devices. This is followed by a discussion on the application of scene sensors for IoTs, for example, building energy management and airliners. Finally, we provide a further outlook on the current challenges and opportunities. Full article

In Wireless Body Area Networks (BAN), energy consumption, energy harvesting, and data communication are the three most important issues. In this paper, we develop an optimal allocation algorithm (OAA) for sensor devices, which are carried by or implanted in human body, harvest energy from their surroundings, and are powered by batteries. Based on the optimal allocation algorithm that uses a two-timescale Lyapunov optimization approach, we design a framework for joint optimization of network service cost and network utility to study energy, communication, and allocation management at the network edge. Then, we formulate the utility maximization problem of network service cost management based on the framework. Specifically, we use OAA, which does not require prior knowledge of energy harvesting to decompose the problem into three subproblems: battery management, data collection amount control and transmission energy consumption control. We solve these through OAA to achieve three main goals: (1) balancing the cost of energy consumption and the cost of data transmission on the premise of minimizing the service cost of the devices; (2) keeping the balance of energy consumption and energy collection under the condition of stable queue; and (3) maximizing network utility of the device. The simulation results show that the proposed algorithm can actually optimize the network performance. Full article

Wireless body area sensor networks (WBASNs) have received growing attention from industry and academia due to their exceptional potential for patient monitoring systems that are equipped with low-power wearable and implantable biomedical sensors under communications standards such as IEEE 802.15.4-2015 and IEEE 802.15.6-2012. The goal of WBASNs is to enhance the capabilities of wireless patient monitoring systems in terms of data accuracy, reliability, routing, channel access, and the data communication of sensors within, on and around the human body. The huge scope of challenges related to WBASNs has led to various research publications and industrial experiments. In this paper, a survey is conducted for the recent state-of-art in the context of medium access control (MAC) and routing protocols by considering the application requirements of patient monitoring systems. Moreover, we discuss the open issues, lessons learned, and challenges for these layers to provide a source of motivation for the upcoming design and development in the domain of WBASNs. This survey will be highly useful for the 6th generation (6G) networks; it is expected that 6G will provide efficient and ubiquitous connectivity to a huge number of IoT devices, and most of them will be sensor-based. This survey will further clarify the QoS requirement part of the 6G networks in terms of sensor-based IoT. Full article

Feedback control of functional neuromuscular stimulation has the potential to improve daily function for individuals with spinal cord injuries (SCIs) by enhancing seated stability. Our fully implanted networked neuroprosthesis (NNP) can provide real-time feedback signals for controlling the trunk through accelerometers embedded in modules distributed throughout the trunk. Typically, inertial sensors are aligned with the relevant body segment. However, NNP implanted modules are placed according to surgical constraints and their precise locations and orientations are generally unknown. We have developed a method for calibrating multiple randomly oriented accelerometers and fusing their signals into a measure of trunk orientation. Six accelerometers were externally attached in random orientations to the trunks of six individuals with SCI. Calibration with an optical motion capture system resulted in RMSE below 5° and correlation coefficients above 0.97. Calibration with a handheld goniometer resulted in RMSE of 7° and correlation coefficients above 0.93. Our method can obtain trunk orientation from a network of sensors without a priori knowledge of their relationships to the body anatomical axes. The results of this study will be invaluable in the design of feedback control systems for stabilizing the trunk of individuals with SCI in combination with the NNP implanted technology. Full article

Remotely monitoring people’s healthcare is still among the most important research topics for researchers from both industry and academia. In addition, with the Wireless Body Networks (WBANs) emergence, it becomes possible to supervise patients through an implanted set of body sensors that can communicate through wireless interfaces. These body sensors are characterized by their tiny sizes, and limited resources (power, computing, and communication capabilities), which makes these devices prone to have faults and sensible to be damaged. Thus, it is necessary to establish an efficient system to detect any fault or anomalies when receiving sensed data. In this paper, we propose a novel, optimized, and hybrid solution between machine learning and statistical techniques, for detecting faults in WBANs that do not affect the devices’ resources and functionality. Experimental results illustrate that our approach can detect unwanted measurement faults with a high detection accuracy ratio that exceeds the 99.62%, and a low mean absolute error of 0.61%, clearly outperforming the existing state-of-art solutions. Full article

The rapid and precise recognition of fish behavior is critical in perceiving health and welfare by allowing farmers to make informed management decisions on recirculating aquaculture systems while reducing labor. The conventional recognition methods are to obtain movement information by implanting sensors on the skin or in the body of the fish, which can affect the normal behavior and welfare of the fish. We present a novel nondestructive method with spatiotemporal and motion information based on deep learning for real-time recognition of fish schools’ behavior. In this work, a dual-stream 3D convolutional neural network (DSC3D) was proposed for the recognition of five behavior states of fish schools, including feeding, hypoxia, hypothermia, frightening and normal behavior. This DSC3D combines spatiotemporal features and motion features by using FlowNet2 and 3D convolutional neural networks and shows significant results suitable for industrial applications in automatic monitoring of fish behavior, with an average accuracy rate of 95.79%. The model evaluation results on the test dataset further demonstrated that our proposed method could be used as an effective tool for the intelligent perception of fish health status. Full article

Wireless body area networks (WBANs) have strong potential in the field of health monitoring. However, the energy consumption required for accurate monitoring determines the time between battery charges of the wearable sensors, which is a key performance factor (and can be critical in the case of implantable devices). In this paper, we study the inherent trade-off between the power consumption of the sensors and the probability of misclassifying a patient’s health state. We formulate this trade-off as a dynamic problem, in which at each step, we can choose to activate a subset of sensors that provide noisy measurements of the patient’s health state. We assume that the (unknown) health state follows a Markov chain, so our problem is formulated as a partially observable Markov decision problem (POMDP). We show that all the past measurements can be summarized as a belief state on the true health state of the patient, which allows tackling the POMDP problem as an MDP on the belief state. Then, we empirically study the performance of a greedy one-step look-ahead policy compared to the optimal policy obtained by solving the dynamic program. For that purpose, we use an open-source Continuous Glucose Monitoring (CGM) dataset of 232 patients over six months and extract the transition matrix and sensor accuracies from the data. We find that the greedy policy saves ≈50% of the energy costs while reducing the misclassification costs by less than 2% compared to the most accurate policy possible that always activates all sensors. Our sensitivity analysis reveals that the greedy policy remains nearly optimal across different cost parameters and a varying number of sensors. The results also have practical importance, because while the optimal policy is too complicated, a greedy one-step look-ahead policy can be easily implemented in WBAN systems. Full article

Wireless sensor network nodes and mobile devices are normally powered by batteries that, when depleted, must be recharged or replaced. This poses important problems, in particular for sensor nodes that are placed in inaccessible areas or biomedical sensors implanted in the human body where the battery replacement is very impractical. Moreover, the depleted battery must be properly disposed of in accordance with national and international regulations to prevent environmental pollution. A very interesting alternative to power mobile devices is energy harvesting where energy sources naturally present in the environment (such as sunlight, thermal gradients and vibrations) are scavenged to provide the power supply for sensor nodes and mobile systems. Since the presence of these energy sources is discontinuous in nature, electronic systems powered by energy harvesting must include a power management system and a storage device to store the scavenged energy. In this paper, the main strategies to design a wireless mobile sensor system powered by energy harvesting are reviewed and different sensor systems powered by such energy sources are presented. Full article

Wireless Body Area Networks (WBANs) are increasingly employed in different medical applications, such as remote health monitoring, early detection of medical conditions, and computer-assisted rehabilitation. A WBAN connects a number of sensor nodes implanted in and/or fixed on the human body for monitoring his/her physiological characteristics. Although medical healthcare systems could significantly benefit from the advancement of WBAN technology, collecting and transmitting private physiological data in such an open environment raises serious security and privacy concerns. In this paper, we propose a novel key-agreement protocol to secure communications among sensor nodes of WBANs. The proposed protocol is based on measuring and verifying common physiological features at both sender and recipient sensors prior to communicating. Unlike existing protocols, the proposed protocol enables communicating sensors to use their previous session pre-knowledge for secure communication within a specific period of time. This will reduce the time required for establishing the shared key as well as avoid retransmitting extracted features in the medium and hence thwarting eavesdropping attacks while maintaining randomness of the key. Experimental results illustrate the superiority of the proposed key agreement protocol in terms of both feature extraction and key agreement phases with an accuracy of 99.50% and an error rate of 0.005%. The efficacy of the proposed protocol with respect to energy and memory utilization is demonstrated compared with existing key agreement protocols. Full article