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Purpose: The power of the Internet has impacted various industries, especially the healthcare industry. With the rapid population growth and advancement of various sensor technologies and smart devices, the demand for quality healthcare services has increased in the past few years. At the same time, Internet of Things (IoT) technology makes intelligent objects communicate with each other by connecting all the objects without human intervention. IoT facilitated services and technologies in healthcare to provide low-cost and efficient service delivery to patients and consumers. It reduces the patient's hospital stay time and efficient information exchange by providing cost-effective healthcare services. This systematic review analyzes different technologies, core applications, datasets, algorithms, challenges, and limitations of IoT in the healthcare sector. Methods: Database search is performed to review IoT healthcare-enabling technologies, algorithms, applications, and IoT-centric architectures. Search has been conducted over popular databases like Springer, Elsevier, and IEEE Xplore. We have downloaded around 544 research papers, out of which we have selected only 116 based on our interests. Results: We identified different challenges in healthcare IoT applications as security and privacy, scalability, high cost, energy efficiency, quality of service, network architecture, mobility, and interoperability. However, data security and privacy issues are significant challenges in the healthcare sector as they deal with patients' confidential information. Also, we proposed a novel architecture that covers most IoT healthcare requirements. IoT healthcare applications are also detailed. In addition, it reviews most of the healthcare datasets and the utilized algorithms. Conclusions: Based on the reviewed articles in this paper, it is has been concluded that IoT devices are beneficial in the healthcare applications, regardless of their limitations in terms of energy, processing, and storage. It is a promising technology that can save time and effort to the practitioners and doctors. Several architectures and technologies are discussed as well as the limitations, which enables researchers to tackle those limitations. However, the future of IoT healthcare holds immense potential for high-security enabled and pervasive applications. Analyzing vast healthcare data using deep learning techniques can play a vital role in extracting valuable insights. To leverage the power of deep learning, it is crucial to design a well-structured cloud architecture capable of implementing these techniques and predicting future trends. While researchers have proposed various frameworks for IoT healthcare, there remains a need to assess the feasibility and effectiveness of these proposed frameworks. Furthermore, it is essential to address the specific requirements and intricacies when designing detailed architectural patterns for healthcare applications. Overall, the future of IoT healthcare is expansive and offers exciting research opportunities for scholars and experts in the field. By embracing innovative technologies and addressing the specific needs of the healthcare industry, we can unlock the full potential of IoT in revolutionizing healthcare delivery and improving patient outcomes. [ABSTRACT FROM AUTHOR]

To reduce frequent sensor recharging and replacement due to resource constraint, it becomes imperative to increase the management of energy and network's quality of service. To this end, this article provides a new wireless body sensor network architecture with two sink nodes and multiple energy management and quality-of-service algorithms. The first algorithm is the normal data avoidance algorithm that is responsible for decreasing the energy usage by avoiding the transmission of normal data. Duplicate data avoidance algorithm avoid transmitting duplicate data thus saving the bandwidth and battery life. Past knowledge-based weighted routing algorithm oversees taking the ideal direction to transmit information, hence improving quality of service. Furthermore, sleep scheduling is integrated to further improve the battery life. In addition, in our proposed model, linear programming which is based on mathematical models was used to model the network lifetime maximization and continuous data transmission minimization. Through simulation in Castalia-based OMNeT++ demonstrates that our proposed work outperforms the works of quasi-sleep-preempt-supported with regard to network lifetime and delay with 50% and 30% improvement, respectively, moreover, it improves the work of critical data with respect to packet drop and throughput with 30% and 75% improvement, respectively. [ABSTRACT FROM AUTHOR]

Evaluation of the motor ability of children with cerebral palsy (CP) based on International Classification of Functioning, Disability and Health for Children and Youth (ICF-CY) framework is an effective way to evaluate exercise rehabilitation therapy at present. Spastic diplegia (SD) is a common symptom in children with CP, which can cause movement disorders and physical disability of lower limbs. To rehabilitate these children, it is important to assess their exercise level in different treatment stages. In this study, an effective method of motion measurement based on inertial sensor network is proposed to evaluate the children’s motor ability in the purpose of the therapy effectiveness validation for the CP children. The extended Kalman filter (EKF) is used to fuse the sensor signals to measure the motion of body segments whose accuracy is verified by the OptiTrack optical system. A novel kinetic model based on a skeletal vector is proposed to reconstruct the posture of children in sports training to evaluate the effect of exercise rehabilitation. Statistical analysis is deployed to analyze the rehabilitation of motor function corresponding to SD children. The results are encouraging; some children’s motor functions of lower limbs have been improved postrehabilitation training. [ABSTRACT FROM AUTHOR]

Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption.

The improvement in the technology in health care monitoring system it is necessary to constantly monitor the patient's physiological parameter. In the advanced in the health care environment by the usage of IOT technology bring convenience to the patient and the physician. Since they are used in various medical areas. The internet of things (IOT) has been broadly used to interconnect the available medical resources and offers the smart, reliable and effective health care services. A sensor node has been attached on the surface of the body the sensor node will gather the information and sends them to the wireless sensor node. The sensor nodes which are arranged on the patient body can sense the heart beat, temperature of surrounding. The main objective of this system is to transmit the patient's health monitoring system through wireless communication in an emergence situation. We propose a secure IOT based healthcare monitoring system to check the heartbeat rate, temperature of the surrounding and saline level of the patient. In the proposed system of health care monitoring system the heart beat rate, temperature of surrounding and MEMS accelerometer is used for when a patient's body will fall down on the floor then it will send the SMS alert to the predefined mobile number and by using IOT we can send the data to the server. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to ECG signal filtering and classification. Unlike the traditional techniques which aim at collecting and processing the ECG signals with the patient being still, lying in bed in hospitals, our proposed algorithm is intentionally designed for monitoring and classifying the patient’s ECG signals in the free-living environment. The patients are equipped with wearable ambulatory devices the whole day, which facilitates the real-time heart attack detection. In ECG preprocessing, an integral-coefficient-band-stop (ICBS) filter is applied, which omits time-consuming floating-point computations. In addition, two-layered Hidden Markov Models (HMMs) are applied to achieve ECG feature extraction and classification. The periodic ECG waveforms are segmented into ISO intervals, P subwave, QRS complex and T subwave respectively in the first HMM layer where expert-annotation assisted Baum-Welch algorithm is utilized in HMM modeling. Then the corresponding interval features are selected and applied to categorize the ECG into normal type or abnormal type (PVC, APC) in the second HMM layer. For verifying the effectiveness of our algorithm on abnormal signal detection, we have developed an ECG body sensor network (BSN) platform, whereby real-time ECG signals are collected, transmitted, displayed and the corresponding classification outcomes are deduced and shown on the BSN screen.

Body sensor networks (BSN) are an important research topic due to various advantages over conventional measurement equipment. One main advantage is the feasibility to deploy a BSN system for 24/7 health monitoring applications. The requirements for such an application are miniaturization of the network nodes and the use of wireless data transmission technologies to ensure wearability and ease of use. Therefore, the reliability of such a system depends on the quality of the wireless data transmission. At present, most BSNs use ZigBee or other IEEE 802.15.4 based transmission technologies. Here, we evaluated the performance of a wireless transmission system of a novel BSN for biomedical applications in the 433MHz ISM band, called Integrated Posture and Activity NEtwork by Medit Aachen (IPANEMA) BSN. The 433MHz ISM band is used mostly by implanted sensors and thus allows easy integration of such into the BSN. Multiple measurement scenarios have been assessed, including varying antenna orientations, transmission distances and the number of network participants. The mean packet loss rate (PLR) was 0.63% for a single slave, which is comparable to IEEE 802.15.4 BSNs in the proximity of Bluetooth orWiFi networks. Secondly, an enhanced version is evaluated during on-body measurements with five slaves. The mean PLR results show a comparable good performance for measurements on a treadmill (2.5%), an outdoor track (3.4%) and in a climate chamber (1.5%).

Intra-Body Communication (IBC), which modulates ionic currents over the human body as the communication medium, offers a low power and reliable signal transmission method for information exchange across the body. This paper first briefly reviews the quasi-static electromagnetic (EM) field modeling for a galvanic-type IBC human limb operating below 1 MHz and obtains the corresponding transfer function with correction factor using minimum mean square error (MMSE) technique. Then, the IBC channel characteristics are studied through the comparison between theoretical calculations via this transfer function and experimental measurements in both frequency domain and time domain. High pass characteristics are obtained in the channel gain analysis versus different transmission distances. In addition, harmonic distortions are analyzed in both baseband and passband transmissions for square input waves. The experimental results are consistent with the calculation results from the transfer function with correction factor. Furthermore, we also explore both theoretical and simulation results for the bit-error-rate (BER) performance of several common modulation schemes in the IBC system with a carrier frequency of 500 kHz. It is found that the theoretical results are in good agreement with the simulation results.

Ultrasonic waves have good propagation in the human body and have been widely applied in biomedical device design without any reported side effects. Therefore, ultrasonic waves can provide an alternative method as an information carrier for body sensor networks (BSNs). This paper presents a novel wireless communication method that uses ultrasonic sound waves as a medium for healthcare systems. We investigated the feasibility of our proposal by testing it in a real digital communication experimental setup. To find an acceptable modulation method, the functionality of the proposed ultrasound-based digital communication approach was tested involving three principal modulation methods: amplitude shift keying (ASK), frequency shift keying (FSK), and phase shift keying (PSK). The modulated digital signals obtained from the experiments were compared with the simulated signals. Analysis of the results shows that ultrasonic waves are feasible and can be used for digital communication.

In the past few years, wireless body sensor networks (WBSNs) emerged as a low-cost solution for healthcare applications. In WBSNs, biosensors collect periodically physiological measurement and send them to the coordinator where the data fusion process takes place. However, processing the huge amount of data captured by the limited lifetime biosensors and taking the right decisions when there is an emergency are major challenges in WBSNs. In this paper, we introduce a biosensor data management framework, starting from data collection to decision making. First, we propose an adaptive data collection approach on the biosensor node level. This approach uses an early warning score system to optimize data transmission and estimates in real time the sensing frequency. Second, we present a data fusion model on the coordinator level using a decision matrix and fuzzy set theory. To evaluate our approach, we conducted multiple series of simulations on real sensor data. The results show that our approach reduces the amount of collected data, while maintaining data integrity. In addition, we show the impact of sampling and filtering data on the accuracy of the taken decisions and compare our data fusion approach with a basic decision tree algorithm. [ABSTRACT FROM PUBLISHER]

With the pervasiveness of smart phones and the advance of wireless body sensor network (BSN), mobile Healthcare (m-Healthcare), which extends the operation of Healthcare provider into a pervasive environment for better health monitoring, has attracted considerable interest recently. However, the flourish of m-Healthcare still faces many challenges including information security and privacy preservation. In this paper, we propose a secure and privacy-preserving framework combining with multilevel trust management. In our scheme, smart phone resources including computing power and energy can be opportunistically gathered to process the computing-intensive PHI (personal health information) during m-Healthcare emergency with minimal privacy disclosure. In specific, to leverage the PHI privacy disclosure and the high reliability of PHI process and transmission in m-Healthcare emergency, we introduce an efficient lightweight encryption for those users whose trust level is low, which is based on mix cipher algorithms and pair of plain text and cipher texts, and allow a medical user to decide who can participate in the opportunistic computing to assist in processing his overwhelming PHI data. Detailed security analysis and simulations show that the proposed framework can efficiently achieve user-centric privacy protection in m-Healthcare system. [ABSTRACT FROM AUTHOR]

Recently, we have proposed a body-sensor-network-based approach, composed of a few body-worn wireless inertial nodes, for automatic assignment of Unified Parkinson's Disease Rating Scale (UPDRS) scores in the following tasks: Leg agility (LA), Sit-to-Stand (S2S), and Gait (G). Unlike our previous works and the majority of the published studies, where UPDRS tasks were the sole focus, in this paper, we carry out a comparative investigation of the LA, S2S, and G tasks. In particular, after providing an accurate description of the features identified for the kinematic characterization of the three tasks, we comment on the correlation between the most relevant kinematic parameters and the UPDRS scoring. We analyzed the performance achieved by the automatic UPDRS scoring system and compared the estimated UPDRS evaluation with the one performed by neurologists, showing that the proposed system compares favorably with typical interrater variability. We then investigated the correlations between the UPDRS scores assigned to the various tasks by both the neurologists and the automatic system. The results, based on a limited number of subjects with Parkinson's disease (PD) (34 patients, 47 clinical trials), show poor-to-moderate correlations between the UPDRS scores of different tasks, highlighting that the patients’ motor performance may vary significantly from one task to another, since different tasks relate to different aspects of the disease. An aggregate UPDRS score is also considered as a concise parameter, which can provide additional information on the overall level of the motor impairments of a Parkinson's patient. Finally, we discuss a possible implementation of a practical e-health application for the remote monitoring of PD patients. [ABSTRACT FROM PUBLISHER]

Body Sensor Network (BSN) is a network of several associated sensor nodes on, inside or around the human body to monitor vital signals, such as, Electroencephalogram (EEG), Photoplethysmography (PPG), Electrocardiogram (ECG), etc. Each sensor node in BSN delivers major information; therefore, it is very significant to provide data confidentiality and security. All existing approaches to secure BSN are based on complex cryptographic key generation procedures, which not only demands high resource utilization and computation time, but also consumes large amount of energy, power and memory during data transmission. However, it is indispensable to put forward energy efficient and computationally less complex authentication technique for BSN. In this paper, a novel biometric-based algorithm is proposed, which utilizes Heart Rate Variability (HRV) for simple key generation process to secure BSN. Our proposed algorithm is compared with three data authentication techniques, namely Physiological Signal based Key Agreement (PSKA), Data Encryption Standard (DES) and Rivest Shamir Adleman (RSA). Simulation is performed in Matlab and results suggest that proposed algorithm is quite efficient in terms of transmission time utilization, average remaining energy and total power consumption. [ABSTRACT FROM AUTHOR]

A low-power biosignal acquisition and classification system for body sensor networks is proposed. The proposed system consists of three main parts: 1) a high-pass sigma delta modulator-based biosignal processor (BSP) for signal acquisition and digitization, 2) a low-power, super-regenerative on–off keying transceiver for short-range wireless transmission, and 3) a digital signal processor (DSP) for electrocardiogram (ECG) classification. The BSP and transmitter circuits, which are the body-end circuits, can be operated for over 80 days using two 605 mAH zinc–air batteries as the power supply; the power consumption is 586.5 μW. As for the radio frequency receiver and DSP, which are the receiving-end circuits that can be integrated in smartphones or personal computers, power consumption is less than 1 mW. With a wavelet transform-based digital signal processing circuit and a diagnosis control by cardiologists, the accuracy of beat detection and ECG classification are close to 99.44% and 97.25%, respectively. All chips are fabricated in TSMC 0.18-μm standard CMOS process. [ABSTRACT FROM PUBLISHER]

Body sensor networks (BSN) are an important research topic due to various advantages over conventional measurement equipment. One main advantage is the feasibility to deploy a BSN system for 24/7 health monitoring applications. The requirements for such an application are miniaturization of the network nodes and the use of wireless data transmission technologies to ensure wearability and ease of use. Therefore, the reliability of such a system depends on the quality of the wireless data transmission. At present, most BSNs use ZigBee or other IEEE 802.15.4 based transmission technologies. Here, we evaluated the performance of a wireless transmission system of a novel BSN for biomedical applications in the 433MHz ISM band, called Integrated Posture and Activity NEtwork by Medit Aachen (IPANEMA) BSN. The 433MHz ISM band is used mostly by implanted sensors and thus allows easy integration of such into the BSN. Multiple measurement scenarios have been assessed, including varying antenna orientations, transmission distances and the number of network participants. The mean packet loss rate (PLR) was 0.63% for a single slave, which is comparable to IEEE 802.15.4 BSNs in the proximity of Bluetooth or WiFi networks. Secondly, an enhanced version is evaluated during on-body measurements with five slaves. The mean PLR results show a comparable good performance for measurements on a treadmill (2.5%), an outdoor track (3.4%) and in a climate chamber (1.5%). [ABSTRACT FROM AUTHOR]

Intra-Body Communication (IBC), which modulates ionic currents over the human body as the communication medium, offers a low power and reliable signal transmission method for information exchange across the body. This paper first briefly reviews the quasi-static electromagnetic (EM) field modeling for a galvanic-type IBC human limb operating below 1 MHz and obtains the corresponding transfer function with correction factor using minimum mean square error (MMSE) technique. Then, the IBC channel characteristics are studied through the comparison between theoretical calculations via this transfer function and experimental measurements in both frequency domain and time domain. High pass characteristics are obtained in the channel gain analysis versus different transmission distances. In addition, harmonic distortions are analyzed in both baseband and passband transmissions for square input waves. The experimental results are consistent with the calculation results from the transfer function with correction factor. Furthermore, we also explore both theoretical and simulation results for the bit-error-rate (BER) performance of several common modulation schemes in the IBC system with a carrier frequency of 500 kHz. It is found that the theoretical results are in good agreement with the simulation results. [ABSTRACT FROM AUTHOR]

An energy-efficient crystal-less double-FSK transceiver for wireless body-area-network (WBAN) sensor nodes is implemented in 0.18-\mum CMOS technology with a 1-V supply. The injection-locking digitally controlled oscillator (IL-DCO) replaces the crystal oscillator (XO), which leads to significantly reduce the energy consumption and system cost. With the proposed calibration method using an injection-locking detector (IL-detector), the frequency drift of DCO can be calibrated within 100-kHz accuracy over 100^\circ temperature variation. For the full satisfaction to the WBAN requirements, such as wide range of quality of service in terms of data rate, bit error rate, and network coexistence, we adopt a scalable double-FSK modulation scheme with divider-based transmitter by a power-efficient switching modulator. As a result, the fabricated crystal-less double-FSK WBAN compatible sensor node transceiver consumes 1 and 2 mW in calibrating and transmitting modes, respectively, at a data rate of up to 10 Mb/s, providing an 80 MHz reference source with 100-kHz accuracy by auto-calibrated DCO. [ABSTRACT FROM PUBLISHER]

Emerging applications in the medical field require body sensor networks to communicate in real-time in a very energy-efficient way. An example is the Artificial Accommodation System-a set of two small active medical implants aiming at restoring accommodation of the human eye-in which sensor data have to be exchanged continuously between both eyes. To achieve energy efficiency, it is essential to operate the radios with a very low duty cycle. Therefore, idle listening and general protocol overhead must be reduced as much as possible. In this paper, we present a relative time synchronization medium access control protocol (RTS-MAC), which keeps relative time synchronization between two or more sensor nodes in a very energy-efficient manner: RTS-MAC makes use of the periodic broadcast of regular data messages and exploits the inter-arrival times to predict future arrivals within tight boundaries. Thereby, no overhead is generated for synchronization purposes, and still, idle listening is reduced to a minimum, which solely depends on the short-term accuracy of the underlying clock systems. We implemented the proposed protocol using off-the-shelf components and employed the internal low-accuracy oscillators of the microcontrollers as clock sources. Thereby, we achieved very low duty cycles close to an ideal minimum. Further, our results indicate that through omitting external crystals in favor of a minimally larger battery, the battery life of a sensor node can be prolonged. [ABSTRACT FROM AUTHOR]

In this paper, a body sensor network (BSN) based context-aware QRS detection scheme is proposed. The algorithm uses the context information provided by the body sensor network to improve the QRS detection performance by dynamically selecting those leads with the best SNR and taking advantage of the best features of two complementary detection algorithms. The accelerometer data from the BSN are used to classify the daily activities of patients and provide context information. The classification results indicate the types of activities that were engaged in. They also indicate their corresponding intensity, which is related to the signal-to-noise ratio (SNR) of the ECG recordings. Activity intensity is first fed to the lead selector to eliminate those leads with low SNR, and then is fed to a selector to select a proper QRS detector according to the noise level. An MIT-BIH noise stress test database is used to evaluate the algorithms. [ABSTRACT FROM AUTHOR]

In recent years, the increasing number of wearable devices on human has been witnessed as a trend. These devices can serve for many purposes: personal entertainment, communication, emergency mission, health care supervision, delivery, etc. Sharing information among the devices scattered across the human body requires a body area network (BAN) and body sensor network (BSN). However, implementation of the BAN/BSN with the conventional wireless technologies cannot give optimal result. It is mainly because the high requirements of light weight, miniature, energy efficiency, security, and less electromagnetic interference greatly limit the resources available for the communication modules. The newly developed intra-body communication (IBC) can alleviate most of the mentioned problems. This technique, which employs the human body as a communication channel, could be an innovative networking method for sensors and devices on the human body. In order to encourage the research and development of the IBC, the authors are favorable to lay a better and more formal theoretical foundation on IBC. They propose a multilayer mathematical model using volume conductor theory for galvanic coupling IBC on a human limb with consideration on the inhomogeneous properties of human tissue. By introducing and checking with quasi-static approximation criteria, Maxwell’s equations are decoupled and capacitance effect is included to the governing equation for further improvement. Finally, the accuracy and potential of the model are examined from both in vitro and in vivo experimental results. [ABSTRACT FROM AUTHOR]

We investigate the use of relaying of creeping waves in the industrial scientific medical frequency bands of 434 MHz, 915 MHz, and 2.4 GHz. The investigation includes generic analysis and experimental setups. For generic analysis, a link budget model is derived based solely on the creeping wave component of the transmitted signal while marginalizing for other effects, such as reflections from the surrounding environment. Closed-form expressions of the gains in network lifetime and energy per bit are derived for a system covering the entire body using relays compared to a reference system offering the same level of reliability without relaying. The experimental setups are used to gather measurements in the 2.4-GHz band with a body sensor network development platform in a nonreflective open-space environment and in a reflective residential environment. The measurements are used to validate the link budget model and evaluate performance of practical systems. Analysis and experimentation demonstrate that relaying of creeping waves offers considerable performance gains in all frequency bands. For example, using a single relay on either side of the body in 2.4 GHz can potentially increase network-lifetime times 40 and decrease energy per bit by 48 dB. Part of this potential is achieved in experimental setups where relaying was shown to increase network lifetime times 13, decrease energy per bit by 13 dB and provide the ability to compensate for a wider fading margin. [ABSTRACT FROM PUBLISHER]

Through an integration of wireless communication and sensing technologies, the concept of a body sensor network (BSN) was initially proposed in the early decade with the aim to provide an essential technology for wearable, ambulatory, and pervasive health monitoring for elderly people and chronic patients. It has become a hot research area due to big opportunities as well as great challenges it presents. Though the idea of an implantable BSN was proposed in parallel with the on-body sensor network, the development in this area is relatively slow due to the complexity of human body, safety concerns, and some technological bottlenecks such as the design of ultralow-power implantable RF transceiver. This paper describes a new wireless implantable BSN that operates in medical implant communication service (MICS) frequency band. This system innovatively incorporates both sensing and actuation nodes to form a closed-control loop for physiological monitoring and drug delivery for critically ill patients. The sensing node, which is designed using system-on-chip technologies, takes advantage of the newly available ultralow-power Zarlink MICS transceiver for wireless data transmission. Finally, the specific absorption rate distribution of the proposed system was simulated to determine the in vivo electromagnetic field absorption and the power safety limits. [ABSTRACT FROM PUBLISHER]

This letter introduces MercuryLive, a platform to enable home monitoring of patients with Parkinson's disease (PD) using wearable sensors. MercuryLive contains three tiers: a resource-aware data collection engine that relies upon wearable sensors, web services for live streaming and storage of sensor data, and a web-based graphical user interface client with video conferencing capability. Besides, the platform has the capability of analyzing sensor (i.e., accelerometer) data to reliably estimate clinical scores capturing the severity of tremor, bradykinesia, and dyskinesia. Testing results showed an average data latency of less than 400 ms and video latency of about 200 ms with video frame rate of about 13 frames/s when 800 kb/s of bandwidth were available and we used a 40% video compression, and data feature upload requiring 1 min of extra time following a 10 min interactive session. These results indicate that the proposed platform is suitable to monitor patients with PD to facilitate the titration of medications in the late stages of the disease. [ABSTRACT FROM PUBLISHER]

The coordination between locomotion and respiration of Parkinson's disease (PD) patients is reduced or even absent. The degree of this disturbance is assumed to be associated with the disease severity [S. Schiermeier, D. Schäfer, T. Schäfer, W. Greulich, and M. E. Schläfke, “Breathing and locomotion in patients with Parkinson's disease,” Eur. J. Physiol., vol. 443, No. 1, pp. 67–71, Jul. 2001]. To enable a long-term and online analysis of the locomotion–respiration coordination for scientific purpose, we have developed a distributed wireless communicating network. We aim to integrate biofeedback protocols with the real-time analysis of the locomotion–respiration coordination in the system to aid rehabilitation of PD patients. The network of sensor nodes is composed of intelligent network operating devices (iNODEs). The miniaturized iNODE contains a continuous data acquisition system based on microcontroller, local data storage, capability of on-sensor digital signal processing in real time, and wireless communication based on IEEE 802.15.4. Force sensing resistors and respiratory inductive plethysmography are applied for motion and respiration sensing, respectively. A number of experiments have been undertaken in clinic and laboratory to test the system. It shall facilitate identification of therapeutic effects on PD, allowing to measure the patients’ health status, and to aid in the rehabilitation of PD patients. [ABSTRACT FROM AUTHOR]

Security of the emerging body sensor network (BSN) in telemedicine applications is a crucial problem because personal medical information must be protected against flaws and misdeeds. The solution is, however, nontrivial because lightweight mechanisms have to be deployed to meet the stringent resource constraints of these networks. It has been suggested that the inherent ability of human body to transfer information is a unique and resource-saving method to secure wireless communications within a BSN. For example, physiological characteristics can be captured by individual sensors of a BSN to generate entity identifiers (EIs) for identifying nodes and even securing keying materials, i.e., by a biometric approach. This study demonstrates the performance analysis of such a biometric trait, i.e., the interpulse intervals (IPIs) of heartbeats that were calculated from electrocardiogram and photoplethysmogram of 99 subjects. Based on the characteristics of IPIs, a lightweight generation scheme of EIs is proposed. Individual randomness and group similarity of the generated EIs are then evaluated. False acceptance rate and false rejection rate are also calculated to measure the effectiveness of the proposed identification system. The results suggest that the readily available IPI information can be a good source for generating EIs among BSN nodes. [ABSTRACT FROM AUTHOR]

Ultrasonic waves have good propagation in the human body and have been widely applied in biomedical device design without any reported side effects. Therefore, ultrasonic waves can provide an alternative method as an information carrier for body sensor networks (BSNs). This paper presents a novel wireless communication method that uses ultrasonic sound waves as a medium for healthcare systems. We investigated the feasibility of our proposal by testing it in a real digital communication experimental setup. To find an acceptable modulation method, the functionality of the proposed ultrasound-based digital communication approach was tested involving three principal modulation methods: amplitude shift keying (ASK), frequency shift keying (FSK), and phase shift keying (PSK). The modulated digital signals obtained from the experiments were compared with the simulated signals. Analysis of the results shows that ultrasonic waves are feasible and can be used for digital communication. [ABSTRACT FROM AUTHOR]