HACMAC: A reliable human activity-based medium access control for implantable body sensor networks

Chronic care is an eminent application of implantable body sensor networks (IBSN). Performing physical activities such as walking, running, and sitting is unavoidable during the long-term monitoring of chronic-care patients. These physical activities cripple the radio frequency (RF) signal between the implanted sensor nodes. This is because various body postures shadow the RF signal. Although shadowing itself may be short, a prolonged activity will significantly increase the effect of the RF-shadowing. This effect dampens the communication between implantable sensor nodes and hence increases the chance of missing life-critical data. To overcome this problem, in this paper we propose a link quality-aware medium access control (MAC) protocol called HACMAC, which adapts the access mechanism during different human activities based on the wireless link-quality. Our simulation results show that compared with the access mechanism suggested by the IEEE 802.15.6 standard, the reliability of the wireless communication is increased using HACMAC even while transmitting at a strongly low transmission power of 25µW effective isotropic radiated power (EIRP) set by the IEEE 802.15.6 standar

Design and Development of Smart Brain-Machine-Brain Interface (SBMIBI) for Deep Brain Stimulation and Other Biomedical Applications

Machine collaboration with the biological body/brain by sending electrical information back and forth is one of the leading research areas in neuro-engineering during the twenty-first century. Hence, Brain-Machine-Brain Interface (BMBI) is a powerful tool for achieving such machine-brain/body collaboration. BMBI generally is a smart device (usually invasive) that can record, store, and analyze neural activities, and generate corresponding responses in the form of electrical pulses to stimulate specific brain regions. The Smart Brain-Machine-Brain-Interface (SBMBI) is a step forward with compared to the traditional BMBI by including smart functions, such as in-electrode local computing capabilities, and availability of cloud connectivity in the system to take the advantage of powerful cloud computation in decision making. In this dissertation work, we designed and developed an innovative form of Smart Brain-Machine-Brain Interface (SBMBI) and studied its feasibility in different biomedical applications. With respect to power management, the SBMBI is a semi-passive platform. The communication module is fully passive—powered by RF harvested energy; whereas, the signal processing core is battery-assisted. The efficiency of the implemented RF energy harvester was measured to be 0.005%. One of potential applications of SBMBI is to configure a Smart Deep-Brain-Stimulator (SDBS) based on the general SBMBI platform. The SDBS consists of brain-implantable smart electrodes and a wireless-connected external controller. The SDBS electrodes operate as completely autonomous electronic implants that are capable of sensing and recording neural activities in real time, performing local processing, and generating arbitrary waveforms for neuro-stimulation. A bidirectional, secure, fully-passive wireless communication backbone was designed and integrated into this smart electrode to maintain contact between the smart electrodes and the controller. The standard EPC-Global protocol has been modified and adopted as the communication protocol in this design. The proposed SDBS, by using a SBMBI platform, was demonstrated and tested through a hardware prototype. Additionally the SBMBI was employed to develop a low-power wireless ECG data acquisition device. This device captures cardiac pulses through a non-invasive magnetic resonance electrode, processes the signal and sends it to the backend computer through the SBMBI interface. Analysis was performed to verify the integrity of received ECG data

Design of Wireless Power Transfer and Data Telemetry System for Biomedical Applications

With the advancement of biomedical instrumentation technologies sensor based remote healthcare monitoring system is gaining more attention day by day. In this system wearable and implantable sensors are placed outside or inside of the human body. Certain sensors are needed to be placed inside the human body to acquire the information on the vital physiological phenomena such as glucose, lactate, pH, oxygen, etc. These implantable sensors have associated circuits for sensor signal processing and data transmission. Powering the circuit is always a crucial design issue. Batteries cannot be used in implantable sensors which can come in contact with the blood resulting in serious health risks. An alternate approach is to supply power wirelessly for tether-less and battery- less operation of the circuits.Inductive power transfer is the most common method of wireless power transfer to the implantable sensors. For good inductive coupling, the inductors should have high inductance and high quality factor. But the physical dimensions of the implanted inductors cannot be large due to a number of biomedical constraints. Therefore, there is a need for small sized and high inductance, high quality factor inductors for implantable sensor applications. In this work, design of a multi-spiral solenoidal printed circuit board (PCB) inductor for biomedical application is presented. The targeted frequency for power transfer is 13.56 MHz which is within the license-free industrial, scientific and medical (ISM) band. A figure of merit based optimization technique has been utilized to optimize the PCB inductors. Similar principal is applied to design on-chip inductor which could be a potential solution for further miniaturization of the implantable system. For layered human tissue the optimum frequency of power transfer is 1 GHz for smaller coil size. For this reason, design and optimization of multi-spiral solenoidal integrated inductors for 1 GHz frequency is proposed. Finally, it is demonstrated that the proposed inductors exhibit a better overall performance in comparison with the conventional inductors for biomedical applications

Design and Implementation of A Three-Level Boost converter for Battery Impedance Spectroscopy

Lithium-ion batteries are the most are widely used as electrical storage device in various applications such as portable electronics, electric vehicles, Photovoltaic application, telecommunication etc due to the characteristics of the batterie such as high-power density, long cycling and high-power efficiency. Extensive condition monitoring of the battery should be implemented due to the usage of the battery so that there will be an increase in all the overall performance and expectancy. This research is focused on implementing an online condition monitoring on the Li-ion battery using a signal injection through a power converter. The implemented technique in this research is known as the Electrochemical Impedance Spectroscopy (EIS). The EIS is a widely known technique used in determining the internal impedance of a battery cell. The estimated impedance can be used to determine the state of charge (Soc) and State of health (SoH) of a battery. The EIS is used to characterize the electrochemical behaviour thereby monitoring the change in the impedance of the cell of the battery. The EIS technique is accomplished by sinusoidally injecting current at different frequencies and measuring the voltage response. A standard Frequency Response Analyser (FRA) is used as an offline test while the battery is disconnected from the Load. The limitation of this standard FRA analyser is that it is bulky and Expensive. Attempts have been made to migrate the techniques to online operations, each having their own challenges. For an online Implementation, the interfacing power converter is used for Signal injection to measure the impedance of the battery. This work explores the low current ripple advantage of a threelevel boost converter to implement EIS on lithium ion battery

Wireless Technologies for Industry 4.0 Applications

Wireless technologies are increasingly used in industrial applications. These technologies reduce cabling, which is costly and troublesome, and introduce several benefits for their application in terms of flexibility to modify the layout of the nodes and scaling of the number of connected devices. They may also introduce new functionalities since they ease the connections to mobile devices or parts. Although they have some drawbacks, they are increasingly accepted in industrial applications, especially for monitoring and supervision tasks. Recently, they are starting to be accepted even for time-critical tasks, for example, in closed-loop control systems involving slow dynamic processes. However, wireless technologies have been evolving very quickly during the last few years, since several relevant technologies are available in the market. For this reason, it may become difficult to select the best alternative. This perspective article intends to guide application designers to choose the most appropriate technology in each case. For this purpose, this article discusses the most relevant wireless technologies in the industry and shows different examples of applications

Multi-Modal Wireless Flexible Gel-Free Sensors with Edge Deep Learning for Detecting and Alerting Freezing of Gait in Parkinson's Patients

Freezing of gait (FoG) is a debilitating symptom of Parkinson's disease (PD). This work develops flexible wearable sensors that can detect FoG and alert patients and companions to help prevent falls. FoG is detected on the sensors using a deep learning (DL) model with multi-modal sensory inputs collected from distributed wireless sensors. Two types of wireless sensors are developed, including: (1) a C-shape central node placed around the patient's ears, which collects electroencephalogram (EEG), detects FoG using an on-device DL model, and generates auditory alerts when FoG is detected; (2) a stretchable patch-type sensor attached to the patient's legs, which collects electromyography (EMG) and movement information from accelerometers. The patch-type sensors wirelessly send collected data to the central node through low-power ultra-wideband (UWB) transceivers. All sensors are fabricated on flexible printed circuit boards. Adhesive gel-free acetylene carbon black and polydimethylsiloxane electrodes are fabricated on the flexible substrate to allow conformal wear over the long term. Custom integrated circuits (IC) are developed in 180 nm CMOS technology and used in both types of sensors for signal acquisition, digitization, and wireless communication. A novel lightweight DL model is trained using multi-modal sensory data. The inference of the DL model is performed on a low-power microcontroller in the central node. The DL model achieves a high detection sensitivity of 0.81 and a specificity of 0.88. The developed wearable sensors are ready for clinical experiments and hold great promise in improving the quality of life of patients with PD. The proposed design methodologies can be used in wearable medical devices for the monitoring and treatment of a wide range of neurodegenerative diseases