Adaptive Body Area Networks Using Kinematics and Biosignals

The increasing penetration of wearable and implantable devices necessitates energy-efficient and robust ways of connecting them to each other and to the cloud. However, the wireless channel around the human body poses unique challenges such as a high and variable path-loss caused by frequent changes in the relative node positions as well as the surrounding environment. An adaptive wireless body area network (WBAN) scheme is presented that reconfigures the network by learning from body kinematics and biosignals. It has very low overhead since these signals are already captured by the WBAN sensor nodes to support their basic functionality. Periodic channel fluctuations in activities like walking can be exploited by reusing accelerometer data and scheduling packet transmissions at optimal times. Network states can be predicted based on changes in observed biosignals to reconfigure the network parameters in real time. A realistic body channel emulator that evaluates the path-loss for everyday human activities was developed to assess the efficacy of the proposed techniques. Simulation results show up to 41% improvement in packet delivery ratio (PDR) and up to 27% reduction in power consumption by intelligent scheduling at lower transmission power levels. Moreover, experimental results on a custom test-bed demonstrate an average PDR increase of 20% and 18% when using our adaptive EMG- and heart-rate-based transmission power control methods, respectively. The channel emulator and simulation code is made publicly available at https://github.com/a-moin/wban-pathloss.Comment: Accepted for publication in IEEE Journal of Biomedical and Health Informatic

How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

Increasing the robustness of active upper limb prostheses

This thesis is based on my work done at the Institute for Neurorehabilitation Systems at the University Medical Center Goettingen. My work has been partially founded by German Ministry for Education and Research (BMBF) via the Bernstein Focus Neurotechnology (BFNT) Göttingen under grant number 1GQ0810 The local ethics committee approved all studies involving human subjects, and all subjects signed informed consents prior to their participation in the studies. The entire thesis has been originally written by me. Part of the materials used in this thesis have also been published in journals or conferences, where I am the first or corresponding author. All rights for re-use of previously published material were obtained. Reused figures and tables of IEEE publications are marked with © [Year] IEEE. Hereby I declare that I have written this thesis independently and with no other aids and sources than quoted

Low-Power Circuits for Brain–Machine Interfaces

This paper presents work on ultra-low-power circuits for brain–machine interfaces with applications for paralysis prosthetics, stroke, Parkinson’s disease, epilepsy, prosthetics for the blind, and experimental neuroscience systems. The circuits include a micropower neural amplifier with adaptive power biasing for use in multi-electrode arrays; an analog linear decoding and learning architecture for data compression; low-power radio-frequency (RF) impedance-modulation circuits for data telemetry that minimize power consumption of implanted systems in the body; a wireless link for efficient power transfer; mixed-signal system integration for efficiency, robustness, and programmability; and circuits for wireless stimulation of neurons with power-conserving sleep modes and awake modes. Experimental results from chips that have stimulated and recorded from neurons in the zebra finch brain and results from RF power-link, RF data-link, electrode- recording and electrode-stimulating systems are presented. Simulations of analog learning circuits that have successfully decoded prerecorded neural signals from a monkey brain are also presented

Fault tolerance in WBAN applications

One of the most promising applications of IoT is Wireless Body Area Net-works (WBANs) in medical applications. They allow physiological signals monitoring of patients without the presence of nearby medical personnel. Furthermore, WBANs enable feedback action to be taken either periodically or event-based following the Networked Control Systems (NCSs) techniques. This thesis first presents the architecture of a fault tolerant WBAN. Sensors data are sent over two redundant paths to be processed, analyzed and monitored. The two main communication protocols utilized in this system are Low power Wi-Fi (IEEE 802.11n) and Long Term Evolution (LTE). Riverbed Modeler is used to study the system’s behavior. Simulation results are collected with 95% confidence analysis on 33 runs on different initial seeds. It is proven that the system is fully operational. It is then shown that the system can withstand interference and system’s performance is quantified. Results indicate that the system succeeds in meeting all required control criteria in the presence of two different interference models. The second contribution of this thesis is the design of an FPGA-based smart band for health monitoring applications in WBANs. This FPGA-based smart band has a softcore processor and its allocated SRAM block as well as auxiliary modules. A novel scheme for full initial configuration and Dynamic Partial Reconfiguration through the WLAN network is integrated into this design. Fault tolerance techniques are used to mitigate transient faults such as Single Event Upsets (SEUs) and Multiple Event Upsets (MEUs). The system is studied in a normal environment as well as in a harsh environment. System availability is then obtained using Markov Models and a case study is presented

Biosignal‐based human–machine interfaces for assistance and rehabilitation : a survey

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal‐based HMIs for assistance and rehabilitation to outline state‐of‐the‐art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full‐text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever‐growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complex-ity, so their usefulness should be carefully evaluated for the specific application