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

Fabrication and characterization of flexible spray-coated antennas

This paper investigates the potential of using spray coating as a methodology for flexible antenna fabrication. The methodology has advantages compared with other antenna-printing techniques, such as screen-printing and gravure printing (more flexibility in design), or inkjet printing (faster production). The methodology is demonstrated using two different types of folded dipole antennas that are designed to operate in the ultra-high frequency radio-frequency identification (UHF RFID) band. Both antennas show good agreement between simulation and measurement of the spray-coated samples in terms of power reflection coefficient and gain. The two folded dipoles, with and without ground plane, show comparable performance in terms of gain, as similar antennas found in literature. The folded dipole on a ground plane is more stable near conductive surfaces and on the human body. Given these results, we conclude that spray coating is a good technique for printing small to medium sized batches of antennas

Electrobioremediation of oil spills

Annually, thousands of oil spills occur across the globe. As a result, petroleum substances and petrochemical compounds are widespread contaminants causing concern due to their toxicity and recalcitrance. Many remediation strategies have been developed using both physicochemical and biological approaches. Biological strategies are most benign, aiming to enhance microbial metabolic activities by supplying limiting inorganic nutrients, electron acceptors or donors, thus stimulating oxidation or reduction of contaminants. A key issue is controlling the supply of electron donors/acceptors. Bioelectrochemical systems (BES) have emerged, in which an electrical current serves as either electron donor or acceptor for oil spill bioremediation. BES are highly controllable and can possibly also serve as biosensors for real time monitoring of the degradation process. Despite being promising, multiple aspects need to be considered to make BES suitable for field applications including system design, electrode materials, operational parameters, mode of action and radius of influence. The microbiological processes, involved in bioelectrochemical contaminant degradation, are currently not fully understood, particularly in relation to electron transfer mechanisms. Especially in sulfate rich environments, the sulfur cycle appears pivotal during hydrocarbon oxidation. This review provides a comprehensive analysis of the research on bioelectrochemical remediation of oil spills and of the key parameters involved in the process

An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier

EMG-based gesture recognition shows promise for human-machine interaction. Systems are often afflicted by signal and electrode variability which degrades performance over time. We present an end-to-end system combating this variability using a large-area, high-density sensor array and a robust classification algorithm. EMG electrodes are fabricated on a flexible substrate and interfaced to a custom wireless device for 64-channel signal acquisition and streaming. We use brain-inspired high-dimensional (HD) computing for processing EMG features in one-shot learning. The HD algorithm is tolerant to noise and electrode misplacement and can quickly learn from few gestures without gradient descent or back-propagation. We achieve an average classification accuracy of 96.64% for five gestures, with only 7% degradation when training and testing across different days. Our system maintains this accuracy when trained with only three trials of gestures; it also demonstrates comparable accuracy with the state-of-the-art when trained with one trial

Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular automata

In this paper, a hardware-optimized approach to emotion recognition based on the efficient brain-inspired hyperdimensional computing (HDC) paradigm is proposed. Emotion recognition provides valuable information for human-computer interactions, however the large number of input channels (>200) and modalities (>3) involved in emotion recognition are significantly expensive from a memory perspective. To address this, methods for memory reduction and optimization are proposed, including a novel approach that takes advantage of the combinatorial nature of the encoding process, and an elementary cellular automaton. HDC with early sensor fusion is implemented alongside the proposed techniques achieving two-class multi-modal classification accuracies of >76% for valence and >73% for arousal on the multi-modal AMIGOS and DEAP datasets, almost always better than state of the art. The required vector storage is seamlessly reduced by 98% and the frequency of vector requests by at least 1/5. The results demonstrate the potential of efficient hyperdimensional computing for low-power, multi-channeled emotion recognition tasks

Hyperdimensional computing for noninvasive brain-computer interfaces: Blind and one-shot classification of EEG error-related potentials

The mathematical properties of high-dimensional (HD) spaces show remarkable agreement with behaviors controlled by the brain. Computing with HD vectors, referred to as "hypervectors," is a brain-inspired alternative to computing with numbers. HD computing is characterized by generality, scalability, robustness, and fast learning, making it a prime candidate for utilization in application domains such as brain-computer interfaces. We describe the use of HD computing to classify electroencephalography (EEG) error-related potentials for noninvasive brain-computer interfaces. Our algorithm encodes neural activity recorded from 64 EEG electrodes to a single temporal-spatial hypervector. This hypervector represents the event of interest and is used for recognition of the subject's intentions. Using the full set of training trials, HD computing achieves on average 5% higher accuracy compared to a conventional machine learning method on this task (74.5% vs. 69.5%) and offers further advantages: (1) Our algorithm learns fast by using 34% of training trials while surpassing the conventional method with an average accuracy of 70.5%. (2) Conventional method requires prior domain expert knowledge to carefully select a subset of electrodes for a subsequent pre-processor and classier, whereas our algorithm blindly uses all 64 electrodes, tolerates noises in data, and the resulting hypervector is intrinsically clustered into HD space; in addition, most preprocessing of the electrode signal can be eliminated while maintaining an average accuracy of 71.7%

Membrane stripping enables effective electrochemical ammonia recovery from urine while retaining microorganisms and micropollutants

Ammonia recovery from urine avoids the need for nitrogen removal through nitrification/denitrification and re-synthesis of ammonia (NH3) via the Haber-Bosch process. Previously, we coupled an alkalifying electrochemical cell to a stripping column, and achieved competitive nitrogen removal and energy efficiencies using only electricity as input, compared to other technologies such as conventional column stripping with air. Direct liquid-liquid extraction with a hydrophobic gas membrane could be an alternative to increase nitrogen recovery from urine into the absorbent while minimizing energy requirements, as well as ensuring microbial and micropollutant retention. Here we compared a column with a membrane stripping reactor, each coupled to an electrochemical cell, fed with source-separated urine and operated at 20 A m−2. Both systems achieved similar nitrogen removal rates, 0.34 ± 0.21 and 0.35 ± 0.08 mol N L−1 d−1, and removal efficiencies, 45.1 ± 18.4 and 49.0 ± 9.3%, for the column and membrane reactor, respectively. The membrane reactor improved nitrogen recovery to 0.27 ± 0.09 mol N L−1 d−1 (38.7 ± 13.5%) while lowering the operational (electrochemical and pumping) energy to 6.5 kWhe kg N−1 recovered, compared to the column reactor, which reached 0.15 ± 0.06 mol N L−1 d−1 (17.2 ± 8.1%) at 13.8 kWhe kg N−1. Increased cell concentrations of an autofluorescent E. coli MG1655 + prpsM spiked in the urine influent were observed in the absorbent of the column stripping reactor after 24 h, but not for the membrane stripping reactor. None of six selected micropollutants spiked in the urine were found in the absorbent of both technologies. Overall, the membrane stripping reactor is preferred as it improved nitrogen recovery with less energy input and generated an E. coli- and micropollutant-free product for potential safe reuse. Nitrogen removal rate and efficiency can be further optimized by increasing the NH3 vapor pressure gradient and/or membrane surface area