Characterizing the Noise Associated with Sensor Placement and Motion Artifacts and Overcoming its Effects for Body-worn Physiological Sensors

Wearable sensors for continuous physiological monitoring have the potential to change the paradigm for healthcare by providing information in scenarios not covered by the existing clinical model. One key challenge for wearable physiological sensors is that their signal-to-noise ratios are low compared to those of their medical grade counterparts in hospitals. Two primary sources of noise are the sensor-skin contact interface and motion artifacts due to the user’s daily activities. These are challenging problems because the initial sensor placement by the user may not be ideal, the skin conditions can change over time, and the nature of motion artifacts is not predictable. The objective of this research is twofold. The first is to design sensors with reconfigurable contact to mitigate the effects of misplaced sensors or changing skin conditions. The second is to leverage signal processing techniques for accurate physiological parameter estimation despite the presence of motion artifacts. In this research, the sensor contact problem was specifically addressed for dry-contact electroencephalography (EEG). The proposed novel extension to a popular existing EEG electrode design enabled reconfigurable contact to adjust to variations in sensor placement and skin conditions over time. Experimental results on human subjects showed that reconfiguration of contact can reduce the noise in collected EEG signals without the need for manual intervention. To address the motion artifact problem, a particle filter based approach was employed to track the heart rate in cardiac signals affected by the movements of the user. The algorithm was tested on cardiac signals from human subjects running on a treadmill and showed good performance in accurately tracking heart rate. Moreover, the proposed algorithm enables fusion of multiple modalities and is also computationally more efficient compared to other contemporary approaches

A real-time noise cancelling EEG electrode employing Deep Learning

Two major problems of head worn electroencephalogram (EEG) are muscle and eye-blink artefacts, in particular in non-clinical environments while performing everyday tasks. Current artefact removal techniques such as principle component analysis (PCA) or independent component analysis (ICA) take signals from a high number of electrodes and separate the noise from the signal by processing them offline in a computationally expensive and slow way. In contrast, we present a smart compound electrode which is able to learn in real-time to remove artefacts. The smart 3D printed electrode consists of a central electrode and a ring electrode where poly-lactate acid (PLA) was used for the the base and Ag/AgCl for the conductive parts allowing standard manufacturing processes. A new deep learning algorithm then learns continuously to remove both eye-blink and muscle artefacts which combines the real-time capabilities of adaptive filters with the power of deep neural networks. The electrode setup together with the deep learning algorithm increases the signal to noise ratio of the EEG in average by 20 dB. Our approach offers a simple 3D printed design in combination with a real-time algorithm which can be integrated into the electrode itself. This electrode has the potential to provide high quality EEG in non-clinical and consumer applications, such as sleep monitoring and brain-computer interface (BCI).Comment: 12 pages, 4 figures, code available under http://doi.org/10.5281/zenodo.413110

Real-time noise cancellation with deep learning

Biological measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We present a new real-time deep learning algorithm which produces adaptively a signal opposing the noise so that destructive interference occurs. As a proof of concept, we demonstrate the algorithm’s performance by reducing electromyogram noise in electroencephalograms with the usage of a custom, flexible, 3D-printed, compound electrode. With this setup, an average of 4dB and a maximum of 10dB improvement of the signal-to-noise ratio of the EEG was achieved by removing wide band muscle noise. This concept has the potential to not only adaptively improve the signal-to-noise ratio of EEG but can be applied to a wide range of biological, industrial and consumer applications such as industrial sensing or noise cancelling headphones

Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

Topological Changes in the Functional Brain Networks Induced by Isometric Force Exertions Using a Graph Theoretical Approach: An EEG-based Neuroergonomics Study

Neuroergonomics, the application of neuroscience to human factors and ergonomics, is an emerging science focusing on the human brain concerning performance at work and in everyday settings. The advent of portable neurophysiological methods, including electroencephalography (EEG), has enabled measurements of real-time brain activity during physical tasks without restricting body movements. However, the EEG signatures of different physical exertion activity levels that involve the musculoskeletal system in everyday settings remain poorly understood. Furthermore, the assessment of functional connectivity among different brain regions during different force exertion levels remains unclear. One approach to investigating the brain connectome is to model the underlying mechanism of the brain as a complex network. This study applied employed a graph-theoretical approach to characterize the topological properties of the functional brain network induced by predefined force exertion levels, namely extremely light (EL), light (L), somewhat hard (SWH), hard (H), and extremely hard (EH) in two frequency bands, i.e., alpha and beta. Twelve female participants performed an isometric force exertion task and rated their perception of physical comfort at different physical exertion levels. A CGX-Mobile-64 EEG was used for recording spontaneous brain electrical activity. After preprocessing the EEG data, a source localization method was applied to study the functional brain connectivity at the source level. Subsequently, the alpha and beta networks were constructed by calculating the coherence between all pairs of 84 brain regions of interests that were selected using Brodmann Areas. Graph -theoretical measures were then employed to quantify the topological properties of the functional brain networks at different levels of force exertions at each frequency band. During an \u27extremely hard\u27 exertion level, a small-world network was observed for the alpha coherence network, whereas an ordered network was observed for the beta coherence network. The results suggest that high-level force exertions are associated with brain networks characterized by a more significant clustering coefficient, more global and local efficiency, and shorter characteristic path length under alpha coherence. The above suggests that brain regions are communicating and cooperating to a more considerable degree when the muscle force exertions increase to meet physically challenging tasks. The exploration of the present study extends the current understanding of the neurophysiological basis of physical efforts with different force levels of human physical exertion to reduce work-related musculoskeletal disorders

Cooperative Human-Machine Interaction in Industrial Environments

Until the present days, there has been little advances in the relation between the shop-floor operator in an industrial environment and the machines execution the manufacturing processes. Normally, the semi-automatic processes for collaborative assembly in industry are composed of a human and non-human elements. In the human perspective, one or more persons can be working in the same cell directly or indirectly with a non-human entity. In a cell can exist several machines, normally robotic arms that perform very specific collaborative tasks with the operators. However, the latest advances are mostly related with security issues and regulations, like immediately stopping the machine if a human touches it, and not much related with operative issues like adjusting the process velocity (within a certain window of cycle time) or give preference to some tasks over another in the beginning of the shift, to benefit the operator's working conditions. Therefore, a step forward to a more advanced interaction between machine and operator should be taken, towards a more adaptive and rich symbiosis. The main goal of the present Dissertation is to explore the relation between the shop-floor operator and the machine in a cyber physical system. For that purpose, biometric sensors will be used (ECG, EMG, EDA, PZT, wearables and others) to monitor the operators physiology during the operative times, and based on that, explore how a collaborative process can be adapted to minimize the operator's stress and fatigue. First, the correct set of sensors should be explored to understand how stress and fatigue metrics can be calculated. Secondly, optimization techniques need to be studied in order to, e.g. finds the correct machine's process parameterization that, on one hand, minimizes the operator's fatigue and stress, and on the other, do not jeopardizes the process requirements in terms of timing and quality. Therefore, this can be stated as a multivariate optimization problem

Building And Validating Next-Generation Neurodevices Using Novel Materials, Fabrication, And Analytic Strategies

Technologies that enable scientists to record and modulate neural activity across spatial scales are advancing the way that neurological disorders are diagnosed and treated, and fueling breakthroughs in our fundamental understanding of brain function. Despite the rapid pace of technology development, significant challenges remain in realizing safe, stable, and functional interfaces between manmade electronics and soft biological tissues. Additionally, technologies that employ multimodal methods to interrogate brain function across temporal and spatial scales, from single cells to large networks, offer insights beyond what is possible with electrical monitoring alone. However, the tools and methodologies to enable these studies are still in their infancy. Recently, carbon nanomaterials have shown great promise to improve performance and multimodal capabilities of bioelectronic interfaces through their unique optical and electronic properties, flexibility, biocompatibility, and nanoscale topology. Unfortunately, their translation beyond the lab has lagged due to a lack of scalable assembly methods for incorporating such nanomaterials into functional devices. In this thesis, I leverage carbon nanomaterials to address several key limitations in the field of bioelectronic interfaces and establish scalable fabrication methods to enable their translation beyond the lab. First, I demonstrate the value of transparent, flexible electronics by analyzing simultaneous optical and electrical recordings of brain activity at the microscale using custom-fabricated graphene electronics. Second, I leverage a recently discovered 2D nanomaterial, Ti3C2 MXene, to improve the capabilities and performance of neural microelectronic devices. Third, I fabricate and validate human-scale Ti3C2 MXene epidermal electrode arrays in clinical applications. Leveraging the unique solution-processability of Ti3C2 MXene, I establish novel fabrication methods for both high-resolution microelectrode arrays and macroscale epidermal electrode arrays that are scalable and sufficiently cost-effective to allow translation of MXene bioelectronics beyond the lab and into clinical use. Thetechnologies and methodologies developed in this thesis advance bioelectronic technology for both research and clinical applications, with the goal of improving patient quality of life and illuminating complex brain dynamics across spatial scales

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

OBSERVER-BASED-CONTROLLER FOR INVERTED PENDULUM MODEL

This paper presents a state space control technique for inverted pendulum system. The system is a common classical control problem that has been widely used to test multiple control algorithms because of its nonlinear and unstable behavior. Full state feedback based on pole placement and optimal control is applied to the inverted pendulum system to achieve desired design specification which are 4 seconds settling time and 5% overshoot. The simulation and optimization of the full state feedback controller based on pole placement and optimal control techniques as well as the performance comparison between these techniques is described comprehensively. The comparison is made to choose the most suitable technique for the system that have the best trade-off between settling time and overshoot. Besides that, the observer design is analyzed to see the effect of pole location and noise present in the system

A Review of Resonant Converter Control Techniques and The Performances

paper first discusses each control technique and then gives experimental results and/or performance to highlights their merits. The resonant converter used as a case study is not specified to just single topology instead it used few topologies such as series-parallel resonant converter (SPRC), LCC resonant converter and parallel resonant converter (PRC). On the other hand, the control techniques presented in this paper are self-sustained phase shift modulation (SSPSM) control, self-oscillating power factor control, magnetic control and the H-∞ robust control technique