Neuromechanical and environment aware machine learning tool for human locomotion intent recognition

Current research suggests the emergent need to recognize and predict locomotion modes (LMs) and LM transitions to allow a natural and smooth response of lower limb active assistive devices such as prostheses and orthosis for daily life locomotion assistance. This Master dissertation proposes an automatic and user-independent recognition and prediction tool based on machine learning methods. Further, it seeks to determine the gait measures that yielded the best performance in recognizing and predicting several human daily performed LMs and respective LM transitions. The machine learning framework was established using a Gaussian support vector machine (SVM) and discriminative features estimated from three wearable sensors, namely, inertial, force and laser sensors. In addition, a neuro-biomechanical model was used to compute joint angles and muscle activations that were fused with the sensor-based features. Results showed that combining biomechanical features from the Xsens with environment-aware features from the laser sensor resulted in the best recognition and prediction of LM (MCC = 0.99 and MCC = 0.95) and LM transitions (MCC = 0.96 and MCC = 0.98). Moreover, the predicted LM transitions were determined with high prediction time since their detection happened one or more steps before the LM transition occurrence. The developed framework has potential to improve the assistance delivered by locomotion assistive devices to achieve a more natural and smooth motion assistance.This work has been supported in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/108309/2015, and part by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from FCT with the project SmartOs -Controlo Inteligente de um Sistema Ortótico Ativo e Autónomo- under Grant NORTE-01-0145-FEDER-030386, and by the FEDER Funds through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with the Reference Project under Grant POCI-01-0145-FEDER-006941

Diffractive Higgs Production by AdS Pomeron Fusion

The double diffractive Higgs production at central rapidity is formulated in terms of the fusion of two AdS gravitons/Pomerons first introduced by Brower, Polchinski, Strassler and Tan in elastic scattering. Here we propose a simple self-consistent holographic framework capable of providing phenomenologically compelling estimates of diffractive cross sections at the LHC. As in the traditional weak coupling approach, we anticipate that several phenomenological parameters must be tested and calibrated through factorization for a self-consistent description of other diffractive process such as total cross sections, deep inelastic scattering and heavy quark production in the central region.Comment: 53 pages, 8 figure

Strategic Learning for Active, Adaptive, and Autonomous Cyber Defense

The increasing instances of advanced attacks call for a new defense paradigm that is active, autonomous, and adaptive, named as the \texttt{`3A'} defense paradigm. This chapter introduces three defense schemes that actively interact with attackers to increase the attack cost and gather threat information, i.e., defensive deception for detection and counter-deception, feedback-driven Moving Target Defense (MTD), and adaptive honeypot engagement. Due to the cyber deception, external noise, and the absent knowledge of the other players' behaviors and goals, these schemes possess three progressive levels of information restrictions, i.e., from the parameter uncertainty, the payoff uncertainty, to the environmental uncertainty. To estimate the unknown and reduce uncertainty, we adopt three different strategic learning schemes that fit the associated information restrictions. All three learning schemes share the same feedback structure of sensation, estimation, and actions so that the most rewarding policies get reinforced and converge to the optimal ones in autonomous and adaptive fashions. This work aims to shed lights on proactive defense strategies, lay a solid foundation for strategic learning under incomplete information, and quantify the tradeoff between the security and costs.Comment: arXiv admin note: text overlap with arXiv:1906.1218

Development of a 3D workspace Shoulder Assessment Tool Incorporating Electromyography and an Inertial Measurement Unit - A preliminary study

Traditional shoulder Range of Movement (ROM) measurement tools suffer from inaccuracy or from long experimental set-up times. Recently, it has been demonstrated that relatively low-cost wearable inertial measurement unit (IMU) sensors can overcome many of the limitations of traditional motion tracking systems. The aim of this study is to develop and evaluate a single IMU combined with an Electromyography (EMG) sensor to monitor the 3D reachable workspace with simultaneous measurement of deltoid muscle activity across the shoulder ROM. Six volunteer subjects with healthy shoulders and one participant with a ‘frozen’ shoulder were recruited to the study. Arm movement in 3D space was plotted in spherical coordinates while the relative EMG intensity of any arm position is presented graphically. The results showed that there was an average ROM surface area of 27291±538 deg2 among all six healthy individuals and a ROM surface area of 13571±308 deg2 for the subject with frozen shoulder. All three sections of the deltoid show greater EMG activity at higher elevation angles. Using such tools enables individuals, surgeons and physiotherapists to measure the maximum envelope of motion in conjunction with muscle activity in order to provide an objective assessment of shoulder performance in the voluntary 3D workspace

An Updated Meta-Analysis of Endothelial Nitric Oxide Synthase Gene: Three Well-Characterized Polymorphisms with Hypertension

BACKGROUND: Numerous individually underpowered association studies have been conducted on endothelial nitric oxide synthase (eNOS) genetic variants across different ethnic populations, however, the results are often irreproducible. We therefore aimed to meta-analyze three eNOS widely-evaluated polymorphisms, G894T (rs1799983) in exon 7, 4b/a in intron 4, and T-786C (rs2070744) in promoter region, in association with hypertension from both English and Chinese publications, while addressing between-study heterogeneity and publication bias. METHODS: Data were analyzed using Stata software (version 11.0), and random-effects model was applied irrespective of between-study heterogeneity, which was evaluated by subgroup and meta-regression analyses. Publication bias was weighed using the Egger's test and funnel plot. RESULTS: There were total 19284/26003 cases/controls for G894T, and 6890/6858 for 4b/a, and 5346/6392 for T-786C polymorphism. Overall comparison of allele 894T with 894G in all study populations yielded a 16% increased risk for hypertension (odds ratio [OR] = 1.16; 95% confidence interval [95% CI]: 1.07-1.27; P = 0.001), and particularly a 32% increased risk (95% CI: 1.16-1.52; P<0.0005) in Asians and a 40% increased risk (95% CI: 1.19-1.65; P<0.0005) in Chinese. Further subgroup analyses suggested that published languages accounted for the heterogeneity for G894T polymorphism. The overall OR of allele 4a versus 4b was 1.29 (95% CI: 1.13-1.46; P<0.0005) in all study populations, and this estimate was potentiated in Asians (OR = 1.42; 95% CI: 1.16-1.72; P<0.0005). For T-786C, ethnicity-stratified analyses suggested a significantly increased risk for -786C allele (OR = 1.25; 95% CI: 1.06-1.47; P = 0.007) and -786CC genotype (OR = 1.69; 95% CI: 1.20-2.38; P = 0.003) in Whites. As an aside, the aforementioned risk estimates reached significance after Bonferroni correction. Finally, meta-regression analysis on other study-level covariates failed to provide any significance for all polymorphisms. CONCLUSION: We, via a comprehensive meta-analysis, ascertained the role of eNOS G894T and 4b/a polymorphisms on hypertension in Asians, and T-786C polymorphism in Whites

Salivary Biomarkers for Detection of Systemic Diseases

Peer reviewe

Systemic Risk Modeling with Lévy Copulas

We investigate a systemic risk measure known as CoVaR that represents the value-at-risk (VaR) of a financial system conditional on an institution being under distress. For characterizing and estimating CoVaR, we use the copula approach and introduce the normal tempered stable (NTS) copula based on the Lévy process. We also propose a novel backtesting method for CoVaR by a joint distribution correction. We test the proposed NTS model on the daily S&amp;P 500 index and Dow Jones index with in-sample and out-of-sample tests. The results show that the NTS copula outperforms traditional copulas in the accuracy of both tail dependence and marginal processes modeling