Development and validation of a neural network for adaptive gait cycle detection from kinematic data

(1) Background: Instrumented gait analysis is a tool for quantification of the different aspects of the locomotor system. Gait analysis technology has substantially evolved over the last decade and most modern systems provide real-time capability. The ability to calculate joint angles with low delays paves the way for new applications such as real-time movement feedback, like control of functional electrical stimulation in the rehabilitation of individuals with gait disorders. For any kind of therapeutic application, the timely determination of different gait phases such as stance or swing is crucial. Gait phases are usually estimated based on heuristics of joint angles or time points of certain gait events. Such heuristic approaches often do not work properly in people with gait disorders due to the greater variability of their pathological gait pattern. To improve the current state-ofthe- art, this thesis aims to introduce a data-driven approach for real-time determination of gait phases from kinematic variables based on long short-term memory recurrent neural networks (LSTM RNNs). (2) Methods: In this thesis, 56 measurements with gait data of 11 healthy subjects, 13 individuals with incomplete spinal cord injury and 10 stroke survivors with walking speeds ranging from 0.2 m s up to 1 m s were used to train the networks. Each measurement contained kinematic data from the corresponding subject walking on a treadmill for 90 seconds. Kinematic data was obtained by measuring the positions of reflective markers on body landmarks (Helen Hayes marker set) with a sample rate of 60Hz. For constructing a ground truth, gait data was annotated manually by three raters. Two approaches, direct regression of gait phases and estimation via detection of the gait events Initial Contact and Final Contact were implemented for evaluation of the performance of LSTM RNNs. For comparison of performance, the frequently cited coordinate- and velocity-based event detection approaches of Zeni et al. were used. All aspects of this thesis have been implemented within MATLAB Version 9.6 using the Deep Learning Toolbox. (3) Results: The mean time difference between events annotated by the three raters was −0.07 ± 20.17ms. Correlation coefficients of inter-rater and intra-rater reliability yielded mainly excellent or perfect results. For detection of gait events, the LSTM RNN algorithm covered 97.05% of all events within a scope of 50ms. The overall mean time difference between detected events and ground truth was −11.62 ± 7.01ms. Temporal differences and deviations were consistently small over different walking speeds and gait pathologies. Mean time difference to the ground truth was 13.61 ± 17.88ms for the coordinate-based approach of Zeni et al. and 17.18 ± 15.67ms for the velocity-based approach. For estimation of gait phases, the gait phase was determined as a percentage. Mean squared error to the ground truth was 0.95 ± 0.55% for the proposed algorithm using event detection and 1.50 ± 0.55% for regression. For the approaches of Zeni et al., mean squared error was 2.04±1.23% for the coordinate-based approach and 2.24±1.34% for the velocity-based approach. Regarding mean absolute error to the ground truth, the proposed algorithm achieved a mean absolute error of 1.95±1.10% using event detection and one of 7.25 ± 1.45% using regression. Mean absolute error for the coordinate-based approach of Zeni et al. was 4.08±2.51% and 4.50±2.73% for the velocity-based approach. (4) Conclusion: The newly introduced LSTM RNN algorithm offers a high recognition rate of gait events with a small delay. Its performance outperforms several state-of-theart gait event detection methods while offering the possibility for real-time processing and high generalization of trained gait patterns. Additionally, the proposed algorithm is easy to integrate into existing applications and contains parameters that self-adapt to individuals’ gait behavior to further improve performance. In respect to gait phase estimation, the performance of the proposed algorithm using event detection is in line with current wearable state-of-the-art methods. Compared with conventional methods, performance of direct regression of gait phases is only moderate. Given the results, LSTM RNNs demonstrate feasibility regarding event detection and are applicable for many clinical and research applications. They may be not suitable for the estimation of gait phases via regression. For LSTM RNNs, it can be assumed, that with a more optimal configuration of the networks, a much higher performance is achieved

Integrated modeling tool for performance engineering of complex computer systems

This report summarizes Advanced System Technologies' accomplishments on the Phase 2 SBIR contract NAS7-995. The technical objectives of the report are: (1) to develop an evaluation version of a graphical, integrated modeling language according to the specification resulting from the Phase 2 research; and (2) to determine the degree to which the language meets its objectives by evaluating ease of use, utility of two sets of performance predictions, and the power of the language constructs. The technical approach followed to meet these objectives was to design, develop, and test an evaluation prototype of a graphical, performance prediction tool. The utility of the prototype was then evaluated by applying it to a variety of test cases found in the literature and in AST case histories. Numerous models were constructed and successfully tested. The major conclusion of this Phase 2 SBIR research and development effort is that complex, real-time computer systems can be specified in a non-procedural manner using combinations of icons, windows, menus, and dialogs. Such a specification technique provides an interface that system designers and architects find natural and easy to use. In addition, PEDESTAL's multiview approach provides system engineers with the capability to perform the trade-offs necessary to produce a design that meets timing performance requirements. Sample system designs analyzed during the development effort showed that models could be constructed in a fraction of the time required by non-visual system design capture tools

The slicing extent technique for fast ray tracing

A new technique for image generation using ray tracing is introduced. The “Slicing Extent Technique” (SET) partitions object space with slicing planes perpendicular to all three axes. Planes are divided into two dimensional rectangular cells, which contain pointers to nearby objects. Cell size and the space between slices varies, and is determined by the objects’ locations and orientations. Unlike oct-tree and other space-partitioning methods, SET is not primarily concerned with dividing space into mutually exclusive volume elements (‘voxels’) and identifying objects within each voxel. Instead, SET is based on analysis of projections of objects onto slicing planes. In comparison to the existing space subdivision methods for ray tracing, SET avoids tree traversal and exhibit no anomalous behavior. There is no reorganization when new objects arrive. Preprocessing to create slices is inexpensive and produces a finely tuned filter mechanism which supports rapid ray tracing

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain

University journeys: alternative entry students and their construction of a means of succeeding in an unfamiliar university culture

This research study takes a multi-disciplinary perspective, using critical discourse theory, transactional communication theory and cross-cultural theory to contribute insight into the experiences of alternative entry students as they strive to access and participate in higher education. The study seeks to determine how these students learn to persevere: how they construct their means of succeeding in the university culture. The methodological structure of the research comprises a collective case study design, encompassing critical ethnography, action research and reflexive approaches to guide a deeper understanding of the experiences of studying at a regional Australian university. The reflexive nature of the research facilitated the development of an original theoretical construct, the ‘deficit-discourse’ shift, which challenges higher education policy and practice, in particular, in relation to academics’ roles in making their discourses explicit and in collaborating with students to facilitate students’ perseverance and success. The research has also generated two models: the Framework for Student Engagement and Mastery and the Model for Student Success at University. The Framework re-conceptualises the university as a dynamic culture made up of a multiplicity of sub-cultures, each with its own literacy or discourse. The Framework recasts the first year experience as a journey, with students’ transition re-conceptualised as the processes of gaining familiarity with and negotiating these new literacies and discourses whereas perseverance is viewed as the processes of mastering and demonstrating them. The Model provides a three step practical strategy (incorporating reflective practice, socio-cultural practice and critical practice) for achieving this engagement: for empowering students to negotiate, master and demonstrate their mastery of the university culture’s multiple discourses. Together, the two models provide students with a means of succeeding in the new university culture

The nature of reflective practice in Grade R

Thesis (MEd (Education))--Cape Peninsula University of Technology, 2019The quality of education in South Africa has drawn critical attention as children continue to perform poorly as they progress through school. Reflective practice is promoted and implemented internationally as a method to improve quality teaching and learning. Nationally a growing interest in the implementation of reflective practice is reflected in the Department of Basic Education’s suggestion that teachers make use of reflective practice to inform their classroom planning. The objective of this study was to investigate the knowledge and understanding Grade R teachers have of reflective practice. As Grade R is the first year and the foundation of a child’s school career, it was of interest to explore whether teachers reflect on their practice. This study was located in an interpretivist paradigm using a case study design. Two Grade R teachers were interviewed using semi-structured interviews. Their annual, termly and weekly planning was analysed alongside the transcripts of the interviews, using thematic analysis to identify common themes of reflective practice in Grade R. Five themes emerged from the analysis, which enabled the exploration of the benefits and challenges of using reflective practice in Grade R. These five themes allowed for the development of the idea of using reflective practice as a means for improving teaching and learning in Grade R. The teachers interviewed value the idea of reflective practice as a way to meet the needs of the children. They describe it as an innate aspect of their teaching. However, they have a tacit understanding and knowledge of reflective practice and it was not evident in the documents they use to inform their teaching. For reflective practice to be encouraged teachers need support from within their schools and from the Department of Basic Education

Parametrising arbitrary galaxy morphologies: potentials and pitfalls

We demonstrate that morphological observables (e.g. steepness of the radial light profile, ellipticity, asymmetry) are intertwined and cannot be measured independently of each other. We present strong arguments in favour of model-based parametrisation schemes, namely reliability assessment, disentanglement of morphological observables, and PSF modelling. Furthermore, we demonstrate that estimates of the concentration and Sersic index obtained from the Zurich Structure & Morphology catalogue are in excellent agreement with theoretical predictions. We also demonstrate that the incautious use of the concentration index for classification purposes can cause a severe loss of the discriminative information contained in a given data sample. Moreover, we show that, for poorly resolved galaxies, concentration index and M_20 suffer from strong discontinuities, i.e. similar morphologies are not necessarily mapped to neighbouring points in the parameter space. This limits the reliability of these parameters for classification purposes. Two-dimensional Sersic profiles accounting for centroid and ellipticity are identified as the currently most reliable parametrisation scheme in the regime of intermediate signal-to-noise ratios and resolutions, where asymmetries and substructures do not play an important role. We argue that basis functions provide good parametrisation schemes in the regimes of high signal-to-noise ratios and resolutions. Concerning Sersic profiles, we show that scale radii cannot be compared directly for profiles of different Sersic indices. Furthermore, we show that parameter spaces are typically highly nonlinear. This implies that significant caution is required when distance-based classificaton methods are used.Comment: 18 pages, 13 figure

An Enhanced Source Location Privacy based on Data Dissemination in Wireless Sensor Networks (DeLP)

open access articleWireless Sensor Network is a network of large number of nodes with limited power and computational capabilities. It has the potential of event monitoring in unattended locations where there is a chance of unauthorized access. The work that is presented here identifies and addresses the problem of eavesdropping in the exposed environment of the sensor network, which makes it easy for the adversary to trace the packets to find the originator source node, hence compromising the contextual privacy. Our scheme provides an enhanced three-level security system for source location privacy. The base station is at the center of square grid of four quadrants and it is surrounded by a ring of flooding nodes, which act as a first step in confusing the adversary. The fake node is deployed in the opposite quadrant of actual source and start reporting base station. The selection of phantom node using our algorithm in another quadrant provides the third level of confusion. The results show that Dissemination in Wireless Sensor Networks (DeLP) has reduced the energy utilization by 50% percent, increased the safety period by 26%, while providing a six times more packet delivery ratio along with a further 15% decrease in the packet delivery delay as compared to the tree-based scheme. It also provides 334% more safety period than the phantom routing, while it lags behind in other parameters due to the simplicity of phantom scheme. This work illustrates the privacy protection of the source node and the designed procedure may be useful in designing more robust algorithms for location privac