Sit-to-Stand Phases Detection by Inertial Sensors

The Sit-to-Stand(STS) is defined as the transition from the sitting to standing position. It is commonly adopted in clinical practice because musculoskeletal or neurological degenerative disorders, as well as the natural process of ageing, deter-mine an increased difficulty in rising up from a seated position. This study aimed to detect the Sit To Stand phases using data from inertial sensors. Due to the high variability of this movement, and, consequently the difficulty to define events by thresholds, we used the machine learning. We collected data from 27 participants (13 females,24.37\ub13.32 years old). They wore 10 Inertial Sensors placed on: trunk,back(L4-L5),left and right thigh, tibia, and ankles. The par-ticipants were asked to stand from an height adjustable chair for 10 times. The STS exercises were recorded separately. The starting and ending points of each phase were identified by key events. The pre-processing included phases splitting in epochs. The features extracted were: mean, standard deviation, RMS, Max and min, COV and first derivative. The features were on the epochs for each sensor. To identify the most fitting classifier, two classifier algorithms,K-nearest Neighbours( KNN) and Support Vector Machine (SVM) were trained. From the data recorded, four dataset were created varying the epochs duration, the number of sensors. The validation model used to train the classifier. As validation model, we compared the results of classifiers trained using Kfold and Leave One Subject out (LOSO) models. The classifier performances were evaluated by confusion matrices and the F1 scores. The classifiers trained using LOSO technique as validation model showed higher values of predictive accuracy than the ones trained using Kfold. The predictive accuracy of KNN and SVM were reported below: \u2022 KFold \u2013 mean of overall predictive accuracy KNN: 0.75; F1 score: REST 0.86, TRUNK LEANING 0.35,STANDING 0.60,BALANCE 0.54, SITTING 0.55 \u2013 mean of overall predictive accuracy SVM: 0.75; F1 score: REST 0.89, TRUNK LEANING 0.48,STANDING 0.48,BALANCE 0.59, SITTING 0.62 \u2022 LOSO \u2013 mean of overall predictive accuracy KNN: 0.93; F1 score: REST 0.96, TRUNK LEANING 0.79,STANDING 0.89,BALANCE 0.95, SITTING 0.88 \u2013 mean of overall predictive accuracy SVM: 0.95; F1 score phases: REST 0.98, TRUNK LEANING 0.86,STANDING 0.91,BALANCE 0.98, SIT-TING 0.9

Virtually synchronous power plant control

During the last century, the electrical energy infrastructures have been governed by synchronous generators, producing electrical energy to the vast majority of the population worldwide. However, power systems are no longer what they used to be. During the last two decades of this new millennium the classical, centralized and hierarchical networks have experienced an intense integration of renewable energy sources, mainly wind and solar, thanks also to the evolution and development of power conversion and power electronics industry. Although the current electrical system was designed to have a core of generation power plants, responsible of producing the necessary energy to supply end users and a clear power flow, divided mainly into transmission and distribution networks, as well as scalable consumers connected at different levels, this scenario has dramatically changed with the addition of renewable generation units. The massive installation of wind and solar farms, connected at medium voltage networks, as well as the proliferation of small distributed generators interfaced by power converters in low voltage systems is changing the paradigm of energy generation, distribution and consumption. Despite the feasibility of this integration in the existing electrical network, the addition of these distributed generators made grid operators face new challenges, especially considering the stochastic profile of such energy producers. Furthermore, the replacement of traditional generation units for renewable energy sources has harmed the stability and the reliable response during grid contingencies. In order to cope with the difficult task of operating the electrical network, transmission system operators have increased the requirements and modified the grid codes for the newly integrated devices. In an effort to enable a more natural behavior of the renewable systems into the electrical grid, advanced control strategies were presented in the literature to emulate the behavior of traditional synchronous generators. These approaches focused mainly on the power converter relying on their local measurement points to resemble the operation of a traditional generating unit. However, the integration of those units into bigger systems, such as power plants, is still not clear as the effect of accumulating hundreds or thousands of units has not been properly addressed. In this regard, the work of this thesis deals with the study of the so-called virtual synchronous machine (VSM) in three control layers. Furthermore, an in-depth analysis of the general structure used for the different virtual synchronous machine approaches is presented, which constitutes the base implementation tree for all existent strategies of virtual synchronous generation. In a first stage, the most inner control loop is studied and analyzed regarding the current control on the power converter. This internal regulator is in charge of the current injection and the tracking of all external power reference. Afterward, the synchronous control is oriented to the device, where the generating unit relies on its local measurements to emulate a synchronous machine in the power converter. In this regard, a sensorless approach to the virtual synchronous machine is introduced, increasing the stability of the power converter and reducing the voltage measurements used. Finally, the model of the synchronous control is extrapolated into a power plant control layer to be able to regulate multiple units in a coordinated manner, thus emulating the behavior of a unique synchronous machine. In this regard, the local measurements are not used for the emulation of the virtual machine, but they are switched to PCC measurements, allowing to set the desired dynamic response at the power plant level.Postprint (published version

Technological advances in deep brain stimulation:Towards an adaptive therapy

Parkinson's disease (PD) is neurodegenerative movement disorder and a treatment method called deep brain stimulation (DBS) may considerably reduce the patient’s motor symptoms. The clinical procedure involves the implantation of a DBS lead, consisting of multiple electrode contacts, through which continuous high frequency (around 130 Hz) electric pulses are delivered in the brain. In this thesis, I presented the research which had the goal to improve current DBS technology, focusing on bringing the conventional DBS system a step closer to adaptive DBS, a personalized DBS therapy. The chapters in this thesis can be seen as individual building blocks for such an adaptive DBS system. After the general introduction, the first two chapters, two novel DBS lead designs are studied in a computational model. The model showed that both studied leads were able to exploit the novel distribution of the electrode contacts to shape and steer the stimulation field to activate more neurons in the chosen target compared to the conventional lead, and to counteract lead displacement. In the fourth chapter, an inverse current source density (CSD) method is applied on local field potentials (LFP) measured in a rat model. The pattern of CSD sources can act as a landmark within the STN to locate the potential stimulation target. The fifth and final chapter described the last building block of the DBS system. We introduced an inertial sensors and force sensor based measurement system, which can record hand kinematics and joint stiffness of PD patients. A system which can act as a feedback signal in an adaptive DBS system

New Approach of Indoor and Outdoor Localization Systems

Accurate determination of the mobile position constitutes the basis of many new applications. This book provides a detailed account of wireless systems for positioning, signal processing, radio localization techniques (Time Difference Of Arrival), performances evaluation, and localization applications. The first section is dedicated to Satellite systems for positioning like GPS, GNSS. The second section addresses the localization applications using the wireless sensor networks. Some techniques are introduced for localization systems, especially for indoor positioning, such as Ultra Wide Band (UWB), WIFI. The last section is dedicated to Coupled GPS and other sensors. Some results of simulations, implementation and tests are given to help readers grasp the presented techniques. This is an ideal book for students, PhD students, academics and engineers in the field of Communication, localization & Signal Processing, especially in indoor and outdoor localization domains

Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders

The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders

Optimal tuned mass-damper-inerter (TMDI) design in wind-excited tall buildings for occupants’ comfort serviceability performance and energy harvesting

The tuned mass-damper-inerter (TMDI) couples the classical tuned mass-damper (TMD), with an inerter device developing a resisting force proportional to the relative acceleration of its ends by the “inertance” constant. Previous works demonstrated that the inclusion of the TMDI leads to more efficient broadband vibration control for a range of different structures under different actions. This paper proposes a novel optimal TMDI design formulation to address occupants’ comfort in wind-excited slender tall buildings susceptible to vortex shedding (VS) effects and to explore optimal TMDI’s potential for transforming part of the windinduced kinetic energy to usable electricity in tall buildings. Attention is focused on investigating benefits of TMDIs with different inertial properties (i.e., secondary mass/weight and inertance) configured in different topologies defined by the number of floors spanned by the inerter device to connect the secondary mass to the building structure. Optimally designed TMDIs for a wide range of inertial properties and three different topologies are obtained through numerical solution of the underlying optimization problem for a benchmark 305.9m tall building with more than 6 height-to-width ratio subjected to experimentally calibrated spatiallycorrelated across-wind force field accounting for VS effects. Performance-based design (PBD) graphs on the TMDI inertial (mass-inertance) plane are furnished demonstrating that any fixed structural performance level in terms of occupants’ comfort (i.e., peak top floor acceleration) can be achieved through lightweight TMDIs if compared with classical TMDs as long as sufficient inertance is provided. Further, TMDI robustness to host structure properties and to reference wind velocity is shown to increase by increasing inertance or by spanning more floors in connecting the secondary mass with the host structure by the inerter. Lastly, it is found that increased available energy for harvesting in wind excited tall buildings is achieved by incorporating electromagnetic motors in TMDIs with varying damping property, while concurrent reduced floor acceleration and increased available energy for harvesting is accomplished by TMDI topologies with inerters spanning more floors