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Myofascial trigger points are hyperirritable, unpleasant when compressed, and they have the ability to generate anomalies of the autonomic nervous system, referred pain, and motor dysfunction. Objective: To determine the Association of quality of life and pain intensity in patients of trigger points. Methods: Data were gathered from the Central Park Teaching Hospital, the Mayo Hospital, the Doctors Hospital, and the Jinnah Hospital. Our investigation focused on the prevalence of disability and quality of life in patients with trigger points. For quality of life and pain, we used the SF 36 Functional Limitation and General Health questionnaire; a visual analog scale was used. Results: Out of 152 patients, 87(57.2%) were males, and 65(42.8%) were females. The patients mean age was 38 ± 12.78 yrs. with a minimum age of 22 yrs. And maximum age of 72 yrs. 4(2.6%) patients has mild Pain, 67(44.1%) have moderate pain, 81(53.3%) have severe pain. 67(44.1%) patients have poor Health quality, 81(53.3%) have moderate health quality, and 4(2.6%) have good health quality. P-value for pain intensity and general health was 0.00, so we reject the null hypothesis and accept the alternative hypothesis, which indicated a significant difference between pain intensity and general health. p-Value was 0.012 for pain intensity and physical functioning. Conclusion: Hence it was concluded that the majority of the patients with trigger points have a poor quality of life and severe Pain. There was an association between quality of life and pain intensity.

GNSS are widely used in localization applications. These systems have the advantage of being able to locate, at low-cost, receivers anywhere on the planet's surface without prior knowledge of the position. However, the accuracy of the provided position strongly depends on the quality of reception and on the number of signals received. And these depend on the environment around the receiver. Indeed, certain environments such as urban canyons or forests are known to be challenging because multiple phenomena such as multipath, Non-Light Of Sight (NLOS), or interference are common there. These phenomena induce an erroneous estimate of the position. In applications where the localization function does not constitute a critical function involving the safety of the overall system, this erroneous estimate only constitutes an inconvenience. But in applications for which the safety of goods or people is at stake, the tolerance of what is called the integrity risk is infinitesimal, not to say null. Moreover, in this type of application, it is not so much the position that matters but the ability to limit the unknown position error. For that, the literature proposes different possibilities of protection level calculation. The ideal protection level would be the one that limits the unknown error as closely as possible without ever underestimating it. This makes it possible to minimize hazardous operations as well as unavailability of the localization function. In this paper, we are interested in the impact of GNSS observation weighting models on the protection levels. We propose a model capable of carrying a multi-criteria and multi-parametric strategy allowing a better adaptivity to the navigation context. The encouraging experimental results show that a parametric and constrained modelling strategy of errors, from the design step, relaxes the requirements on the calculation of the protection levels. They also show the complementarity between the characterization of errors which must be concerned with the functional behaviour and a Fault Detection and Exclusion layer which is responsible for the dysfunctional behaviour.

ION ITM 2021 - International Technical Meeting of Institute of Navigation, Virtual, , 25-/01/2021 - 28/01/2021; The accuracy of the satellite-based positioning system is severely affected in the presence of local phenomenons like multipath, NLOS and interferences. These local effects are difficult to model due to spatial and (or) temporal dependencies. This study aims to model the position error on the track and later use it for the track classification. The characterization of the track errors can be beneficial as it can help to increase the capacity of the railway track by providing adaptable protection level and also for sending warning alerts to the other trains if any of them is in degraded mode to avoid any sort of accidents. We present a very simple machine learning approach to model the position error. For this purpose, we investigate the Expectation-Maximization (EM) algorithm to estimate the parameters of the Gaussian Mixture Model (GMM). We intuitively selected 3 classes to represent different error shape features to avoid complexity and to keep some diversity. In the learning phase, 5 different runs are used to capture as much as the error variations on the track throughout the day. The models estimated in the learning process is applied to the test samples for the track characterization. The results show that class representation varies due to the error variation within the day. For some particular obstacles, the track is always represented by the class that shows deteriorated conditions. It also shows that the class representation is very similar to the learning samples when the error models are applied to the test sample collected on a different day with a nearly similar satellite configuration.

L'article présente le projet ERSAT GGC et les techniques de détection des effets locaux sur les GNSS développées pour l'introduction du GNSS dans le système ERTMS/ETCS

ENC 2020, 28th European Navigation Conference, Dresden , ALLEMAGNE, 22-/11/2020 - 25/11/2020; GNSS observations weighting models have received attention in the past decades. Several reasons can explain this gain in consideration. It mainly corresponds to the emergence of autonomous, global, continuous, precise and integrity-compliant localization systems. Indeed, for safety critical applications, adopting a naive strategy of observations' equal weighting could not guarantee to achieve all goals, and even more for terrestrial applications where the local environment can strongly disturb signals reception. The existing observation models use one or two metrics to estimate the confidence to be given to an observation. The first is the satellites' elevations. This model considers that the satellites with low elevation are more subject to global errors (orbit and atmosphere errors) or local multipaths or NLOS phenomena. The second criterion is the carrier to noise ratio provided by the receiver. However, combining these two criteria by a simple multiplication of the two models taken separately, presents a limit: the result is no more « under control ». In this study, we propose to formulate a new weighting strategy using these two criteria more guided by expertise's expected results.

ION ITM 2020, International Technical Meeting of Institute of Navigation, San Diego, ETATS-UNIS, 21-/01/2020 - 24/01/2020; GNSS used as a standalone positioning system fulfils most of the requirements for many applications since decades. However, the need of high available, accurate and fail-safe positioning systems for new applications such as rail autonomous vehicles for train signaling applications motivates the community to explore novel solutions. In this context, multi-constellation multi-frequency GNSS receivers have the potential to enhance the positioning solutions. However, when applied to safety-critical land transportation system, the localization function not only needs to meet accuracy requirements, but more importantly, it also needs to bound the positioning errors in order to reduce the risk of unexpected and undetected faults. Thus, in order to reach continuous and fail-safe positioning solutions, the use of complementary heterogeneous sensors with a smart hybridization becomes essential. For safety-related and regulated applications as in railways, one will also need, as a next step, to develop the capacity of assessing the safety of the hybrid solution. The concept of integrity can help to reach this requirement and is currently investigated for rail applications. RAIM (Receiver Autonomous Integrity Monitoring) was first defined for aviation applications and is widely used for their safety assessment. However, due to the different safety requirements and operational environment in aviation and land applications (i.e. railway and road application), dedicated algorithms need to be developed for such applications. Indeed, terrestrial applications can face particularly harsh environments (urban canyon, forests ...) in which a dilemma appears between the (reduced) availability of visible satellites and the possibility of encountering multiple simultaneous errors (NLOS, multipath interferences ...). Under these conditions, implementing a strategy based solely on a FDE layer can lead to a decrease in the availability of the localization function. Conversely, relying solely on a strategy of weighting observation error models cannot constitute an acceptable solution from a safety point of view. Only a harmonious combination of these two strategies in a stringent environmental condition can achieve the availability and security requirements. With a real dataset collected along a railway line, we compare the positioning accuracy of a GNSS/IMU tightly coupled system before and after weighting and FDE schemes. The results show that the average value of Horizontal Position Error (HPE) has been reduced after applying the performance improvement method on the GNSS/IMU system. It also shows that the implemented Horizontal Protection Level (HPL) correctly bounds HPE.

This deliverable is the second report delivered by GATE4Rail WP3. The general objective of WP3 was to define a comprehensive methodology and the associated tools able to characterise the Global Navigation Satellite System (GNSS) performance into the railways application scenarios defined in WP2 in the framework of a VB detection in ERTMS (European Railway Traffic Management System)/ETCS (European Train Control System) context.The work performed in WP3 is focused on the definition of the main railway environments that will impact GNSS reception, thus PVT performance. This deliverable D3.2 in particular, defines which are the key hazards (both local and global) that have to be taken into account and simulated in order to cover the main events that can occur during a railway operational scenario and how error models will be introduced in the simulation framework defined by the project.

Positioning in indoor environment is one of the most important and interesting topic in navigation system that still present numerous open problems to investigate. Many researchers are attempting to investigate on technologies able to reach valid location in an indoor space, where the well-known GNSS positioning systems are not able to operate. Among the many solutions proposed for location-based services, several wireless communications technologies have the potential to be employed for indoor positioning. In this paper, an Ultrawideband (UWB) indoor positioning commercial system is presented which exploits two-way time of flight (TWTF) to compute range measurements. These measurements are used in multi-lateration method to compute the position of a trans-receiver (TAG). Firstly, the calibration results have been shown, comparing the internal IMU sensor of UWB system with a mass-market one, using the angles measured thanks to the total station as reference. Secondly, we also have statically analysed the positioning and the ranging capabilities of the system in a favourable environment, as an indoor office room. The average 3D accuracy obtained from the test is 100 ± 25mm. Also the ranging measurements has been analysed as the raw data from which the Pozyx® inner algorithm starts to compute the positions. It has been demonstrated that the range accuracy is about 320 ± 30mm. After these first tests, the system has also been tested in a harsh environment in a narrow corridor where a horizontal accuracy of about 87.4 mm is obtained with a maximum ranging error of ±225 mm. The system and algorithms tested in this report gave almost similar performances in both environments.

In the frame of GATE4Rail project, Work Package (WP) 3 deals with the definition of a comprehensive methodology and the associated tools able to characterise the GNSS performance into the railways application scenarios defined in WP2 in the framework of a Virtual Balise detection in ETCS/ERTMS context. The present document (deliverable D3.1) is the first report delivered by GATE4Rail WP3. It is specifically dedicated to the definition of a standardised database in order to characterize GNSS in different rail scenarios and the description of Consortium tools included in the GATE4Rail test-bed able to generate local and global hazards and evaluate their impact on GNSS performance