Indoor positioning system for wireless sensor networks

Tese de Doutoramento - Programa Doutoral em Engenharia Electrónica e ComputadoresPositioning technologies are ubiquitous nowadays. From the implementation of the global positioning system (GPS) until now, its evolution, acceptance and spread has been unanimous, due to the underlying advantages the system brings. Currently, these systems are present in many different scenarios, from the home to the movie theatre, at work, during a walk in the park. Many applications provide useful information, based on the current position of the user, in order to provide results of interest. Positioning systems can be implemented in a wide range of contexts: in hospitals to locate equipment and guide patients to the necessary resources, or in public spaces like museums, to guide tourists during visits. They can also be used in a gymnasium to point the user to his next workout machine and, simultaneously, gather information regarding his fitness plan. In a congress or conference, the positioning system can be used to provide information to its participants about the on-going presentations. Devices can also be monitored to prevent thefts. Privacy and security issues are also important in positioning systems. A user might not want to be localized or its location to be known, permanently or during a time interval, in different locations. This information is therefore sensitive to the user and influences directly the acceptance of the system itself. Concerning outdoor systems, GPS is in fact the system of reference. However, this system cannot be used in indoor environment, due to the high attenuation of the satellite signals from non-line-of-sight conditions. Another issue related to GPS is the power consumption. The integration of these devices with wireless sensor networks becomes prohibitive, due to the low power consumption profile associated with devices in this type of networks. As such, this work proposes an indoor positioning system for wireless sensor networks, having in consideration the low energy consumption and low computational capacity profile. The proposed indoor positioning system is composed of two modules: the received signal strength positioning module and the stride and heading positioning module. For the first module, an experimental performance comparison between several received signal strength based algorithms was conducted in order to assess its performance in a predefined indoor environment. Modifications to the algorithm with higher performance were implemented and evaluated, by introducing a model of the effect of the human body in the received signal strength. In the case of the second module, a stride and heading system was proposed, which comprises two subsystems: the stride detection and stride length estimation system to detect strides and infer the travelled distance, and an attitude and heading reference system to provide the full three-dimensional orientation stride-by-stride. The stride detection enabled the identification of the gait cycle and detected strides with an error percentage between 0% and 0.9%. For the stride length estimation two methods were proposed, a simplified method, and an improved method with higher computational requirements than the former. The simplified method estimated the total distance with an error between 6.7% and 7.7% of total travelled distance. The improved method achieved an error between 1.2% and 3.7%. Both the stride detection and the improved stride length estimation methods were compared to other methods in the literature with favourable results. For the second subsystem, this work proposed a quaternion-based complementary filter. A generic formulation allows a simple parameterization of the filter, according to the amount of external influences (accelerations and magnetic interferences) that are expected, depending on the location that the device is to be attached on the human body. The generic formulation enables the inclusion/exclusion of components, thus allowing design choices according to the needs of applications in wireless sensor networks. The proposed method was compared to two other existing solutions in terms of robustness to interferences and execution time, also presenting a favourable outcome.Os sistemas de posicionamento fazem parte do quotidiano. Desde a implementação do sistema GPS (Global Positioning System) até aos dias que correm, a evolução, aceitação e disseminação destes sistemas foi unânime, derivada das vantagens subjacentes da sua utilização. Hoje em dia, eles estão presentes nos mais variados cenários, desde o lar até́ à sala de cinema, no trabalho, num passeio ao ar livre. São várias as aplicações que nos fornecem informação útil, usando como base a descrição da posição atual, de modo a produzir resultados de maior interesse para os utilizadores. Os sistemas de posicionamento podem ser implementados nos mais variados contextos, como por exemplo: nos hospitais, para localizar equipamento e guiar os pacientes aos recursos necessários, ou nas grandes superfícies públicas, como por exemplo museus, para guiar os turistas durante as visitas. Podem ser igualmente utilizados num ginásio para indicar ao utilizador qual a máquina para onde se deve dirigir durante o seu treino e, simultaneamente, obter informação acerca desta mesma máquina. Num congresso ou conferência, o sistema de localização pode ser utilizado para fornecer informação aos seus participantes sobre as apresentações que estão a decorrer no momento. Os dispositivos também podem ser monitorizados para prevenir roubos. Existem também questões de privacidade e segurança associados aos sistemas de posicionamento. Um utilizador poderá não desejar ser localizado ou que a sua localização seja conhecida, permanentemente ou num determinado intervalo de tempo, num ou em vários locais. Esta informação é por isso sensível ao utilizador e influencia diretamente a aceitação do próprio sistema. No que diz respeito aos sistemas utilizados no exterior, o GPS (ou posicionamento por satélite) é de facto o sistema mais utilizado. No entanto, em ambiente interior este sistema não pode ser usado, por causa da grande atenuação dos sinais provenientes dos satélites devido à falta de linha de vista. Um outro problema associado ao recetor GPS está relacionado com as suas características elétricas, nomeadamente os consumos energéticos. A integração destes dispositivos nas redes de sensores sem fios torna-se proibitiva, devido ao perfil de baixo consumo associado a estas redes. Este trabalho propõe um sistema de posicionamento para redes de sensores sem fio em ambiente interior, tendo em conta o perfil de baixo consumo de potência e baixa capacidade de processamento. O sistema proposto é constituído por dois módulos: o modulo de posicionamento por potência de sinal recebido e o módulo de navegação inercial pedestre. Para o primeiro módulo foi feita uma comparação experimental entre vários algoritmos que utilizam a potência do sinal recebido, de modo a avaliar a sua utilização num ambiente interior pré-definido. Ao algoritmo com melhor prestação foram implementadas e testadas modificações, utilizando um modelo do efeito do corpo na potência do sinal recebido. Para o segundo módulo foi proposto um sistema de navegação inercial pedestre. Este sistema é composto por dois subsistemas: o subsistema de deteção de passos e estimação de distância percorrida; e o subsistema de orientação que fornece a direção do movimento do utilizador, passo a passo. O sistema de deteção de passos proposto permite a identificação das fases da marcha, detetando passos com um erro entre 0% e 0.9%. Para o sistema de estimação da distância foram propostos dois métodos: um método simplificado de baixa complexidade e um método melhorado, mas com maiores requisitos computacionais quando comparado com o primeiro. O método simplificado estima a distância total com erros entre 6.7% e 7.7% da distância percorrida. O método melhorado por sua vez alcança erros entre 1.2% e 3.7%. Ambos os sistemas foram comparados com outros sistemas da literatura apresentando resultados favoráveis. Para o sistema de orientação, este trabalho propõe um filtro complementar baseado em quaterniões. É utilizada uma formulação genérica que permite uma parametrização simples do filtro, de acordo com as influências externas (acelerações e interferências magnéticas) que são expectáveis, dependendo da localização onde se pretende colocar o dispositivo no corpo humano. O algoritmo desenvolvido permite a inclusão/exclusão de componentes, permitindo por isso liberdade de escolha para melhor satisfazer as necessidades das aplicações em redes de sensores sem fios. O método proposto foi comparado com outras soluções em termos de robustez a interferências e tempo de execução, apresentando também resultados positivos

Body attenuation and path loss exponent estimation for RSS-based positioning in WSN

The influence of the human body in antenna systems has significant impact in the received signal strength (RSS) of wireless transmissions. Accounting for body effect is generally considered as being able to improve position estimation based on RSS measurements. In this work we perform several experiments with a wireless sensor network, using a sensor node equipped with an inertial measurement unit (IMU), in order to obtain the relative orientation between the sensor node and multiple anchor nodes. A model of the RSS attenuation induced by the body was created using experimental measurements in a controlled environment and applied to a real-time positioning system. A path loss exponent (PLE) estimation method using RSS information from neighbor anchors was also implemented and evaluated. Weighted centroid localization (WCL) algorithm was the positioning method used in this work. When the sensor node was placed on the user’s body, accounting for body effect produced negligible improvements (6%) in the best-case scenario and consistently degraded accuracy under real conditions, whether the node was placed on the user’s body (in the order of 3%), 10 cm away (from 14% to 35%) or 20 cm away from the body (from 42% to 105%) for results in the 70th percentile. The PLE estimation method showed improvements (in the order of 11%) when the sensor node is further away from the body. Results demonstrate that the distance between sensor node and the body has an extremely important influence on the accuracy of the position estimate.This work has been supported by FCT (Fundação para a Ciência e Tecnologia) in the scope of the project UID/EEA/04436/2013. Helder D. Silva is supported by FCT under the grant SFRH/BD/78018/2011info:eu-repo/semantics/publishedVersio

Experimental study on RSS based indoor positioning algorithms

This work compares the performance of indoor positioning systems suitable for low power wireless sensor networks. The research goal is to study positioning techniques that are compatible with real-time positioning in wireless sensor networks, having low-power and low complexity as requirements. Map matching, approximate positioning (weighted centroid) and exact positioning algorithms (least squares) were tested and compared in a small predefined indoor environment. We found that, for our test scenario, weighted centroid algorithms provide better results than map matching. Least squares proved to be completely unreliable when using distances obtained by the one-slope propagation model. Major improvements in the positioning error were found when body influence was removed from the test scenario. The results show that the positioning error can be improved if the body effect in received signal strength is accounted for in the algorithms.Helder D. Silva is supported by the Portuguese Foundation for Science and Technology under the grant SFRBD/78018/2011.info:eu-repo/semantics/publishedVersio

Challenges in characterization of GNSS precise positioning systems for automotive

Autonomous driving is currently one of the main focuses of attention in the automotive industry. A requirement for efficient and safe driving of autonomous vehicles is the ability to precisely pinpoint the location of the vehicle, in the decimeter- to centimeter-level on a global scale. GNSS is expected to play a major role in providing accurate absolute and global positioning, yet many challenges arise in dense urban environments due to lack of line-of-sight to satellites and multi-path, decreasing availability and accuracy. Also, the position accuracy announced by GNSS receiver manufacturers is rather optimistic, typically obtained in best-case scenarios. However, this is rarely encountered in real-world driving conditions, especially in urban areas, leading to a mismatch between receiver specification and real world performance. This paper provides a systematic study regarding the requirements, methods, and solutions available for the characterization/evaluation of a GNSS po- sitioning system in real world driving conditions. An architecture for a precise Automotive Global Reference System (centimeter-level), able to characterize a decimeter-level accuracy GNSS position- ing system in dynamic conditions, is proposed. To the best of authors’ knowledge, such a study is not available in the literature.This work has been supported by: European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project no 037902; Funding Reference: POCI-01-0247-FEDER-037902]

Validation of a knee angle measurement system based on imus

Inertial Measurements Unit (IMU) based systems are a purposeful and alternative tool to monitor human gait mainly because they are cheaper, smaller and can be used without space restrictions compared to other gait analysis methods. In the scientific community, there are well-known studies that test the accuracy and efficiency of this method compared to ground truth systems. Gait parameters such as stride length, distance, velocity, cadence, gait phases duration and detection, or joint angles are tested and validated in these studies in order to study and improve this technology. In this article, knee joint angles were calculated from IMUs’ data and they were compared with DARwIn OP knee joint angles. IMUs were attached to the left leg of the robot and left knee flexion-extension (F-E) was evaluated. The RMSE values were less than 6 when DARwIn OP was walking, and less than 5 when the robot kept the left leg stretched and performed an angle of-30 . ◦ ◦ ◦This work is supported by the FCT Fundação para a Ciência e Tecnologia - with the scholarship reference SFRH/BD/102659/2014, the reference project UID/EEA/04436/2013, by FEDER funds through the COM PETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) - with the reference project POCI-01-0145-FEDER-006941; and the LIACC Project PEst/UID/CEC/00027/2015

Walk distance estimation using an ankle-mounted inertial measurement unit

This work proposes walk distance estimation methods suitable for low power, low computational capability devices, using an ankle-mounted inertial measurement unit. A stride detection method using gyroscope data was implemented, and two stride length estimation methods were developed using the stride cycle information: a simple method, which estimates the leg angle during the forward swing of the leg; and an improved method, which uses the inverted pendulum model to provide the initial conditions for integration of the gyroscope and accelerometer signals in the three-dimensional space. The proposed methods were compared with a two-dimensional stride length estimation method, highlighting the importance of misalignments during sensor placement. Compared to the two-dimensional method, the simple method proposed in this paper achieved approximately the same level of performance with lower computational costs, whereas the three-dimensional method achieved 67% to 78% improvement in performance.This work was supported by FCT with the reference project UID/EEA/04436/2013, COMPETE 2020 with the code POCI-01-0145-FEDER-006941. Helder D. Silva was supported by FCT under the grant SFRBD/78018/2011.info:eu-repo/semantics/publishedVersio

Analysis of postural kinetics data using Artificial Neural Networks in Alzheimer's Disease

Inertial measurement Units (IMU) (accelerometers and gyroscopes), placed in strategic parts of the human body, are a growing field on kinetic posture and imbalance study in Alzheimer’s Disease (AD). On the other hand, Artificial Neural Network (ANN) are a powerful statistical tool used on pattern recognition on big data such as IMU kinetic records. Still, on ANN research, issues like the best number of hidden layers and the best number of neurons in each hidden layer remain open. In our study we developed a software tool of Multilayer Perceptrons ANN analysis (Back Propagation and Scale Gradient Conjugate training algorithms) that automatically tests different configurations for the ANNs on the diagnosis of Alzheimer’s disease. Analysis was performed primarily on all 159 variables, biometrics and IMU records of 21 AD patients and 21 healthy subjects exposed to seven different tasks with increasing postural stress, and posteriorly on selected data derived from MannWhitney analysis. Multilayer Perceptron ANN reached a performance of 95% on the diagnosis of AD, proving to be a potential useful clinical tool

Application of machine learning in postural control kinematics for the diagnosis of Alzheimer’s disease

The use of wearable devices to study gait and postural control is a growing field on neurodegenerative disorders such as Alzheimer's disease (AD). In this paper, we investigate if machine-learning classifiers offer the discriminative power for the diagnosis of AD based on postural control kinematics. We compared Support VectorMachines (SVMs), Multiple Layer Perceptrons (MLPs), Radial Basis Function Neural Networks (RBNs), and Deep Belief Networks (DBNs) on 72 participants (36 AD patients and 36 healthy subjects) exposed to seven increasingly difficult postural tasks. The decisional space was composed of 18 kinematic variables (adjusted for age, education, height, and weight), with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA) score), top ranked in an error incremental analysis. Classification results were based on threefold cross validation of 50 independent and randomized runs sets: training (50%), test (40%), and validation (10%). Having a decisional space relying solely on postural kinematics, accuracy of AD diagnosis ranged from 71.7 to 86.1%. Adding the MoCA variable, the accuracy ranged between 91 and 96.6%. MLP classifier achieved top performance in both decisional spaces. Having comprehended the interdynamic interaction between postural stability and cognitive performance, our results endorse machine-learning models as a useful tool for computer-aided diagnosis of AD based on postural control kinematics.The Algoritmi Center was funded by the FP7 ITN Marie Curie Neural Engineering Transformative Technologies (NETT) projectinfo:eu-repo/semantics/publishedVersio

Characterisation of microbial attack on archaeological bone

As part of an EU funded project to investigate the factors influencing bone preservation in the archaeological record, more than 250 bones from 41 archaeological sites in five countries spanning four climatic regions were studied for diagenetic alteration. Sites were selected to cover a range of environmental conditions and archaeological contexts. Microscopic and physical (mercury intrusion porosimetry) analyses of these bones revealed that the majority (68%) had suffered microbial attack. Furthermore, significant differences were found between animal and human bone in both the state of preservation and the type of microbial attack present. These differences in preservation might result from differences in early taphonomy of the bones. © 2003 Elsevier Science Ltd. All rights reserved