Pozicioniranje i praćenje pješaka u zatvorenom prostoru koristeći senzore pametnih telefona, otkrivanje koraka i algoritam za geokodiranje

The paper deals with indoor navigation using inertial sensors (accelerometers, gyroscopes, etc.) built in a smartphone. The main disadvantage of the use of inertial sensors is the accuracy, which rapidly decreases with the increasing time of the measurement. The reason of the deteriorating accuracy is the presence of errors in inertial measurements, which are accumulated in the integration process. The paper describes the determination of a pedestrian trajectory using step detection method, which is improved with utilization of the adaptive step length estimation algorithm. This algorithm reflects the change of the step length with different types of movement. The proposal of the data processing uses information from floormap, what allows the verification of the pedestrian position and detects the collision of the trajectory with the floormap. The proposed algorithm significantly increases the accuracy of the resulting trajectory. Another extension of the proposed algorithm is the implementation of the barometer measurements for determination of the height differences. This fact allows change the floor in a multi-storey buildings. The experimental measurement was realized with a smartphone Samsung Galaxy S4.Rad se bavi navigacijom u zatvorenom prostoru koristeći inercijalne senzore (akcelerometre, žiroskope, itd.) ugrađene u pametne telefone. Najveći nedostatak korištenja inercijalnih senzora je netočnost koja se ubrzano povećava produljenjem vremena mjerenja. Razlog smanjenja točnosti je prisutnost pogrešaka inercijalnih mjerenja koje se akumuliraju kroz proces integracije. Rad opisuje određivanje putanje pješaka koristeći metodu praćenja koraka koja je poboljšana korištenjem algoritma za procjenu prilagodljive duljine koraka. Ovaj algoritam odražava promjene u duljini koraka s različitim vrstama kretanja. Prijedlog obrade podataka koristi informacije iz tlocrta katova što omogućava potvrdu položaja pješaka i otkriva koliziju putanje s tlocrtom. Predloženi algoritam znatno povećava točnost dobivene putanje. Drugi dodatak predloženog algoritma se odnosi na upotrebu barometarskih mjerenja pri određivanju visinskih razlika. Ova činjenica omogućava promjenu kata u višekatnoj zgradi. Eksperimentalno mjerenje je izvršeno uz pomoć pametnog telefona Samsung Galaxy S4

Neck Flexion Angle Estimation during Walking

Neck pain is recently known as the fourth leading cause of disability and the number of patients is apparently increasing. By analyzing the effect of gravitational force on inertial sensor attached to the neck, this study aims to investigate the head flexion posture during walking. The estimated angle is compared with the craniovertebral angle which is measured with an optical tracker. A total of twenty subjects with no history of neck pain or discomfort were examined by walking on the treadmill inside the working range of an optical tracker. In our laboratory settings, the neck flexion angle (NFA) may have a linear relationship with the craniovertebral angle (CVA) in both static case and constant speed walking case. Therefore, inertial sensor, which is lightweight, low cost, and especially free in movement, can be used instead of a camera system. Our proposed estimation method shows its flexibility and gives a result with the mean of absolute error of estimated neck angle varying from 0.48 to 0.58 degrees, which is small enough to use in applications

Cooperative home light: assessment of a security function for the automotive field

Crime and feeling of security are omnipresent and can be influenced by lighting conditions. However, lighting improvements are generally concentrated on street lighting. Meanwhile, a vast variety of new technologies, including innovative lighting systems and connected mobility, are entering into the automotive field. Hence, opportunities are not limited only to provide traffic improvements, entertainment features or driver assistance functions but also measures to tackle (vehicle-related) crime and to increase feeling of security. In this paper, we suggest a security function, namely the cooperative home light (CHL), which makes use of new technologies and has the potential to tackle crime as well as to increase drivers’ feeling of security. We also provide an overview of an implementation. However, because of the underlying challenges, the main focus of this paper is to assess the CHL. Therefore, we introduce our three-steps approach consisting of a transfer of related work, a customer survey and results from our proprietary simulation environment in order to assess the CHL

Classification of physical activity from the embedded smartphone sensors: algorithm development and validation

Physical activity classification has grown in importance lately, for reasons such as positioning or health issues. Given the ubiquity of smartphones and the plethora of sensors they contain, these devices have become an extremely useful tool for this task. In that direction, this project provides an algorithm to count steps using the accelerometer of an Android smartphone. This algorithm searches for patterns in the accelerometer’s signal based on the correlation between consecutive fragments of the signal after a pre-processing step that adapts the data, to count steps under relatively unconstrained ways of carrying the smartphone. The accuracy of the designed algorithm is 92.5% using a database of eleven subjects and four different tests for each subject. As some limitations have been found, a plan for improving the algorithm has been introduced, based on the experience acquired.Clasificar actividad física es cada vez más importante, ya sea para posicionamiento o por problemas de salud. Dada la omnipresencia de los smartphones y el conjunto de sensores que contienen, estos aparatos se han convertido en herramientas verdaderamente útiles para ésta tarea. En esta línea, este proyecto proporciona un algoritmo para contar pasos a partir del acelerómetro de un móvil Android. Este algoritmo busca patrones en el señal de acelerometría basándose en la correlación entre fragmentos consecutivos de señal tras un preprocesado para adaptar los datos; para contar pasos con formas de llevar el móvil poco restrictivas. La precision del algoritmo diseñado es de 92.5% usando una base de datos de once sujetos y cuatro pruebas distintas para cada sujeto. Aunque los resultados no son tan buenos como se pretendía, se han planteado unos posibles pasos para mejorar el algoritmo basados en la experiencia adquirida.Classificar l’activitat física és cada cop més important, ja sigui per posicionament o per problemes de salut. Donada l’omnipresència dels smartphones i el conjunt de sensors que contenen, aquests aparells s’han convertit en eines verdaderament útils per aquesta tasca. En aquesta línia, aquest projecte proporciona un algorisme per comptar passos a partir de l’acceleròmetre d’un mòbil Android. Aquest algorisme busca patrons en el senyal d’accelerometria basant-se en la correlació entre fragments consecutius de senyal després d’un pre-processament per adaptar les dades; per comptar passos amb maneres de portar el mòbil poc restrictives. La precisió de l’algorisme dissenyat és de 92.5% fent servir una base de dades d’onze subjectes i quatre proves diferents per cada subjecte. Com s’han trobat certes limitacions, s’han plantejat uns possibles passos per millorar l’algorisme basats en l’experiència adquirida

A Semantic Web approach to ontology-based system: integrating, sharing and analysing IoT health and fitness data

With the rapid development of fitness industry, Internet of Things (IoT) technology is becoming one of the most popular trends for the health and fitness areas. IoT technologies have revolutionised the fitness and the sport industry by giving users the ability to monitor their health status and keep track of their training sessions. More and more sophisticated wearable devices, fitness trackers, smart watches and health mobile applications will appear in the near future. These systems do collect data non-stop from sensors and upload them to the Cloud. However, from a data-centric perspective the landscape of IoT fitness devices and wellness appliances is characterised by a plethora of representation and serialisation formats. The high heterogeneity of IoT data representations and the lack of common accepted standards, keep data isolated within each single system, preventing users and health professionals from having an integrated view of the various information collected. Moreover, in order to fully exploit the potential of the large amounts of data, it is also necessary to enable advanced analytics over it, thus achieving actionable knowledge. Therefore, due the above situation, the aim of this thesis project is to design and implement an ontology based system to (1) allow data interoperability among heterogeneous IoT fitness and wellness devices, (2) facilitate the integration and the sharing of information and (3) enable advanced analytics over the collected data (Cognitive Computing). The novelty of the proposed solution lies in exploiting Semantic Web technologies to formally describe the meaning of the data collected by the IoT devices and define a common communication strategy for information representation and exchange

Biometric walk recognizer. Research and results on wearable sensor-based gait recognition

Gait is a biometric trait that can allow user authentication, though being classified as a "soft" one due to a certain lack in permanence, and to sensibility to specific conditions. The earliest research relies on computer vision-based approaches, especially applied in video surveillance. More recently, the spread of wearable sensors, especially those embedded in mobile devices, which are able to capture the dynamics of the walking pattern through simpler 1D signals, has spurred a different research line. This capture modality can avoid some problems related to computer vision-based techniques, but suffers from specific limitations. Related research is still in a less advanced phase with respect to other biometric traits. However, the promising results achieved so far, the increasing accuracy of sensors, the ubiquitous presence of mobile devices, and the low cost of related techniques, make this biometrics attractive and suggest to continue the investigations in this field. The first Chapters of this thesis deal with an introduction to biometrics, and more specifically to gait trait. A comprehensive review of technologies, approaches and strategies exploited by gait recognition proposals in the state-of-the-art is also provided. After such introduction, the contributions of this work are presented in details. Summarizing, it improves preceding result achieved during my Master Degree in Computer Science course of Biometrics and extended in my following Master Degree Thesis. The research deals with different strategies, including preprocessing and recognition techniques, applied to the gait biometrics, in order to allow both an automatic recognition and an improvement of the system accuracy

A Wireless Device for Ambulatory Cardiac and Respiratory Monitoring - Design Considerations and Essential Performance

In recent years, utilization of mobile devices for tracking health parameters has increased. These devices are able to monitor different parameters such as heart rate, respiration patterns, amount of activity and energy expenditure. The devices specialized for medical applications provide more accurate measurements, assisting medical decision-makings and diagnosis procedures. This thesis work presents the development of an ambulatory health monitoring system for measuring heart activity, respiration and movement. The developed system consists of a measurement unit, an Android application and a computer software. The measurement device, along with capturing the data from the required sensors, is also able to locally store and/or transmit the data wirelessly to a hand-held device. The designed Android application is responsible for receiving this data, reconstructing it and visualizing it in real-time. The computer software is developed to extract the locally stored information after the recordings. The electronic design of the measurement unit is thoroughly described and the limitations are explored. Additionally, the structure of the implemented embedded software is illustrated and justified. Some brief overview of the structure of the Android application and the computer software is also provided. The signal quality achieved by the system was evaluated and the power consumption was measured for different use cases. Our results showed that the developed system provides a competing signal quality comparing to devices in the market. Additionally, it has been shown that transmitting the data via Bluetooth Smart is less power hungry than storing it to a memory card. The report is finalized by mentioning the challenges faced during the development process