Assessment of Daily Life Mobility Levels using Wearable Inertial Sensors and Minimun Measured Input Models

Tracking activities during daily life and assessing movement parameters is essential for complementing the information gathered in confined environments such as clinical and physical activity laboratories for the assessment of mobility. Inertial measurement units (IMUs) are used as to monitor the motion of human movement for prolonged periods of time and without space limitations. The focus in this study was to provide a robust, low-cost and an unobtrusive solution for evaluating human motion using a single IMU. First part of the study focused on monitoring and classification of the daily life activities. A simple method that analyses the variations in signal was developed to distinguish two types of activity intervals: active and inactive. Neural classifier was used to classify active intervals; the angle with respect to gravity was used to classify inactive intervals. Second part of the study focused on extraction of gait parameters using a single inertial measurement unit (IMU) attached to the pelvis. Two complementary methods were proposed for gait parameters estimation. First method was a wavelet based method developed for the estimation of gait events. Second method was developed for estimating step and stride length during level walking using the estimations of the previous method. A special integration algorithm was extended to operate on each gait cycle using a specially designed Kalman filter. The developed methods were also applied on various scenarios. Activity monitoring method was used in a PRIN’07 project to assess the mobility levels of individuals living in a urban area. The same method was applied on volleyball players to analyze the fitness levels of them by monitoring their daily life activities. The methods proposed in these studies provided a simple, unobtrusive and low-cost solution for monitoring and assessing activities outside of controlled environments.

Gait analysis using a single wearable inertial measurement unit

Procedures for quantitative walking analysis include the assessment of body segment movements within defined gait cycles. Recently, methods to track human body motion using inertial measurement units have been suggested. It is not known if these techniques can be readily transferred to clinical measurement situations. This work investigates the aspects necessary for one inertial measurement unit mounted on the lower back to track orientation, and determine spatio-temporal features of gait outside the confines of a conventional gait laboratory. Apparent limitations of different inertial sensors can be overcome by fusing data using methods such as a Kalman filter. The benefits of optimizing such a filter for the type of motion are unknown. 3D accelerations and 3D angular velocities were collected for 18 healthy subjects while treadmill walking. Optimization of Kalman filter parameters improved pitch and roll angle estimates when compared to angles derived using stereophotogrammetry. A Weighted Fourier Linear Combiner method for estimating 3D orientation angles by constructing an analytical representation of angular velocities and allowing drift free integration is also presented. When tested this method provided accurate estimates of 3D orientation when compared to stereophotogrammetry. Methods to determine spatio-temporal features from lower trunk accelerations generally require knowledge of sensor alignment. A method was developed to estimate the instants of initial and final ground contact from accelerations measured by a waist mounted inertial device without rigorous alignment. A continuous wavelet transform method was used to filter and differentiate the signal and derive estimates of initial and final contact times. The technique was tested with data recorded for both healthy and pathologic (hemiplegia and Parkinson’s disease) subjects and validated using an instrumented mat. The results show that a single inertial measurement unit can assist whole body gait assessment however further investigation is required to understand altered gait timing in some pathological subjects.

Target Tracking in UWB Multistatic Radars

Detection, localization and tracking of non-collaborative objects moving inside an area is of great interest to many surveillance applications. An ultra- wideband (UWB) multistatic radar is considered as a good infrastructure for such anti-intruder systems, due to the high range resolution provided by the UWB impulse-radio and the spatial diversity achieved with a multistatic configuration. Detection of targets, which are typically human beings, is a challenging task due to reflections from unwanted objects in the area, shadowing, antenna cross-talks, low transmit power, and the blind zones arised from intrinsic peculiarities of UWB multistatic radars. Hence, we propose more effective detection, localization, as well as clutter removal techniques for these systems. However, the majority of the thesis effort is devoted to the tracking phase, which is an essential part for improving the localization accuracy, predicting the target position and filling out the missed detections. Since UWB radars are not linear Gaussian systems, the widely used tracking filters, such as the Kalman filter, are not expected to provide a satisfactory performance. Thus, we propose the Bayesian filter as an appropriate candidate for UWB radars. In particular, we develop tracking algorithms based on particle filtering, which is the most common approximation of Bayesian filtering, for both single and multiple target scenarios. Also, we propose some effective detection and tracking algorithms based on image processing tools. We evaluate the performance of our proposed approaches by numerical simulations. Moreover, we provide experimental results by channel measurements for tracking a person walking in an indoor area, with the presence of a significant clutter. We discuss the existing practical issues and address them by proposing more robust algorithms.

Study of 3 Lambda H and 4 Lambda H in the reaction of 6 Li + 12 C at 2 A GeV

Die Produktion von Hyperkernen wurde in peripheren Schwerionenreaktionen untersucht, bei denen eine Kohlenstofffolie mit $^6$Li Projektilen mit einer Strahlenergie von $2 A$~GeV bestrahlt wurde. Es konnten klare Signale f{"{u}}r $Lambda$, $^3_{Lambda}$H, $^4_{Lambda}$H in deren jeweiligen invarianten Massenverteilungen aus Mesonenzerfall beobachtet werden.rnrnIn dieser Arbeit wird eine unabh{"{a}}ngige Datenauswertung vorgelegt, die eine Verifizierung fr"{u}herer Ergebnisse der HypHI Kollaboration zum Ziel hatte. Zu diesem Zweck wurde eine neue Track-Rekonstruktion, basierend auf einem Kalman-Filter-Ansatz, und zwei unterschiedliche Algorithmen zur Rekonstruktion sekund"{a}rer Vertices entwickelt.rn%-Rekonstruktionsalgorithmen .rnrnDie invarianten Massen des $Lambda$-Hyperon und der $^3_{Lambda}$H- und $^4_{Lambda}$H-Hyperkerne wurden mit $1109.6 pm 0.4$, $2981.0 pm 0.3$ und $3898.1 pm 0.7$~MeV$/c^2$ und statistischen Signifikanzen von $9.8sigma$, $12.8sigma$ beziehungsweise $7.3sigma$ bestimmt. Die in dieser Arbeit erhaltenen Ergebnisse stimmen mit der fr{"{u}}heren Auswertung {"{u}}berein.rnrnDas Ausbeutenverh{"{a}}ltnis der beiden Hyperkerne wurde als $N(^3_{Lambda}$H)/$N(^4_{Lambda}$H)$ sim 3$ bestimmt. Das deutet darauf hin, dass der Produktionsmechanismus f{"{u}}r Hyperkerne in Schwerionen-induzierten Reaktionen im Projektil-Rapidit{"{a}}tsbereich nicht allein durch einen Koaleszenzmechanismus beschrieben werden kann, sondern dass auch sekund{"{a}}re Pion-/Kaon-induzierte Reaktionen und Fermi-Aufbruch involviert sind.rn

Studio ed implementazione di algoritmi per la navigazione inerziale di velivoli autonomi ad ala rotante

I sistemi di navigazione inerziale, denominati INS, e quelli di navigazione inerziale assistita, ovvero che sfruttano anche sensori di tipo non inerziale come ad esempio il GPS, denominati in questo caso INS/GPS, hanno visto un forte incremento del loro utilizzo soprattutto negli ultimi anni. I filtri complementari sfruttano segnali in ingresso che presentano caratteristiche complementari in termine di banda. Con questo lavoro di tesi mi sono inserito nel contesto del progetto SHERPA (Smart collaboration between Humans and ground-aErial Robots for imProving rescuing activities in Alpine environments), un progetto europeo, coordinato dall'Università di Bologna, che prevede di mettere a punto una piattaforma robotica in grado di aiutare i soccorritori che operano in ambienti ostili, come quelli del soccorso alpino, le guardie forestali, la protezione civile. In particolare è prevista la possibilità di lanciare i droni direttamente da un elicottero di supporto, per cui potrebbe essere necessario effettuare l'avvio del sistema in volo. Ciò comporta che il sistema di navigazione dovrà essere in grado di convergere allo stato reale del sistema partendo da un grande errore iniziale, dal momento che la fase di inizializzazione funziona bene solo in condizioni di velivolo fermo. Si sono quindi ricercati, in special modo, schemi che garantissero la convergenza globale. Gli algoritmi implementati sono alla base della navigazione inerziale, assistita da GPS ed Optical Flow, della prima piattaforma aerea sviluppata per il progetto SHERPA, soprannominata DreamDroneOne, che include una grande varietà di hardware appositamente studiati per il progetto, come il laser scanner, la camera termica, ecc. Dopo una panoramica dell'architettura del sistema di Guida, Navigazione e Controllo (GNC) in cui mi sono inserito, si danno alcuni cenni sulle diverse terne di riferimento e trasformazioni, si descrivono i diversi sensori utilizzati per la navigazione, si introducono gli AHRS (Attitude Heading Rference System), per la determinazione del solo assetto sfruttando la IMU ed i magnetometri, si analizza l'AHRS basato su Extended Kalman Filter. Si analizzano, di seguito, un algoritmo non lineare per la stima dell'assetto molto recente, e il sistema INS/GPS basato su EKF, si presenta un filtro complementare molto recente per la stima di posizione ed assetto, si presenta un filtro complementare per la stima di posizione e velocità, si analizza inoltre l'uso di un predittore GPS. Infine viene presentata la piattaforma hardware utilizzata per l'implementazione e la validazione, si descrive il processo di prototipazione software nelle sue fasi e si mostrano i risultati sperimentali.

Statistical mapping of real environments with uwb radars

In this master thesis I evaluated the performance of a Ultra-Wide Bandwidth (UWB) radar system for indoor environments mapping. In particular, I used a statistical Bayesian approach which is able to combine all the measurements collected by the radar, including system non-idealities such as the error on the estimated antenna pointing direction or on the estimated radar position. First I verified through simulations that the system was able to provide a sufficiently accurate reconstruction of the surrounding environment despite the limitations imposed by the UWB technology. In fact, the emission of UWB pulses is limited in terms of transmitted power by international regulations. Motivated by the promising results obtained through simulations, I successively carried out a measurement campaign in a real indoor environment using a UWB commercial device. The obtained results showed that the UWB radar system is capable of providing an accurate reconstruction of indoor environments also adopting not directional antennas.

Localizzazione e tracking tramite filtri statistici in sistemi multi antenna

Uno dei temi più recenti nel campo delle telecomunicazioni è l'IoT. Tale termine viene utilizzato per rappresentare uno scenario nel quale non solo le persone, con i propri dispositivi personali, ma anche gli oggetti che le circondano saranno connessi alla rete con lo scopo di scambiarsi informazioni di diversa natura. Il numero sempre più crescente di dispositivi connessi in rete, porterà ad una richiesta maggiore in termini di capacità di canale e velocità di trasmissione. La risposta tecnologica a tali esigenze sarà data dall’avvento del 5G, le cui tecnologie chiave saranno: massive MIMO, small cells e l'utilizzo di onde millimetriche. Nel corso del tempo la crescita delle vendite di smartphone e di dispositivi mobili in grado di sfruttare la localizzazione per ottenere servizi, ha fatto sì che la ricerca in questo campo aumentasse esponenzialmente. L'informazione sulla posizione viene utilizzata infatti in differenti ambiti, si passa dalla tradizionale navigazione verso la meta desiderata al geomarketing, dai servizi legati alle chiamate di emergenza a quelli di logistica indoor per industrie. Data quindi l'importanza del processo di positioning, l'obiettivo di questa tesi è quello di ottenere la stima sulla posizione e sulla traiettoria percorsa da un utente che si muove in un ambiente indoor, sfruttando l'infrastruttura dedicata alla comunicazione che verrà a crearsi con l'avvento del 5G, permettendo quindi un abbattimento dei costi. Per fare ciò è stato implementato un algoritmo basato sui filtri EKF, nel quale il sistema analizzato presenta in ricezione un array di antenne, mentre in trasmissione è stato effettuato un confronto tra due casi: singola antenna ed array. Lo studio di entrambe le situazioni permette di evidenziare, quindi, i vantaggi ottenuti dall’utilizzo di sistemi multi antenna. Inoltre sono stati analizzati altri elementi chiave che determinano la precisione, quali geometria del sistema, posizionamento del ricevitore e frequenza operativa.

Dynamic registration using ultrasound for anatomical referencing

This paper proposes methods to circumvent the need to attach physical markers to bones for anatomical referencing in computer-assisted orthopedic surgery. Using ultrasound, a bone could be non-invasively referenced, and so the problem is formulated as the need for dynamic registration. A method for correspondence establishment is presented, and the matching step is based on three least-squares algorithms: two that are typically used in registration methods such as ICP, and the third is a form of the Unscented Kalman filter that was adapted to work in this context. A simulation was developed in order to reliably evaluate and compare the dynamic registration methods

Multi-node TOA-DOA cooperative LOS-NLOS localization : enabling high accuracy and reliability

This dissertation investigates high performance cooperative localization in wireless environments based on multi-node time-of-arrival (TOA) and direction-of-arrival (DOA) estimations in line-of-sight (LOS) and non-LOS (NLOS) scenarios. Here, two categories of nodes are assumed: base nodes (BNs) and target nodes (TNs). BNs are equipped with antenna arrays and capable of estimating TOA (range) and DOA (angle). TNs are equipped with Omni-directional antennas and communicate with BNs to allow BNs to localize TNs; thus, the proposed localization is maintained by BNs and TNs cooperation. First, a LOS localization method is proposed, which is based on semi-distributed multi-node TOA-DOA fusion. The proposed technique is applicable to mobile ad-hoc networks (MANETs). We assume LOS is available between BNs and TNs. One BN is selected as the reference BN, and other nodes are localized in the coordinates of the reference BN. Each BN can localize TNs located in its coverage area independently. In addition, a TN might be localized by multiple BNs. High performance localization is attainable via multi-node TOA-DOA fusion. The complexity of the semi-distributed multi-node TOA-DOA fusion is low because the total computational load is distributed across all BNs. To evaluate the localization accuracy of the proposed method, we compare the proposed method with global positioning system (GPS) aided TOA (DOA) fusion, which are applicable to MANETs. The comparison criterion is the localization circular error probability (CEP). The results confirm that the proposed method is suitable for moderate scale MANETs, while GPS-aided TOA fusion is suitable for large scale MANETs. Usually, TOA and DOA of TNs are periodically estimated by BNs. Thus, Kalman filter (KF) is integrated with multi-node TOA-DOA fusion to further improve its performance. The integration of KF and multi-node TOA-DOA fusion is compared with extended-KF (EKF) when it is applied to multiple TOA-DOA estimations made by multiple BNs. The comparison depicts that it is stable (no divergence takes place) and its accuracy is slightly lower than that of the EKF, if the EKF converges. However, the EKF may diverge while the integration of KF and multi-node TOA-DOA fusion does not; thus, the reliability of the proposed method is higher. In addition, the computational complexity of the integration of KF and multi-node TOA-DOA fusion is much lower than that of EKF. In wireless environments, LOS might be obstructed. This degrades the localization reliability. Antenna arrays installed at each BN is incorporated to allow each BN to identify NLOS scenarios independently. Here, a single BN measures the phase difference across two antenna elements using a synchronized bi-receiver system, and maps it into wireless channel’s K-factor. The larger K is, the more likely the channel would be a LOS one. Next, the K-factor is incorporated to identify NLOS scenarios. The performance of this system is characterized in terms of probability of LOS and NLOS identification. The latency of the method is small. Finally, a multi-node NLOS identification and localization method is proposed to improve localization reliability. In this case, multiple BNs engage in the process of NLOS identification, shared reflectors determination and localization, and NLOS TN localization. In NLOS scenarios, when there are three or more shared reflectors, those reflectors are localized via DOA fusion, and then a TN is localized via TOA fusion based on the localization of shared reflectors.

Time-variable gravity signal in Greenland revealed by high-low satellite-to-satellite tracking

[1] In the event of a termination of the Gravity Recovery and Climate Experiment (GRACE) mission before the launch of GRACE Follow-On (due for launch in 2017), high-low satellite-to-satellite tracking (hl-SST) will be the only dedicated observing system with global coverage available to measure the time-variable gravity field (TVG) on a monthly or even shorter time scale. Until recently, hl-SST TVG observations were of poor quality and hardly improved the performance of Satellite Laser Ranging observations. To date, they have been of only very limited usefulness to geophysical or environmental investigations. In this paper, we apply a thorough reprocessing strategy and a dedicated Kalman filter to Challenging Minisatellite Payload (CHAMP) data to demonstrate that it is possible to derive the very long-wavelength TVG features down to spatial scales of approximately 2000 km at the annual frequency and for multi-year trends. The results are validated against GRACE data and surface height changes from long-term GPS ground stations in Greenland. We find that the quality of the CHAMP solutions is sufficient to derive long-term trends and annual amplitudes of mass change over Greenland. We conclude that hl-SST is a viable source of information for TVG and can serve to some extent to bridge a possible gap between the end-of-life of GRACE and the availability of GRACE Follow-On.

Evaluating atmospheric methane inversion model results for Pallas, northern Finland

A state-of-the-art inverse model, CarbonTracker Data Assimilation Shell (CTDAS), was used to optimize estimates of methane (CH4) surface fluxes using atmospheric observations of CH4 as a constraint. The model consists of the latest version of the TM5 atmospheric chemistry-transport model and an ensemble Kalman filter based data assimilation system. The model was constrained by atmospheric methane surface concentrations, obtained from the World Data Centre for Greenhouse Gases (WDCGG). Prior methane emissions were specified for five sources: biosphere, anthropogenic, fire, termites and ocean, of which bio-sphere and anthropogenic emissions were optimized. Atmospheric CH 4 mole fractions for 2007 from northern Finland calculated from prior and optimized emissions were compared with observations. It was found that the root mean squared errors of the posterior esti - mates were more than halved. Furthermore, inclusion of NOAA observations of CH 4 from weekly discrete air samples collected at Pallas improved agreement between posterior CH 4 mole fraction estimates and continuous observations, and resulted in reducing optimized biosphere emissions and their uncertainties in northern Finland.

Fine-grained indoor positioning and tracking systems

Indoor positioning has attracted considerable attention for decades due to the increasing demands for location based services. In the past years, although numerous methods have been proposed for indoor positioning, it is still challenging to find a convincing solution that combines high positioning accuracy and ease of deployment. Radio-based indoor positioning has emerged as a dominant method due to its ubiquitousness, especially for WiFi. RSSI (Received Signal Strength Indicator) has been investigated in the area of indoor positioning for decades. However, it is prone to multipath propagation and hence fingerprinting has become the most commonly used method for indoor positioning using RSSI. The drawback of fingerprinting is that it requires intensive labour efforts to calibrate the radio map prior to experiments, which makes the deployment of the positioning system very time consuming. Using time information as another way for radio-based indoor positioning is challenged by time synchronization among anchor nodes and timestamp accuracy. Besides radio-based positioning methods, intensive research has been conducted to make use of inertial sensors for indoor tracking due to the fast developments of smartphones. However, these methods are normally prone to accumulative errors and might not be available for some applications, such as passive positioning. This thesis focuses on network-based indoor positioning and tracking systems, mainly for passive positioning, which does not require the participation of targets in the positioning process. To achieve high positioning accuracy, we work on some information of radio signals from physical-layer processing, such as timestamps and channel information. The contributions in this thesis can be divided into two parts: time-based positioning and channel information based positioning. First, for time-based indoor positioning (especially for narrow-band signals), we address challenges for compensating synchronization offsets among anchor nodes, designing timestamps with high resolution, and developing accurate positioning methods. Second, we work on range-based positioning methods with channel information to passively locate and track WiFi targets. Targeting less efforts for deployment, we work on range-based methods, which require much less calibration efforts than fingerprinting. By designing some novel enhanced methods for both ranging and positioning (including trilateration for stationary targets and particle filter for mobile targets), we are able to locate WiFi targets with high accuracy solely relying on radio signals and our proposed enhanced particle filter significantly outperforms the other commonly used range-based positioning algorithms, e.g., a traditional particle filter, extended Kalman filter and trilateration algorithms. In addition to using radio signals for passive positioning, we propose a second enhanced particle filter for active positioning to fuse inertial sensor and channel information to track indoor targets, which achieves higher tracking accuracy than tracking methods solely relying on either radio signals or inertial sensors.

Detection and Tracking of Traffic Signs Using a Recursive Bayesian Decision Framework

In this paper we propose a new method for the automatic detection and tracking of road traffic signs using an on-board single camera. This method aims to increase the reliability of the detections such that it can boost the performance of any traffic sign recognition scheme. The proposed approach exploits a combination of different features, such as color, appearance, and tracking information. This information is introduced into a recursive Bayesian decision framework, in which prior probabilities are dynamically adapted to tracking results. This decision scheme obtains a number of candidate regions in the image, according to their HS (Hue-Saturation). Finally, a Kalman filter with an adaptive noise tuning provides the required time and spatial coherence to the estimates. Results have shown that the proposed method achieves high detection rates in challenging scenarios, including illumination changes, rapid motion and significant perspective distortion

Development of a grid connected micro wind generator. A practical activity for the course on electric generation with wind energy

This paper describes a practical activity, part of the renewable energy course where the students have to build their own complete wind generation system, including blades, PM-generator, power electronics and control. After connecting the system to the electric grid the system has been tested during real wind scenarios. The paper will describe the electric part of the work surface-mounted permanent magnet machine design criteria as well as the power electronics part for the power control and the grid connection. A Kalman filter is used for the voltage phase estimation and current commands obtained in order to control active and reactive power. The connection to the grid has been done and active and reactive power has been measured in the system.

PDR with a foot-mounted IMU and ramp detection

The localization of persons in indoor environments is nowadays an open problem. There are partial solutions based on the deployment of a network of sensors (Local Positioning Systems or LPS). Other solutions only require the installation of an inertial sensor on the person’s body (Pedestrian Dead-Reckoning or PDR). PDR solutions integrate the signals coming from an Inertial Measurement Unit (IMU), which usually contains 3 accelerometers and 3 gyroscopes. The main problem of PDR is the accumulation of positioning errors due to the drift caused by the noise in the sensors. This paper presents a PDR solution that incorporates a drift correction method based on detecting the access ramps usually found in buildings. The ramp correction method is implemented over a PDR framework that uses an Inertial Navigation algorithm (INS) and an IMU attached to the person’s foot. Unlike other approaches that use external sensors to correct the drift error, we only use one IMU on the foot. To detect a ramp, the slope of the terrain on which the user is walking, and the change in height sensed when moving forward, are estimated from the IMU. After detection, the ramp is checked for association with one of the existing in a database. For each associated ramp, a position correction is fed into the Kalman Filter in order to refine the INS-PDR solution. Drift-free localization is achieved with positioning errors below 2 meters for 1,000-meter-long routes in a building with a few ramps.