Inertial wearable sensors constitute a booming industry. They are self contained, low powered and highly miniaturized. They allow for remote or self monitoring of health-related parameters. When used to obtain 3-D position, velocity and orientation information, research has shown that it is possible to draw conclusion about issues such as fall risk, Parkinson disease and gait assessment. A key issues in extracting information from accelerometers and gyroscopes is the fusion of their noisy data to allow accurate assessment of the disease. This, so far, is an unsolved problem. Typically, a Kalman filter or its nonlinear, non-Gaussian version are implemented for estimating attitude â?? which in turn is critical for position estimation. However, sampling rates and large state vectors required make them unacceptable for the limited-capacity batteries of low-cost wearable sensors. The low-computation cost complementary filter has recently been re-emerging as the algorithm for attitude estimation. We employ it with a heuristic drift elimination method that is shown to remove, almost entirely, the drift caused by the gyroscope and hence generate a fairly accurate attitude and drift-eliminated position estimate. Inertial sensor data is obtained from the 10-axis SP-10C sensor, attached to a wearable insole that is inserted in the shoe. Data is obtained from walking in a structured indoor environment in Votey Hall.

Les drones ou systèmes de drones aériens jouent un rôle de plus en plus important danstous les domaines, spécialement les drones à décollage et atterrissage verticaux. L’un desplus connus est le Quadrotor et, sans doute, il est la plateforme de recherche la plus utilisée.Cette thèse traite le problème de l’estimation et de la commande d’attitude appliqué àun corps rigide se déplaçant dans l’espace 3D tel que le Quadrotor. La première contributionde cette thèse est la conception et l’implémentation d’une solution d’estimation d’attitude.Celle-ci est basée sur un ensemble de filtres complémentaires combinés avec un algorithmealgébrique tel que TRIAD, QUEST, etc. avec la possibilité de choisir deux formes différentesdes filtres: la première dénommée forme Directe, et la seconde dénommée forme Passive.Les filtres proposés ont une flexibilité dans le choix de l’ordre qui peut être pris grand afinde bien réduire l’effet du bruit de mesure et permettent d’aboutir à un estimateur qui peutprendre en compte le biais éventuel des gyromètres. L’analyse par la théorie de Lyapunovprouve que les erreurs d’estimation tendent globalement et asymptotiquement vers zéro. Unesuite logique de cette première contribution est la proposition d’une solution pour la commanded’attitude qui constitue la deuxième contribution de cette thèse. Elle se traduit par ledéveloppement d’une nouvelle loi de commande d’attitude d’un corps rigide dans l’espace3D, dans laquelle seulement les vecteurs de mesures inertiels avec les mesures des gyromètressont utilisés. Elle utilise le principe de fusion des données à travers un filtre complémentairepermettant l’élimination des bruits des mesures tout en assurant une stabilité presque globalede l’équilibre désiré. La troisième contribution est une loi de commande pour la stabilisationd’attitude sans mesure de vitesse angulaire, ni mesure d’attitude. Pour cela, un systèmelinéaire auxiliaire basé sur les mesures des vecteurs inertiels a été introduit. Ce dernier sesubstitue au manque de l’information de la vitesse angulaire. L’analyse de stabilité du contrôleurproposé est basée sur la théorie de Lyapunov couplée avec le théorème de LaSalle. Ellepermet de conclure sur la stabilité presque globale de l’équilibre désiré. Les performances dessolutions proposées ont été validées par un ensemble de tests expérimentaux / Nowadays, we see a growing popularity of the use of Unmanned Aerial Vehicles (UAV) ofespecially Vertical Take-Off and Landing (VTOL) type. One of the most known VTOL is thequadrotor or Quadcopter which is probably the most used one as a research platform. Thisthesis deal with attitude control and estimation techniques applied to a rigid body movingin 3D space such as Quadcopter VTOL. The first contribution of this thesis is the design ofa new class of complementary linear-like filters allowing the fusion of inertial vector measurementswith angular velocity measurements and combined with algebraic algorithms asTRIAD, QUEST etc. to give an efficient attitude estimation solution. This class of filtersallows several possibilities of implementation such as the order of the filters which can bechosen high in order to reduce more the measurement noise and the form of the filters thatcan be direct or passive and the ability to take into account the possible gyro bias. Lyapunovanalysis shows the global asymptotic convergence of the estimation errors to zero. The sameprinciple of data fusion is used for the proposed new attitude control law in which the complementaryfilters were included to reduce the effect of measurement noise. The obtainedcontroller ensures almost global stability of the desired equilibrium point; it represents thesecond contribution of this thesis. The third contribution takes into consideration an interestingspecial case, where instantaneous measurements of attitude and angular velocity areunavailable. A first order linear auxiliary system based directly on vector measurements isused in an observer-like system to handle the luck of angular velocity. The proposed controllerensures almost global asymptotic stability of the trajectories to the desired equilibriumpoint. Detailed sets of experiments were done to validate the obtained results

An IMU is a sensor with many differing use cases, it makes use of an accelerometer, gyroscope and sometimes a magnetometer. One of the biggest problems with IMU sensors is the effect vibrations can have on their data. The reason for this study is to find a solution to this problem by filtering the data. The tests for this study were conducted in cooperation with Husqvarna using two of their automowers. The tests were made by running the automowers across different surfaces and recording the IMU data. To find filters for the IMU data a comprehensive literature survey was conducted to find suitable methods to filter out vibrations. The two filters selected for further testing were the complementary filter and the LMS filter. When the tests had been run all the data was added to data sheets where it could be analyzed and have the filters added to the data. From the gathered data the data spikes were clearly visible and were more than enough to trigger the mower's emergency stop and need to be manually reset. The vibrations were too irregular to filter using the LMS filter since it requires a known signal to filter against. Hence only the complementary filter was implemented fully. With the complementary filter these vibrations can be minimized and brought well below the level required to trigger an emergency stop. With a high filter weight constant such as 0.98, the margin of error from vibrations can be brought down to +- 1 degrees as the lowest and +- 4,6 degrees as highest depending on the surface and automower under testing. The main advantage with using the complementary filter is that it only requires one weight constant to adjust the filter intensity making it easy to use. The one disadvantage is that the higher the weight constant is the more delay there is on the data.

Hand-intensive work is closely related to the prevalence of upper body and hand/wrist work-related musculoskeletal disorders (WMSDs) in office work, manufacturing, service industries, as well as the healthcare industry. Some risk factors include vibrations, forceful exertions, heavy manual handling, repetitive motions, and prolonged nonneutral wrist postures. To address the growing WMSD epidemic among various occupational groups, simple-to-use exposure measurements are required. However, common quantitative measurement methods for the hand/wrist, such as electrogoniometry and optical motion capture, are both costly and challenging to use. Small, portable inertial measurement units (IMUs) may therefore be considered as a potentially good, affordable wearable option for measuring hand/wrist posture. However, it is difficult to track the position and orientation of a rigid body due to, among other factors, the IMU sensors' sensitivity to ambient magnetic disturbances. As a result, despite advancements in hardware quality, there is still no widely accepted standard for IMU-based motion capture. This study attempted to address this issue by analysing various orientation algorithms to estimate wrist angle from two IMU sensors and compare them to the electrogoniometer-derived measures, i.e., the gold-standard method in field measurements. Five hand-intensive simulated work tasks, each lasting 4–10 minutes, were completed by six participants. These tasks were chosen to resemble some difficult real-world work conditions closely. The wrist posture of the participants was measured using an electrogoniometer and two IMU sensors that were mounted on top of the electrogoniometer's end blocks. The IMU signal of each sensor was processed using seven different orientation algorithms, and the flexion/extension and radial/ulnar deviation angles between them were extracted and compared to the corresponding electrogoniometer angles. For the best-performing orientation algorithm, which was a first-order complementary filter, the mean cross-correlation coefficient between the two measurements was between 0.41 and 0.90 for the flexion/extension and between 0.19 and 0.53 for the radial/ulnar deviation. The mean absolute error (standard deviation) of the best-performing algorithm for the 10th, 50th, and 90th percentile flexion/extension was 8.38 (8.5), 3.99 (3.4), and 11.93 (10) degrees and for the corresponding percentiles of radial/ulnar deviation it was 9.6 (6.5), 5.5 (4.8), and 10.21 (7.1) degrees.  This result can likely be further improved by applying a better orientation algorithm and reducing measurement artifacts such as sensor vibration. However, this experiment demonstrates the potential of IMU-based wrist angle estimation as a simple measurement tool for occupational risk assessment. / Manuellt fysiskt arbete kan orsaka arbetsrelaterade besvär i rörelseorganen i överkropp och hand/handled inom kontorsarbete, tillverkning, industri samt inom hälso- och sjukvårdssektorn. Vibrationer, kraftfulla ansträngningar, tung manuell hantering, upprepade rörelser och långvariga icke-neutrala handledsställningar är några av riskfaktorerna. För att komma till rätta med de arbetsrelaterade besvären bland olika yrkesgrupper, krävs lättanvända exponeringsmätmetoder, eftersom observationsmetoder har en låg tillförlitlighet och de sedvanliga objektiva kvantitativa mätmetoderna för hand/handled, såsom elektrogoniometri och optiska rörelsemätningar, är både dyra och svåra att använda. Små, bärbara s.k. inertial measurement units (IMUs) är därför ett utmärkt, prisvärt och praktiskt alternativ för att mäta hand-/handledsrörelse. Men att estimera positionen och orienteringen av en kroppsdel med hjälp av IMU-sensorer medför stora utmaningar inte minst på grund av sensorernas känslighet för omgivande magnetiska störningar. Trots framsteg i hårdvarukvalitet, finns det fortfarande ingen allmänt accepterad standardmetod för IMU-baserad rörelsemätning. Syftet med det här projektet var att öka kunskapen i det här området genom att analysera olika orienteringsalgoritmer för att uppskatta den absoluta handledsvinkeln från två IMU-sensorer och sedan jämföra den med motsvarande ifrån den etablerade standardmätmetoden med en elektrogoniometer. Fem simulerade handintensiva arbetsuppgifter, var och en mellan 4–10 minuter, genomfördes av sex deltagare. Dessa uppgifter valdes för att härma några arbetsförhållanden som har rapporterats ge risk för besvär. Deltagarnas handledsställning mättes med hjälp av en elektrogoniometer och två IMU-sensorer som monterades ovanpå elektrogoniometers ändblock. IMU-signalen från varje sensor analyserades med sju olika, tidigare framtagna, orienteringsalgoritmer, vinklarna för flexion/extension samt radial/ulnar deviation beräknades och jämfördes sedan med motsvarande elektrogoniometervinklar. För den bäst presterande algoritmen, en första ordningens komplementfilter, varierade den genomsnittliga korrelationskoefficienten mellan 0,41 och 0,90 för flexion/extension och mellan 0,19 och 0,53 för radial/ulnar deviation. Det genomsnittliga absoluta felet (standardavvikelse), för den bäst presterande algoritmen, för den 10:e, 50:e och 90:e percentilen flexion/extension var 8,38 (8,5), 3,99 (3,4) och 11,93 (10) grader. Motsvarande percentiler av radial/ulnar deviation var 9,6 (6,5), 5,5 (4,8) och 10,21 (7,1) grader. Detta resultat kan sannolikt förbättras ytterligare genom att tillämpa en bättre orienteringsalgoritm och minska mätartefakter från sensorvibrationer. Detta experiment visar dock potentialen för IMU-baserad handledsvinkeluppskattning som ett enkelt mätverktyg vid riskbedömningar inom manuella arbeten.