INTRODUCTION: Low back pain (LBP) is often exacerbated by movement and, during functional movements, people with LBP tend to inherently modify their posture and movement patterns to avoid aggravating their pain further. Therefore, analysis of spinal movement in people with LBP is necessary and may lead to the identification of movement-based clusters for optimal treatment. PURPOSE: The overall purpose of this study was to investigate how movement patterns in people with chronic LBP relate to their subjective level of pain-related disability. First, we determined which spinal movements best discriminate between people with chronic LBP and asymptomatic controls; second, we ascertained whether the level of movement dysfunction in people with chronic LBP correlates with the Oswestry Disability Index (ODI). METHODS: 44 volunteers (26 with current chronic LBP, 15 asymptomatic controls) participated in this study. An OPAL (APDM Wearable Technology) inertial measurement unit (IMU) sensor was attached to the skin over the spinal region of L3-L4, which allowed for transmission of motion data from the lumbar spine. Participants were instructed to perform a series of eleven uniplanar and functional multiplanar spinal movements at a comfortable pace. The median peak angular velocity values from the lumbar spine (measured by the gyroscope component of the IMU) were extracted from MATLAB and Python, and all statistical analyses were subsequently performed in RStudio. RESULTS: From a mixed-direction stepwise regression based on the minimization of the Aikake Information Criterion (AIC), a binomial logistic regression model containing kinematic data from Flexion, Extension-Twist, Axial Rotation, and Sit-to-Stand tasks was selected as the best model to predict chronic LBP status (McFadden’s pseudo-R-squared: 0.310). Moreover, compared to sagittal uniplanar tasks, functional multiplanar movements were better at predicting LBP status (AICmulti-uni = −3.2). Lastly, we found no association between the kinematic data from the movements that best predicted LBP status and ODI scores (p-value = 0.405). CONCLUSION: In this study, we found no correlation between self-reported disability and kinematic median peak angular velocity data from Flexion, Extension-Twist, Axial Rotation, and Sit-to-Stand tasks. Therefore, other factors that may modulate physical function, such as psychosocial factors, should be investigated.

Yes / Normal ankle function provides a key contribution to everyday activities, particularly step/stair ascent and descent, where many falls occur. The rising to up-on-the-toes (UTT) 30 second test (UTT-30) is used in the clinical assessment of ankle muscle strength/function and endurance and is typically assessed by an observer counting the UTT movement completed. The aims of this study are: (i) to determine whether inertial measurement units (IMUs) provide valid assessment of the UTT-30 by comparing IMU-derived metrics with those from a force-platform (FP), and (ii) to de-scribe how IMUs can be used to provide valid assessment of the movement dynamics/stability when performing a single UTT movement that is held for 5 s (UTT-stand). Twenty adults (26.2 ± 7.7 years) performed a UTT-30 and a UTT-stand on a force-platform with IMUs attached to each foot and the lumbar spine. We evaluate the agreement/association between IMU measures and measures de-termined from the FP. For UTT-30, IMU analysis of peaks in plantarflexion velocity and in FP’s centre of pressure (CoP) velocity was used to identify each repeated UTT movement and provided an objective means to discount any UTT movements that were not completed ‘fully’. UTT movements that were deemed to have not been completed ‘fully’ were those that yielded peak plantarflexion and CoP velocity values during the period of rising to up-on-the-toes that were below 1 SD of each participant’s mean peak rising velocity across their repeated UTT. The number of UTT movements detected by the IMU approach (23.5) agreed with the number determined by the FP (23.6), and each approach determined the same number of ‘fully’ completed movements (IMU, 19.9; FP, 19.7). For UTT-stand, IMU-derived movement dynamics/postural stability were moderately-to-strongly correlated with measures derived from the FP. Our findings highlight that the use of IMUs can provide valid assessment of UTT test(s).

Detta projekt har framtagit ett program för att kartlägga vadmusklers aktivering. Med hjälp av IMU i kombination med EMG kan programmet dra slutsatser om löpares muskelaktivering med olika skor. I studier där muskelaktivitet mättes med hjälp av stationära tryckplattor uppkom det ett behov av ett program som kunde bearbeta båda typer av data samtidigt. Därför har detta projekt tagit fram ett program som använder EMG- och IMU-data för att analysera hur individers prestation påverkas av skorna de använder. Barfotalöpning användes som referens för att kunna avgöra hur ansträngning skiljer sig mellan olika skor. Resultatet blir en procentskala där barfota är 100 % ansträngning och prestationen med olika skor kan ligga över och under barfotalöpnings aktiveringsnivå. Med det nya programmet ska forskare kunna förstå påverkan som löpares skoval har på ansträngning. / This project has constructed a program for mapping calf muscles activation. With the help of IMU in combination with EMG the program can draw conclusions regarding runners’ performances with different shoes. In studies where muscles activity was measured with the help of stationary pressure plates there arose a need for a program that could process both kinds of data simultaneously. Therefore, this Project presents a program that uses EMG and IMU data to analyse an individual’s performance based on the wearers’ shoes. Barefoot running was used as a reference to determine the level of effort with different shoes. The result is expressed as a precent scale where barefoot is 100% effort and the performance with different shoes can be above or below barefoot activation level. With the new program, scientists could understand the correlation between runners’ shoe choice and their efforts.

Recent studies show that deep learning can be employed to learn from sensor data to improve accuracy and robustness of sensor fusion algorithms. In the same vein, in this thesis we use a state-of-the-art temporal convolution network to predict zero velocity updates (ZUPT) from raw inertial measurement unit (IMU) signals, and use the network output to improve the performance of an optimization-based pose estimator. Experiments were conducted on publicly available datasets, and results show that (i) the network can distinguish a car in motion vs. a car standing still by observing an IMU signal, and (ii) that ZUPT detection enhances the observability of states in the optimization-based pose estimation, thus reducing local drift. / Nyligen gjorda studier visar att djupinlärning kan användas för att lära av sensordata för att förbättra noggrannhet och robusthet hos sensorfusionsalgoritmer. På samma sätt använder vi i denna avhandling en tidsberoende faltnings neuronnätsmodell (TCN) för att detektera om ett fordon står stilla även kallat zero velocity updates (ZUPT) från IMU rå- data och använder neuronnätsprediktionen för att förbättra prestandan hos en optimeringsbaserad positionsuppskattning. Experiment utfördes på allmänt publicerade datamängder, och resultaten visar att (i) neuronnätsmodellen kan läras till att urskilja en bil i rörelse kontra en bil som står stilla genom att observera en IMU- signal, och (ii) att ZUPT- detektering förbättrar observerbarheten för tillstånd i den optimeringsbaserade positioneringsuppskattningen, vilket minskar lokal drift.

Detta arbete har syftat till att skapa ett digitaliserat hjälpmedel till jägare och skyttar, för att assistera innan avfyrning och ge information kring hårda stötar mot vapnet. Kikarsikten är ömtåliga och om de skadas kan det begränsa precisionen hos siktet. Detta i sin tur begränsar skyttens förmåga att utföra sitt arbete korrekt. För att få en förståelse om de krafter som påverkar ett vapen har rekyl- och kollisionstester genomförts under olika förhållanden. Utifrån värden från dessa tester modifieras mjukvaran för att prototypen skall utföra sina funktioner så effektivt som möjligt. Prototypen som tagits fram i detta arbete är uppbyggd utav tröghetssensorer från Analog Devices som erbjuder funktioner för att mäta vinklingen samt kollisioner på ett skjutvapen. / This work has aimed to create a digital aid for hunters and shooters, both to assist before firing and provide information regarding impacts on the rifle. Riflescopes are precise and quite fragile instruments. If these are damaged, the shooter's ability is greatly hindered. The prototype developed is a build of inertial sensors from Analog Devices that offer exceptional functionality to assist the user in the aforementioned weaknesses. Recoil and collision tests are some of the tests performed during this project. The outcome of the tests is to provide a fundamental understanding of the forces that can affect a rifle during different conditions. Based on the data from these tests, the software is modified so the prototype can perform its tasks as efficiently as possible.

This paper describes, the development of a sensor fusion algorithm-based Kalman lter ar- chitecture, in combination with a low cost Inertial Measurement Unit (IMU) for an Attitude Heading Reference System (AHRS). A low cost IMU takes advantage of the use of MEMS technology enabling cheap, compact, low grade sensors. The use of low cost IMUs is primar- ily targeted towards Unmanned Aerial Vehicle (UAV) applications due to the requirements for small package size, light weight, and low energy consumption. The high dynamics nature of smaller airframes, coupled with the typical vibration induced noise of UAVs require an e cient, reliable, and robust AHRS for vehicle control. To eliminate the singularities at 90 on the pitch and roll axes, and to keep the computational e ciency high, quaternions are used for state attitude representation.

Because of the limitation of GPS indoors there is a demand for alternative methods to accurately determine both position and orientation. Previous attempts at positional tracking has required an infrastructure of hardware and sensors to provide the path of an object or person. This is not a mobile solution to a mobile problem. This project aims to answer the question if it is possible to use an Inertial measurement unit sensor for this application. It will also create a prototype device that will demonstrate the capabilities of the proposed method. The goal of the project is to reach an accuracy of ±20 cm for position and ±5 degrees for rotation. A Kalman filter will be used to filter the output from the sensor in order to get more stable and accurate readings. The results show that it is possible to determine position of ±20 cm up to 100 cm with the proposed method. An inertial measurement unit is capable of measuring rotation accuracy of ±5 degrees and a prototype has been designed and manufactured to demonstrate the method.

<p>This thesis deals with the problem of detecting faults in an environment where the measurements are affected by additive noise. To do this, a residual sensitive to faults is derived and statistical methods are used to distinguish faults from noise. Standard methods for fault detection compare a batch of data with a model of the system using the generalized likelihood ratio. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. One method to handle this is the parity-space method which solves the problem by removing the influence of the initial state using a projection.</p><p>In this thesis, the case where prior knowledge about the initial state is available is treated. This can be obtained for example from a Kalman filter. Combining the prior estimate with a minimum variance estimate from the data batch results in a smoothed estimate. The influence of the estimated initial state is then removed. It is also shown that removing the influence of the initial state by an estimate from the data batch will result in the parity-space method. To model slowly changing faults, an efficient parameterization using Chebyshev polynomials is given.</p><p>The methods described above have been applied to an Inertial Measurement Unit, IMU. The IMU usually consists of accelerometers and gyroscopes, but has in this work been extended with a magnetometer. Traditionally, the IMU has been used to estimate position and orientation of airplanes, missiles etc. Recently, the size and cost has decreased making it possible to use IMU:s for applications such as augmented reality and body motion analysis. Since a magnetometer is very sensitive to disturbances from metal, such disturbances have to be detected. Detection of the disturbances makes compensation possible. Another topic covered is the fundamental question of observability for fault inputs. Given a fixed or linearly growing fault, conditions for observability are given.</p><p>The measurements from the IMU show that the noise distribution of the sensors can be well approximated with white Gaussian noise. This gives good correspondence between practical and theoretical results when the sensor is kept at rest. The disturbances for the IMU can be approximated using smooth functions with respect to time. Low rank parameterizations can therefore be used to describe the disturbances. The results show that the use of smoothing to obtain the initial state estimate and parameterization of the disturbances improves the detection performance drastically.</p>

This thesis deals with the problem of detecting faults in an environment where the measurements are affected by additive noise. To do this, a residual sensitive to faults is derived and statistical methods are used to distinguish faults from noise. Standard methods for fault detection compare a batch of data with a model of the system using the generalized likelihood ratio. Careful treatment of the initial state of the model is quite important, in particular for short batch sizes. One method to handle this is the parity-space method which solves the problem by removing the influence of the initial state using a projection. In this thesis, the case where prior knowledge about the initial state is available is treated. This can be obtained for example from a Kalman filter. Combining the prior estimate with a minimum variance estimate from the data batch results in a smoothed estimate. The influence of the estimated initial state is then removed. It is also shown that removing the influence of the initial state by an estimate from the data batch will result in the parity-space method. To model slowly changing faults, an efficient parameterization using Chebyshev polynomials is given. The methods described above have been applied to an Inertial Measurement Unit, IMU. The IMU usually consists of accelerometers and gyroscopes, but has in this work been extended with a magnetometer. Traditionally, the IMU has been used to estimate position and orientation of airplanes, missiles etc. Recently, the size and cost has decreased making it possible to use IMU:s for applications such as augmented reality and body motion analysis. Since a magnetometer is very sensitive to disturbances from metal, such disturbances have to be detected. Detection of the disturbances makes compensation possible. Another topic covered is the fundamental question of observability for fault inputs. Given a fixed or linearly growing fault, conditions for observability are given. The measurements from the IMU show that the noise distribution of the sensors can be well approximated with white Gaussian noise. This gives good correspondence between practical and theoretical results when the sensor is kept at rest. The disturbances for the IMU can be approximated using smooth functions with respect to time. Low rank parameterizations can therefore be used to describe the disturbances. The results show that the use of smoothing to obtain the initial state estimate and parameterization of the disturbances improves the detection performance drastically.

Reliability is one of the most critical performance measures of guided missile systems. It is directly related to missile mission success. In order to have a high reliability value, reliability analysis should be carried out at all phases of the system design. Carrying out reliability analysis at all the phases of system design helps the designer to make reliability related design decisions in time and update the system design. In this study, reliability analysis process performed during the conceptual design phase of a Medium Range Anti-Tank Missile System Inertial Measurement Unit (IMU) was introduced. From the reliability requirement desired for the system, an expected IMU reliability value was derived by using reliability allocation methods. Then, reliability prediction for the IMU was calculated by using Relex Software. After that, allocated and predicted reliability values of the IMU were compared. It was seen that the predicted reliability value of the IMU did not meet the required reliability value. Therefore, reliability improvement analysis was carried out.