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Belastningsskador hos arbetare är ett ökande problem hos olika företag och det har visat sig finnas en tydlig koppling mellan dessa skador och handledens vinkelhastigheten. Det är därför av stort intresse att kunna mäta dessa vinkelhastigheter på ett noggrant och smidigt sätt. Syftet med denna rapport är att utvärdera precisionen av IMU-sensorers förmåga att beräkna vinkelhastigheten av handleden. Detta görs genom att jämföra data från IMU-sensorer med data från ett optiskt spårningssystem (OTS), vilket klassas som en gold standard inom detta område. Ett experiment bestående av åtta övningar utfördes: tre standard rörelser (flexion och rotation i takterna 40, 90 och 140 slag per minut) och fyra simulerade arbeten (målning, pappersvikning, datorarbete och hårföning). Grad av överensstämmelse ges av 1,96 standardavvikelser (SD) för standardrörelserna (10 deltagare) vilka var -31,8 grader/s och 34,2 grader/s, medan för de simulerade arbetena var det -35,1 grader/s och 34,2 grader/s. Det lägsta medelvärdet av medelkvadratavvikelse (RMSD) var 15,7 grader/s och erhölls vid 40 BPM medan den högsta medelvärdet var 93,9 grader/s och erhölls vid målningsövningen. Medelvärdet av korrelationskoefficienten mellan IMU-sensorer och OTS varierade mellan 0,97 och 0,42 och korrelationskoefficienterna av deltagarnas 50:e percentiler av vinkelhastigheten var 0,95 för standardrörelserna och 0,96 för de simulerade arbetena. Medelvärdet av absoluta differensen mellan sensorer och OTS var givet i percentiler (10:e, 50:e och 90:e). Det största spannet för 50:e percentilen gavs vid 140 BPM (18,3 ± 24,6) och det minsta spannet vid 40 BPM (3,5 ± 4,7). Trots att det fanns mindre differenser mellan metodernas mätningar av vinkelhastighet, anser vi att IMU-sensorer har potential att användas för att mäta vinkelhastigheter hos handledens och med vidare utveckling kan den nuvarande differensen minimeras. / Musculoskeletal disorders (MSDs) are increasingly frequent amongst workers and there is a clear connection between work injuries and wrist angular velocities. One of the biggest issues therefore is the currently limited availability of means to measure these angular velocities. The aim of this study is to validate the usability of the IMU sensors to measure angular velocities. This is done by comparing the data from the IMU:s with the data obtained with the optical motion tracking system (OTS), which is considered gold standard within this field of studies. An experiment consisting of eight exercises was conducted: three standard movements (flexion and rotation in the pace 40, 90 and 140 repetitions per minute) and four simulated practical work tasks (painting, folding paper, computer exercise and using a hairdryer). The limits of agreement for the standard movements (10 subjects) were -31,8 degrees/s and 34,2 degrees/s, whereas for the simulated practical work tasks they were -35,1 degrees/s and 28,2 degrees/s. The lowest mean value of the root mean square deviation (RMSD) value was 15,7 degrees/s which represents the 40 BPM task whilst the highest mean value was 93,9 degrees/s which correspond to the painting task. The mean value of the correlation coefficients between the IMU:s and the OTS ranged between 0,97 and 0,42 and the correlation coefficient between the subjects 50:th percentiles of the angular velocity, was 0,95 for the standard movements whilst for the practical work tasks it was 0,96. The mean value of the absolute difference between the sensors and the OTS was given in percentiles (10th, 50th and 90th). The largest range within the 50th percentile occurred during the 140 BPM task (18,3 ± 24,6) and the smallest range during the 40 BPM task (3,5 ± 4,7). Although the measured angular velocities vary to a certain extent between the two methods, we conclude that the IMU sensors present the potential to work as measuring units for wrist angular velocities and with further development the current differences can be minimized. / Forte dnr: 2017-01209 "Enkel och tideffektiv metod att mät, analysera och presentera biomekaniskbelastning för hand-handled"

This thesis addresses the problem of tracking multiple ground targets whose motion is dependent on one another. Multiple approaches which integrate the social force based motion model into different filtering algorithms are proposed. The social force concept has previously been used to model pedestrian motion where the interactions among pedestrians are described using social forces. First, the social force based motion model integrated into the Probability Hypothesis Density (PHD) framework is proposed. Two different implementations, namely, the Sequential Monte Carlo (SMC) technique and the Gaussian Mixture (GM) technique, are derived to implement the proposed Social Force PHD (SF-PHD) filter in ground target tracking scenarios. Next, a social-force-based motion model integrated into the stacked Kalman filter (stacked SF-KF) is developed and its multiple model (stacked IMM-SF-KF) variant is derived. Then, the assumption used in the proposed algorithms, that the actual values of the social force parameters are known, is not valid at all times and the assumption is relaxed. Hence, simultaneous parameter estimation techniques for the social force parameters during the tracking are proposed. Three approaches based on the state augmentation method, the Expectation Maximization (EM) method and the maximum likelihood method are derived. The maximum likelihood method can be implemented offline or online, depending on the requirement. The traditional Posterior Cramer Rao Lower Bound (PCRLB), which is the inverse of the Fisher information matrix, gives a bound on the optimal achievable accuracy of the estimated state of a target with independent motion. Subsequently, a modified performance measure based on the PCRLB for targets whose motion is dependent on each other is derived to validate the performance of the proposed algorithms. Finally, the PCRLB that accounts for unknown interactions is derived to validate the proposed simultaneous parameter estimation techniques. Simulated and real data are used to show the performance of the proposed algorithms and simultaneous parameter estimation techniques compared to the algorithms in the literature. / Thesis / Doctor of Philosophy (PhD) / This thesis addresses the problem of tracking multiple ground targets whose motion is dependent on one another. In target tracking literature, it is commonly assumed that a target’s motion follows a nearly constant velocity, constant turn or a constant acceleration model independent of the motion of other targets. But the actual behavior of a ground target may be more intricate than that and it is often affected by the motion of other targets, obstacles in the surrounding and its intended destination. Hence, a more sophisticated motion modeling technique, which integrates the various factors that affect the motion of ground targets, is needed. In this thesis, multiple approaches which integrate the social force based motion model into different filtering algorithms are proposed. The social force concept has previously been used to model pedestrian motion where the interactions among pedestrians are described using social forces. First, the social force based motion model integrated into the Probability Hypothesis Density (PHD) framework is proposed. Two different implementations, namely, the Sequential Monte Carlo (SMC) technique and the Gaussian Mixture (GM) technique, are derived to implement the proposed Social Force PHD (SF-PHD) filter in ground target tracking scenarios. Next, a social-force-based motion model integrated into the stacked Kalman filter (stacked SF-KF) is developed and its multiple model (stacked IMM-SF-KF) variant is derived. Then, the assumption used in the proposed algorithms, that the actual values of the social force parameters are known, is not valid at all times and the assumption is relaxed. Hence, simultaneous parameter estimation techniques for the social force parameters during the tracking are proposed. Three approaches based on the state augmentation method, the Expectation Maximization (EM) method and the maximum likelihood method are derived. The maximum likelihood method can be implemented offline or online, depending on the requirement. The traditional Posterior Cramer Rao Lower Bound (PCRLB), which is the inverse of the Fisher information matrix, gives a bound on the optimal achievable accuracy of the estimated state of a target with independent motion. Subsequently, a modified performance measure based on the PCRLB for targets whose motion is dependent on each other is derived to validate the performance of the proposed algorithms. Finally, the PCRLB that accounts for unknown interactions is derived to validate the proposed simultaneous parameter estimation techniques. Simulated and real data are used to show the performance of the proposed algorithms and simultaneous parameter estimation techniques compared to the algorithms in the literature.

This dissertation explores the applicability of RC helicopters as a tool to map wind conditions. This dissertation presents the construction of a robust instrumentation system capable of wireless in-situ measurement and mapping of ship airwake. The presented instrumentation system utilizes an RC helicopter as a carrier platform and uses the helicopter's dynamics for spatial 3D mapping of wind turbulence. The system was tested with a YP676 naval training craft to map ship airwake generated in controlled heading wind conditions. Novel system modeling techniques were developed to estimate the dynamics of an instrumented RC helicopter, in conjunction with onboard sensing, to estimate spatially varying (local) wind conditions. The primary problem addressed in this dissertation is the reliable estimation and separation of pilot induced dynamics from the system measurements, followed by the use of the dynamics residuals/discrepancies to map the wind conditions. This dissertation presents two different modelling approaches to quantify ship airwake using helicopter dynamics. The helicopter systems were characterized using both machine learning and analytical aerodynamic modelling approaches. In the machine learning based approaches, neural networks, along with other models, were trained then assessed in their capability to model dynamics from pilot inputs and other measured helicopter states. The dynamics arising from the wind conditions were fused with the positioning estimates of the helicopter to generate ship airwake maps which were compared against CFD generated airwake patterns. In the analytical modelling based approach, the dynamic response of an RC helicopter to a spatially varying parameterized wind field was modeled using a 30-state nonlinear ordinary differential equation-based dynamic system, while capturing essential elements of the helicopter dynamics. The airwake patterns obtained from both types of approach were compared against anemometrically produced wind maps of turbulent wind conditions artificially generated in a controlled indoor environment. Novel hardware architecture was developed to acquire data critical for the operation and calibration of the proposed system. The mechatronics design of three prototypes of the proposed system were presented and performance evaluated using experimental testing with a modified YP676 naval training vessel in the Chesapeake Bay area. In closing, qualitative analysis of these systems along with potential applications and improvements are discussed to conclude this dissertation. / Ph. D.

<p>Surface electromyography (sEMG) has been used to monitor muscle activity and predict fatigue in the workplaces. However, objectively measuring fatigue is challenging in complex work with unpredictable work cycles, where sEMG may be influenced by the dynamically changing posture demands. The sEMG is affected by various variables and substantial change in mean power frequencies (MPF), and a decline over 8-9% is primarily considered musculoskeletal fatigue. These MPF thresholds have been frequently used, and there were limited efforts to test their appropriateness in determining musculoskeletal fatigue in live workplaces (which predominantly consist of complex tasks). In addition, the techniques that consider both muscular and postural measurements that incorporate dynamic posture changes observed in complex work have not yet been explored. The overall objective of this work is to leverage both postural and muscular cues to identify musculoskeletal fatigue in complex tasks/jobs (i.e., tasks involving different levels of exertions, durations, and postures). The work was completed in two studies.</p> The first study aimed to (1) predict subjective fatigue using objective measurements in non-repetitive tasks, (2) determine whether the musculoskeletal fatigue thresholds in non-repetitive tasks differed from the previously reported threshold, and (3) utilize the empirically calculated thresholds to test their appropriateness in determining musculoskeletal fatigue in live surgical workplaces. The findings showed that the multi-modal measurements indicate better sensitivity than single-modality (sEMG) measurements in detecting decreases in MPF, a predictor of fatigue. In addition, the results showed that the thresholds in dynamic non-repetitive tasks, like surgery, are different than the previously reported 8% threshold. Additionally, implementing muscle-specific thresholds increased the likelihood of more accurately reporting subjective fatigue. The second study aimed to develop a multi-modal fatigue index to detect musculoskeletal fatigue. A controlled laboratory study was performed to simulate the non-repetitive physical demands at different postures. A series of experiments were conducted to test the effectiveness of various metrics/models to identify subjective fatigue in complex tasks. Next, the composite fatigue index (CFI) function was developed using the time-synced integration of both muscular signals (measured with sEMG sensors) and postural signals (measured with Inertial Measurement Unit (IMU) sensors). The variables from sEMG (amplitude, frequency, and the number of muscles showing signs of fatigue) and IMU (the prevalence of static and demanding postures and the number of shoulders in static/demanding posture) sensors were integrated to generate the CFI function. The prevalence of static/demanding postures was developed using the cumulative exposures to static/demanding postures based on the material fatigue failure theory. The single value fatigue index was obtained using the resultant CFI function, which incorporates both muscular and postural variables, to quantify the muscular fatigue in dynamic non-repetitive tasks. The findings suggested that the propagation of musculoskeletal fatigue can be detected using the multi-modal composite fatigue index in complex tasks. The resultant CFI function was then applied to surgery tasks to differentiate the fatigued and non-fatigued groups. The findings showed that the multi-modal fatigue assessment techniques could be utilized to incorporate the muscular and postural measurements to identify fatigue in complex tasks beyond single-modality assessment approaches.

Stroke is the largest cause of severe adult complex disability, caused when the blood supply to the brain is interrupted, either by a clot or a burst blood vessel. It is characterised by deficiencies in movement and balance, changes in sensation, impaired motor control and muscle tone, and bone deformity. Clinically applied stroke management relies heavily on the observational opinion of healthcare workers. Despite the proven validity of a few clinical outcome measures, they remain subjective and inconsistent, and suffer from a lack of standardisation. Motion capture of the upper limb has also been used in specialised laboratories to obtain accurate and objective information, and monitor progress in rehabilitation. However, it is unsuitable in environments that are accessible to stroke patients (for example at patients’ homes or stroke clubs), due to the high cost, special set-up and calibration requirements. The aim of this research project was to validate and assess the sensitivity of a relatively low cost, wearable, compact and easy-to-use monitoring system, which uses inertial sensors in order to obtain detailed analysis of the forearm during simple functional exercises, typically used in rehabilitation. Forearm linear and rotational motion were characterised for certain movements on four healthy subjects and a stroke patient using a motion capture system. This provided accuracy and sensitivity specifications for the wearable monitoring system. With basic signal pre-processing, the wearable system was found to report reliably on acceleration, angular velocity and orientation, with varying degrees of confidence. Integration drift errors in the estimation of linear velocity were unresolved. These errors were not straightforward to eliminate due to the varying position of the sensor accelerometer relative to gravity over time. The cyclic nature of rehabilitation exercises was exploited to improve the reliability of velocity estimation with model-based Kalman filtering, and least squares optimisation techniques. Both signal processing methods resulted in an encouraging reduction of the integration drift in velocity. Improved sensor information could provide a visual display of the movement, or determine kinematic quantities relevant to the exercise performance. Hence, the system could potentially be used to objectively inform patients and physiotherapists about progress, increasing patient motivation and improving consistency in assessment and reporting of outcomes.

<p>The interest in video surveillance has increased in recent years. Cameras are now installed in e.g. stores, arenas and prisons. The video data is analyzed to detect abnormal or undesirable events such as thefts, fights and escapes. At the Informatics Unit at the division of Information Systems, FOI in Linköping, algorithms are developed for automatic detection and tracking of humans in video data. This thesis deals with the target tracking problem when a 3D camera is used. A 3D camera creates images whose pixels represent the ranges to the scene. In recent years, new camera systems have emerged where the range images are delivered at up to video rate (30 Hz). One goal of the thesis is to determine how range data affects the frequency with which the measurement update part of the tracking algorithm must be performed. Performance of the 2D tracker and the 3D tracker are evaluated with both simulated data and measured data from a 3D camera. It is concluded that the errors in the estimated image coordinates are independent of whether range data is available or not. The small angle and the relatively large distance to the target explains the good performance of the 2D tracker. The 3D tracker however shows superior tracking ability (much smaller tracking error) if the comparison is made in the world coordinates.</p>

The interest in video surveillance has increased in recent years. Cameras are now installed in e.g. stores, arenas and prisons. The video data is analyzed to detect abnormal or undesirable events such as thefts, fights and escapes. At the Informatics Unit at the division of Information Systems, FOI in Linköping, algorithms are developed for automatic detection and tracking of humans in video data. This thesis deals with the target tracking problem when a 3D camera is used. A 3D camera creates images whose pixels represent the ranges to the scene. In recent years, new camera systems have emerged where the range images are delivered at up to video rate (30 Hz). One goal of the thesis is to determine how range data affects the frequency with which the measurement update part of the tracking algorithm must be performed. Performance of the 2D tracker and the 3D tracker are evaluated with both simulated data and measured data from a 3D camera. It is concluded that the errors in the estimated image coordinates are independent of whether range data is available or not. The small angle and the relatively large distance to the target explains the good performance of the 2D tracker. The 3D tracker however shows superior tracking ability (much smaller tracking error) if the comparison is made in the world coordinates.

One of the purposes of this study is to develop a vision system consisting of 3 inexpensive, commercial cameras. The system is intended to be used for tracking the motion of objects in a large calibration volume, typically 6.5 m. wide and 0.7 m. high. Hence, a mechanism is designed and constructed for the calibration of the cameras. The second purpose of the study is to develop an algorithm, which can be used to obtain the kinematic data associated with a rigid body, using a vision system. Special filters are implemented in the algorithm to identify the 3 markers attached on the body. Optimal curves are fitted to the position data of the markers after smoothing the data appropriately. The outputs of the algorithm are the position, velocity and acceleration of any point (visible or invisible) on the body and the angular velocity and acceleration of the body. The singularities associated with the algorithm are also determined. Using the vision setup and the developed algorithm for tracking the kinematics of a rigid body, the motions of the saddles of different horses are investigated for different gaits. Similarities and differences between horses and/or gaits are analyzed to lead to quantitative results. Using the limits induced by the whole body vibration of humans, for the first time in the world, daily, allowable riding time and riding distances are determined for different horses and gaits. Furthermore, novel, quantitative horse comfort indicators are proposed. Via the experiments performed, these indicators are shown to be consistent with the comfort assessment of experienced riders. Finally, in order to implement the algorithms proposed in this study, a computer code is developed using MATLAB&reg / .

Traditional methods of input on mobile devices are cumbersome and difficult to use. Devices have become smaller, while their operating systems have become more complex, to the extent that they are approaching the level of functionality found on desktop computer operating systems. The buttons and toggle-sticks currently employed by mobile devices are a relatively poor replacement for the keyboard and mouse style user interfaces used on their desktop computer counterparts. For example, when looking at a screen image on a device, we should be able to move the device to the left to indicate we wish the image to be panned in the same direction. This research investigates a new input model based on the natural hand motions and reactions of users. The model developed by this work uses the generic embedded video cameras available on almost all current-generation mobile devices to determine how the device is being moved and maps this movement to an appropriate action. Surveys using mobile devices were undertaken to determine both the appropriateness and efficacy of such a model as well as to collect the foundational data with which to build the model. Direct mappings between motions and inputs were achieved by analysing users' motions and reactions in response to different tasks. Upon the framework being completed, a proof of concept was created upon the Windows Mobile Platform. This proof of concept leverages both DirectShow and Direct3D to track objects in the video stream, maps these objects to a three-dimensional plane, and determines device movements from this data. This input model holds the promise of being a simpler and more intuitive method for users to interact with their mobile devices, and has the added advantage that no hardware additions or modifications are required the existing mobile devices.