Inertial Motion Capturing : Rigid Body Pose and Posture Estimation with Inertial Sensors

This dissertation is about estimating poses from inertial sensor data, that is estimating orientations and positions. Both poses of single rigid bodies as well as poses of so called skeletons, i.e. systems of jointed rigid bodies, are covered. The key insight into orientation estimation of a single rigid body is to view it as the fusion of sensor data and its dynamics model with prior information. To this end, three different Kalman Filter variations are presented, which fuse the same sensor data and the same dynamics with three different priors. It turns out that the classical model to correct the inclination in an orientation estimator, namely comparing the accelerometer measurement with (negative) gravity, is equivalent to the assumption that the rigid body does not accelerate on long-term average. Assuming that the velocity is zero on long-term average or that the rigid body stays at the same position on long-term average are alternative assumptions and both priors also yield orientation estimators. Moreover, the orientation estimator resulting from the position assumption also estimates a position, which is locally accurate - it follows the accelerometer measurements - but does not drift unboundedly, which it would if the position were obtained by integrating according to the dynamic model only. The focus here is more on the interplay of inertial sensor data and its dynamic model with prior information than it is on practical applications. For instance, for the integrated position to be a usable quantity, the estimate has to be conditioned on the long-term average of the position being zero instead of the velocity or acceleration being zero. In the second, bigger part of this dissertation the posture of a skeleton, i.e. the poses of all the skeleton's bodies, are estimated, again using inertial sensor data only. Notably, no magnetometers are used to recover the rotations around the vertical. Without magnetometers, the rotation of the skeleton as a whole around the vertical, of course, can not be estimated. However, to asses the skeleton's posture, it is also not important. If inertial sensor data of all bodies is fused with the prior information that a skeleton's bodies are jointed using hinges and spherical joints, the relative orientations of the bodies become observable completely: If two accelerometers of two jointed bodies measure the acceleration of a motion, then the relative orientation of those two bodies can be recovered from the directions of the accelerometer measurements, if effects due to movements of the joints are compensated for. The posture estimator that exploits this insight is developed and used in the sensor suit SIRKA, which is workwear with inertial sensors embedded into the clothing. On computationally very limited hardware, which is completely integrated into the suit, the estimator yields posture estimates in real-time. To make this possible, a technique to decouple the sensor's sampling rate from the estimation rate is introduced. Moreover, the sensor orientations and positions inside the suit are almost arbitrary and do not need adjustment. Instead, they are calibrated automatically. The motion capturing workwear is used in a real-world setting, estimating the posture of a worker welding steel on a shipyard. That would not be possible using a motion capturing suit relying on magnetometers

Coordinated Pitch Observation for a Humanoid Robot Soccer Team

Abstract—While the quality of matches between the teams in the RoboCup Standard Platform League has increased a lot, there are still certain situations that prevent the game from progressing. One of the most severe ones is when a team loses track of the ball, because it cannot score goals or prevent the opponent team from scoring goals without knowing where the ball is. In this paper a method is presented to quickly find the ball again by searching the least-recently observed parts of the pitch. A consistent model shared by all robots of the team to identify these parts of the field is explained, as well as the procedure to coordinate the observation among the teammates, such that a varying number of robots can participate in the process. I

Identification of Relevant ICF Categories in Vocational Rehabilitation: A Cross Sectional Study Evaluating the Clinical Perspective

Introduction Vocational rehabilitation (VR) emphasizes a need for medical support, rehabilitation and biopsychosocial approach to enable individuals to successfully participate in the workforce. Optimal rehabilitation management relies on an in-depth knowledge of the typical spectrum of problems encountered of patients in VR. The International Classification of Functioning, Disability and Health (ICF) is based on a universal conceptual model and provides a holistic view of functioning of the lived experience of people such as those undergoing VR. The objectives of this study are to describe the functioning and health of persons undergoing VR and to identify the most common problems around work and in VR using the ICF as the reference framework. Methods An empirical cross-sectional multicenter study was conducted using convenience sampling from March 2009 to March 2010. Data were collected using a Case Record Form rated by health professionals which was based on an extended version of the ICF Checklist containing 292 ICF categories and sociodemographic information. Results 152 patients with various health conditions participated. We identified categories from all four ICF components: 24 for body functions, six for body structures, 45 for activities and participation, and 25 for environmental factors. Conclusions Our study identified a multitude of ICF categories that describe functioning domains and which represent the complexity of VR. Such a comprehensive approach in assessing patients in VR may help to understand and customize the process of VR in the clinical setting and to enhance multidisciplinary communicatio

Flexibility of a Eukaryotic Lipidome – Insights from Yeast Lipidomics

Mass spectrometry-based shotgun lipidomics has enabled the quantitative and comprehensive assessment of cellular lipid compositions. The yeast Saccharomyces cerevisiae has proven to be a particularly valuable experimental system for studying lipid-related cellular processes. Here, by applying our shotgun lipidomics platform, we investigated the influence of a variety of commonly used growth conditions on the yeast lipidome, including glycerophospholipids, triglycerides, ergosterol as well as complex sphingolipids. This extensive dataset allowed for a quantitative description of the intrinsic flexibility of a eukaryotic lipidome, thereby providing new insights into the adjustments of lipid biosynthetic pathways. In addition, we established a baseline for future lipidomic experiments in yeast. Finally, flexibility of lipidomic features is proposed as a new parameter for the description of the physiological state of an organism

Inertial Motion Capturing : Starrkörperposen- und Körperhaltungsschätzung mit Inertialsensoren

This dissertation is about estimating poses from inertial sensor data, that is estimating orientations and positions. Both poses of single rigid bodies as well as poses of so called skeletons, i.e. systems of jointed rigid bodies, are covered. The key insight into orientation estimation of a single rigid body is to view it as the fusion of sensor data and its dynamics model with prior information. To this end, three different Kalman Filter variations are presented, which fuse the same sensor data and the same dynamics with three different priors. It turns out that the classical model to correct the inclination in an orientation estimator, namely comparing the accelerometer measurement with (negative) gravity, is equivalent to the assumption that the rigid body does not accelerate on long-term average. Assuming that the velocity is zero on long-term average or that the rigid body stays at the same position on long-term average are alternative assumptions and both priors also yield orientation estimators. Moreover, the orientation estimator resulting from the position assumption also estimates a position, which is locally accurate - it follows the accelerometer measurements - but does not drift unboundedly, which it would if the position were obtained by integrating according to the dynamic model only. The focus here is more on the interplay of inertial sensor data and its dynamic model with prior information than it is on practical applications. For instance, for the integrated position to be a usable quantity, the estimate has to be conditioned on the long-term average of the position being zero instead of the velocity or acceleration being zero. In the second, bigger part of this dissertation the posture of a skeleton, i.e. the poses of all the skeleton's bodies, are estimated, again using inertial sensor data only. Notably, no magnetometers are used to recover the rotations around the vertical. Without magnetometers, the rotation of the skeleton as a whole around the vertical, of course, can not be estimated. However, to asses the skeleton's posture, it is also not important. If inertial sensor data of all bodies is fused with the prior information that a skeleton's bodies are jointed using hinges and spherical joints, the relative orientations of the bodies become observable completely: If two accelerometers of two jointed bodies measure the acceleration of a motion, then the relative orientation of those two bodies can be recovered from the directions of the accelerometer measurements, if effects due to movements of the joints are compensated for. The posture estimator that exploits this insight is developed and used in the sensor suit SIRKA, which is workwear with inertial sensors embedded into the clothing. On computationally very limited hardware, which is completely integrated into the suit, the estimator yields posture estimates in real-time. To make this possible, a technique to decouple the sensor's sampling rate from the estimation rate is introduced. Moreover, the sensor orientations and positions inside the suit are almost arbitrary and do not need adjustment. Instead, they are calibrated automatically. The motion capturing workwear is used in a real-world setting, estimating the posture of a worker welding steel on a shipyard. That would not be possible using a motion capturing suit relying on magnetometers

Effects of self-monitored jogging on physical fitness, blood pressure and serum lipids: a controlled study in sedentary middle-aged men

To study the effects of long-term, home-based exercise on physical fitness and cardiovascular risk factors of middle-aged nonsmoking males, a controlled study was conducted in 61 sedentary Swiss men. Thirty-nine men were randomly allocated to jog 2 h/week for 4 months on an individually prescribed and heart-rate-controlled basis, whereas 22 men served as controls. Despite varying adherence to the exercise regimen, the 4-month net change (effect in exercise group minus effect in control group) in estimated endurance capacity was significant and positive. Net changes in arterial blood pressure, measured with a random-zero device, were nonsignificant, but after exclusion of low-normotensive men (n = 19) from analysis, a significant net effect of exercise on diastolic blood pressure was seen (-4.3 mmHg; p = .048). The following net changes in serum lipid levels occurred: HDL cholesterol + 0.12 mmol/l (p = .028), total triglycerides -0.21 mmol/l (ns), HDL-C/total cholesterol ratio +0.02 (p = .047). Exploratory analyses revealed that an increase in estimated endurance capacity was associated with a rise in systolic and diastolic blood pressure (r = 0.49 and 0.43, respectively; p less than 0.01 both). Changes in the waist-hip ratio were directly related to the change in diastolic blood pressure (r = 0.27; p less than 0.05). Multivariable analysis indicated that much of the beneficial effect of exercise on diastolic blood pressure was apparently mediated through a decrease in body fat. This study confirms that individually prescribed jogging can reduce cardiovascular risk factors in self-selected nonsmoking males.(ABSTRACT TRUNCATED AT 250 WORDS

Evaluation of data completeness in the electronic health record for the purpose of patient recruitment into clinical trials: a retrospective analysis of element presence

Background Computerized clinical trial recruitment support is one promising field for the application of routine care data for clinical research. The primary task here is to compare the eligibility criteria defined in trial protocols with patient data contained in the electronic health record (EHR). To avoid the implementation of different patient definitions in multi-site trials, all participating research sites should use similar patient data from the EHR. Knowledge of the EHR data elements which are commonly available from most EHRs is required to be able to define a common set of criteria. The objective of this research is to determine for five tertiary care providers the extent of available data compared with the eligibility criteria of randomly selected clinical trials. Methods Each participating study site selected three clinical trials at random. All eligibility criteria sentences were broken up into independent patient characteristics, which were then assigned to one of the 27 semantic categories for eligibility criteria developed by Luo et al. We report on the fraction of patient characteristics with corresponding structured data elements in the EHR and on the fraction of patients with available data for these elements. The completeness of EHR data for the purpose of patient recruitment is calculated for each semantic group. Results 351 eligibility criteria from 15 clinical trials contained 706 patient characteristics. In average, 55% of these characteristics could be documented in the EHR. Clinical data was available for 64% of all patients, if corresponding data elements were available. The total completeness of EHR data for recruitment purposes is 35%. The best performing semantic groups were ‘age’ (89%), ‘gender’ (89%), ‘addictive behaviour’ (74%), ‘disease, symptom and sign’ (64%) and ‘organ or tissue status’ (61%). No data was available for 6 semantic groups. Conclusions There exists a significant gap in structure and content between data documented during patient care and data required for patient eligibility assessment. Nevertheless, EHR data on age and gender of the patient, as well as selected information on his disease can be complete enough to allow for an effective support of the manual screening process with an intelligent preselection of patients and patient data