Gebhard, Marion
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Item-typ:Veröffentlichung, Multimodal sensor data fusion methods for infrastructureless head-worn interfaces - Sensor systems for robust and adaptive human-robot collaboration(2022-01-31); ; ; Human-robot collaboration is becoming increasingly important especially in the context of rehabilitation robotics where people use robots to regain autonomy. For this purpose, a variety of approaches to control these systems has been developed. A highly intuitive approach is head-motion based control, which enables precise mapping of 3D control commands onto a system via deliberate head movements. This thesis presents a system to ensure the necessary robustness and adaptivity for the control of a robotic system by means of head motion. For that purpose, a lightweight, infrastructureless sensor system was developed that can be worn on the head to fully control a robotic system in all degrees of freedom in Cartesian space. The system is modular in design and data fusion scheme to grant as much adaptivity as possible. The core of the sensor system consists of a Magnetic, Angular Rate, and Gravity (MARG) sensors, which are used to determine the orientation of an object in 3D space. The orientation computation is based on the numerical integration of angular rate measurements from a three-axis gyroscope. Unfortunately Micro-Electromechanical Systems (MEMS) gyroscopes are subject to noise terms that degrade the orientation estimation. To counteract this, MARG sensors are equipped with global reference measurement sensors: an accelerometer and magnetometer. The accelerometer is used to correct orientation in the plane perpendicular to gravity, while the magnetometer is used as an electronic compass to correct the remaining axis. This arrangement enables a globally referenced orientation computation. However, magnetometers are subject to interference, which can completely invalidate its use as a reference measurement. To increase robustness against such disturbances, a data fusion process has been developed which compensates short-term disturbances and allows for simple incorporation of additional references for error correction without further effort. On this basis, a novel approach was developed that uses the physiological coupling of a human’s eyes and head rotation to support the MARG sensor’s orientation determination during long-term magnetic field perturbations. Experimental data demonstrates that this method provides an error reduction of up to 50 percent. The usage of an eye tracker logically opens up the use of visual methods for orientation determination. Therefore, within this thesis an open-source visual Simultaneous Localization And Mapping (SLAM) for RGB-D cameras is integrated into the data fusion process to enable a robust calculation of the head pose in space. The data fusion process is designed to dynamically switch between magnetic, inertial, eye tracking-based and visual reference technologies to enable robust orientation estimation under various perturbations, e.g. gyroscope bias, magnetic disturbances and visual sensor data failure. The combination of these sensors and methods provides the capability, in addition to sensing head rotation only, of precise eye or head gaze vector control to perform accurate positioning of a robot’s End Effector (EEF) in Cartesian space. The work is finalized with a functional verification of the system in a human-robot workplace, which indicates that the sensor system and methods enable a precise control mechanism for robot teleoperation.Dissertation698 517 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Multi-axial analysis of vibration with IMU-based sensors for activity monitoring(2026-03-10); ; Activity monitoring is an essential tool to measure the physical activity of living beings, which is used to evaluate wellbeing, detect dangerous events and observe behavior for research. Recently, vibration-based activity monitoring has gained increased research attention, as its sensing principle enables non-contact monitoring without line-of-sight. The method analyzes activity-induced vibrations of objects in the vicinity of living beings to estimate their physical activity. A promising use case is large-scale animal monitoring in research, where previous methods had limited practical success due to poor scalability or accuracy. However, previous vibration-based activity monitoring methods solely relied on commercial geophones which offer high sensitivity but are unsuited to this use case due to limited bandwidth, a large physical size and a higher cost. This thesis presents a method for vibration-based activity monitoring that utilizes a compact six-axis vibration sensor based on an inertial measurement unit (IMU) to enable monitoring of mice in the home-cage monitoring (HCM) scenario. HCM refers to methods to monitor home-cages, which are transparent containers that are employed at research facilities for long-term housing of rodents. The proposed method measures activity-induced vibrations of the home-cage with a small IMU-based vibration sensor and analyzes the vibration to extract activity-related information and classify physical activity. A major challenge is the low amplitude of activity-induced vibrations, which has resulted in an insufficient signal-to-noise ratio (SNR) in a preliminary study where an IMU was directly attached to a cage for vibration measurement. For this reason, this thesis proposes the novel tuned-beam IMU vibration sensing device that is able to measure activity-induced vibrations with an excellent SNR. The tuned-beam IMU measures vibration in six axes using the accelerometers and gyroscopes of a commercial IMU, and integrates a beam-shaped support structure into the PCB of the sensor. The beam structure is designed to oscillate in resonance with the cage's vibration, which magnifies the measured vibration amplitude and substantially increases the SNR. The beam geometry is optimized in a transient structural finite element analysis (FEA) and finely tuned with an experimental procedure in the assembled state. Additionally, a sensor fusion algorithm is presented that fuses signal components across the IMU's sensors in the wavelet space to reduce sensor noise. It combines correlated signals of accelerometers and gyroscopes that are generated by certain normal modes of the beam structure. Furthermore, a robust classification algorithm is developed that classifies multi-axial vibration sequences of variable length. It analyzes the sequences with multi-level discrete wavelet transformation (MLDWT) to extract sequential time-frequency features. These features are then classified by a convolutional neural network (CNN) -- long short-term memory (LSTM) classification network to predict the activity that had generated the vibration. The network is designed to extract local and long-term patterns of the vibration that are predictive of the activity class. The proposed method is complemented by a camera-based reference system that is used to label the vibration sequences. It uses a commercial behavior analysis software and a custom post-processor that corrects errors which are a result of the challenging home-cage environment. The proposed method is verified in an experimental study where data is collected over a week. The method is able to predict activity with high accuracy and is able to monitor long-term activity with comparable accuracy to the reference method while using low-cost hardware. In summary, the proposed method presents a high-performance cost-efficient method that has a high impact on scalable monitoring methods for the HCM use case. This enables automated evaluation of wellbeing, optimization of caretaking procedures and procurement of unbiased long-term behavioral data for research, which are all in high demand.Dissertation84 43 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, AMiCUS - Bewegungssensor-basiertes Human-Robot Interface zur intuitiven Echtzeit-Steuerung eines Roboterarmes mit Kopfbewegungen(2017-09-14); ; ; Within the work presented here, the demonstrator AMiCUS (Adaptive Head Motion Control for User-friendly Support) has been developed. AMiCUS senses the head motions of a tetraplegic user and uses these to control a robot arm in real-time. At first, a MEMS-based Attitude Heading Reference System (AHRS) suitable for head control has been chosen. Next, a solution has been proposed how to attach the AHRS to the head of a potential user to measure his head motion. A control structure has been developed that enables intuitive real-time control of a robot arm in Cartesian space using the 3 input signals provided by head motion. It has been taken into account that the Range of Motion (ROM) of a potential user can be restricted. Therefore, the control paradigm can be adapted to the individual user, using his full available ROM. Moreover, possibilities to generate control commands which can be integrated consistently into the existing control paradigm have been investigated in order to be able to turn the system on and off, and to perform switching operations using solely head motion. On this basis, the first version of the demonstrator, AMiCUS alpha v.1, has been realized. To allow safe and efficient operation, AMiCUS alpha v.1 provides acoustic and visual feedback to the user. Besides that, special attention has been paid to keeping the complexity of the implemented algorithms low to save resources, such as computing and battery power. The strengths and weaknesses of the system have been assessed during a user study with 25 subjects without motion limitations and 6 tetraplegics with severe motion limitations of the head. Both subjective and objective target quantities have been used. It could be shown that AMiCUS alpha v.1 enables smooth, precise and efficient control of a robot arm to perform simple manipulation tasks. AMiCUS could reliably distinguish between direct control signals and switching commands. Furthermore, no situation has been observed which has put the operational safety to a risk. Overall, user satisfaction was high. However, there were also points of criticism. These have been taken into account during the development of the follow-up version of the demonstrator, AMiCUS alpha v2.0. A tetraplegic exemplarily evaluated AMiCUS alpha v2.0 and compared it to its previous version, AMiCUS alpha v1.0. The direct comparison showed that AMiCUS alpha v2.0 was a subjective improvement compared to AMiCUS alpha v1.0. Most remaining problems, such as difficulties during the imagination of gripper rotations, are likely to be solved by means of learning effects. In a semi-realistic scenario the tetraplegic had the final task to pour water from a bottle into a glass. Like the other tasks, she could also solve this task independently. Overall, the results which could be obtained using the demonstrator AMiCUS are promising. Further development into a stand-alone assistive system or into a complement of a semi-autonomous system is conceivable.Dissertation742 687