Touchscreens are increasingly being integrated into modern aircraft cockpits due to their space-efficiency and adaptability. However, operating touchscreens in turbulent conditions reduces input accuracy and alters the applied force. These issues are caused by involuntary limb movements induced by external accelerations such as turbulence, called biodynamic feedthrough. Understanding and mitigating this phenomenon is crucial to be able to provide consistent haptic feedback on touchscreens. This study investigates the dynamics of biodynamic feedthrough into the applied normal force on the screen, by conducting a human-in-the-loop experiment with fifteen participants. In the experiment, participants performed both stationary and dynamic position tracking tasks on a touchscreen, while being exposed to turbulence in all three linear motion directions. It was observed that on average 63% higher normal forces were applied during stationary tasks than during dynamic tasks, likely resulting in increased arm co-contraction. Additionally, a second-order mass-spring-damper model was fitted to the obtained force data. It was found that biodynamic feedthrough into the applied normal force has the highest feedthrough levels when the direction of turbulence aligns with the direction of the applied force, and that up to 90% of the disturbance-induced normal force can be described by the proposed individualized models. The results also show the variability of dynamics between participants and therefore the importance of individualized models. These findings provide insights for future developments in adaptive haptic feedback for touchscreens, aiming to enhance the reliability and safety of touchscreen interactions in turbulent environments.

Virtual training environments are powerful tools for various applications. To increase the immersion and realism of these environments, several devices have been developed capable of rendering a wide range of haptic sensations. As humans mainly interact with objects using their hands, the main focus of research has been on developing devices capable of providing feedback to the hand and fingers. Currently, most of these devices only provide one form of feedback, either kinesthetic feedback to finger posture or tactile sensations in the form of pressing or skin stretching. These devices seldom provide more than one kind of feedback while remaining compact enough to be wearable with a natural range of motion of the hand. In this work, we present a device that combines an existing kinesthetic feedback glove with a novel tactile fingertip display. The haptic display consists of a cable-driven platform, powered by a stand-alone actuation module. It has been designed to be compact enough for a near-natural range of motion, with the possibility of multi-finger applications in mind. To validate the design, we conducted an experiment with 16 healthy young par- ticipants who were asked to reproduce virtual shapes with their index fingers after exploring those shapes under two different conditions: 1) with purely kinesthetic feedback, and 2) with kinesthetic feedback and tactile feedback. The outcome metrics were the exploration time, reproduction time, and reproduction error. For each testing condition, the workload and motivation of the participants were also evaluated. We found that participants had a lower reproduction error and used more time to reproduce the shapes with the novel haptic display enabled compared to purely kinesthetic feedback. We did not find significant differences in exploration time, workload, or motivation between testing conditions. Thus, the combined feedback provided by our novel device leads to better performance in shape reproduction compared to only kinesthetic feedback. Further, our device is lightweight and compact, potentially enabling multi-finger use which may lead to even greater performance and immersion in virtual object manipulation tasks.

Shoulder injuries, prevalent worldwide, often occur from ageing and accidents. In Western countries, these injuries primarily afflict the elderly population, while in rural regions of Bangladesh, Iran, India, and Pakistan, they affect younger individuals who are often the family's primary earners. Due to that, preventing and aiding the recovery of shoulder injuries is crucial. To address this, strain maps with vibrotactile feedback, emerge as a promising solution. However, the feedback system must be affordable, compact, comfortable, user-friendly, easily understood, and portable to suit the local environment. Vibrotactile feedback appears promising but can distract the user from work. Hence, this study seeks to investigate if vibrotactile feedback can be paired with strain maps to guide users in maintaining healthy postures and reducing the risk of shoulder injuries in rural areas, where visual feedback is used as a benchmark. To provide feedback using strain maps, shoulder angles are determined using Python's OpenCV and MediaPipe libraries. PyGame is utilized to display the strain maps, and OpenCV helps delineate boundaries between regions of high and low strain within the shoulder. Visual feedback is integrated into the strain map display, while vibrotactile feedback is delivered through a wearable haptic device. Despite challenges related to axial rotation accuracy and the camera-dependent nature of shoulder angle measurements, user experiments, conducted independently for shoulder elevation and planar elevation, reveal that vibrotactile feedback shows better performance compared to visual feedback. Consequently, this study concludes that vibrotactile feedback has the potential to prevent shoulder injuries with strain maps, but also still needs to improve for future work.

Indoor positioning systems cannot rely on conventional localization methods, such as GPS, to locate devices because of interference with the structure of buildings. One solution is to use magnetic positioning, which is based on spatial variations in the patterns of the ambient magnetic field. To model magnetic fields, Gaussian process regression is used, providing predictions of the magnetic field at unvisited locations along with uncertainty quantification. These predictions and their uncertainties are valuable information for probabilistic localization algorithms used for magnetic positioning. Full Gaussian process regression has poor scalability, becoming computationally intractable from roughly 10,000 one-dimensional measurements due to its associated cubic computational complexity. In the existing literature, approximations for Gaussian process regression have been extensively studied to reduce this computational complexity. Of these approximations, only approximations involving basis functions and local experts have been used in the context of scalable magnetic field modeling. A favorable approximation framework from existing literature uses structured kernel interpolation (SKI), allowing for fast regression through efficient Krylov subspace methods. The SKI framework is favorable as it allows for fast regression in low dimensions without introducing boundary effects. In this thesis, the SKI framework is used to approximate two distinct magnetic field models: the shared model, which considers independence between the magnetic field components with shared hyperparameters, and the scalar potential model, which includes physical properties of the magnetic field (Maxwell’s equations) in the model. The scalability of the approach is shown using simulations and experiments with magnetic field measurements. Through the simulations, it is shown the SKI framework accurately and efficiently approximates the models. The applicability of the SKI framework for scalable magnetic field modeling is investigated using data collected using a motion capture suit, the Xsens MVN Link Suit. In the final experiment, a magnetic field map is constructed based on more than 40,000 three-dimensional measurements without splitting the data set, which took less than one minute on a standard laptop.

Meditation is a contemplative practice that is believed to entail attentional and emotional regulation. One of the biggest challenges in developing personalized, accessible healthcare options with meditation is finding understandable features that signify whether someone is meditating. Specifically, there is no consensus on a feature resulting from the electroencephalogram (EEG) in the current body of literature on meditation.

In this thesis, I propose a dynamic systems analysis on EEG data to obtain a dynamic feature capable of distinguishing meditation from an eyes-closed resting baseline. I gathered the EEG data at TNO (Dutch Organisation for Applied Scientific Research) from twenty-two participants during a sixteen-minute loving-kindness meditation and two two-minute baselines. The proposed methodology characterizes temporal and spatial characteristics of the EEG simultaneously by approximating the EEG dynamics with a linear model on short time windows. I assess changes among three features: the frequency and magnitude of the oscillatory dynamics and the corresponding active electrodes.

The analysis can identify changes in EEG dynamics for each individual. Across all participants, regions associated with vision and language processing were active throughout the experiment. Notably, attention-related regions were more involved during meditation than rest. Moreover, the results show a shift in active regions throughout the meditation and the baselines for several participants.

Moreover, the thesis investigates the sensitivity of the analytical approach to changes in the electrode subset used for the analysis. For each participant, I constructed a subset of electrodes that were most involved in the changing EEG dynamics. The personalized subset was most sensitive to changes between meditation and rest, compared to other subsets based on commercial wearable EEG headsets. Finally, I compare the findings of the dynamic systems approach to a conventional analytical approach, and the participants’ emotional ratings inquired in subjective questionnaires. Unfortunately, from the current data, there appears to be no relationship between the proposed features and the conventional measures or the subjective questionnaires.

Hand pose tracking in 3D configuration is one of the popular components of human-computer interaction research, which can be seen applying in virtual reality (VR) and augmented reality (AR). Although the research field has been thoroughly studied in the past few decades, most methods emphasized recognizing hand gestures or joint locations. However, computer interaction these days requires numerous manual object interactions. The paper first reviews state-of-the-art hand pose estimation and tracking methods to achieve accurate hand tracking and control. Then we describe the general procedure for recovery 3D hand model. In the second place, the results of tracking whole hand kinematics using the extended Kalman filter (EKF) based on data recorded from the surface markers are presented. Finally, in the part of teleoperation, the paper presents a novel method for applying synergy-based mapping strategy for SoftHand 2 control. And we demonstrate the simulation results of telemanipulation with hand racking through Simulink.