Hendrik van der Loos

Electroencephalography (EEG) is a common neuroimaging technique used in clinical, research, and consumer technology due to its non-invasiveness, high time resolution, and sensitivity. Traditional EEG systems consist of wet electrodes and a desktop amplifier for measuring brain activity. While this system provides high signal quality in lab settings, it limits real-world EEG use, such as for ambulatory EEG monitoring, or EEG measurements during exercise. This is due to the training and set up time required for wet electrode application and the bulky, wired traditional amplifier. While some wearable EEG devices exist on the market, EEG measurements during motion requires further signal quality validation to confirm clinical or research utility due to motion artifacts. The objectives of this thesis are to develop custom dry flexible electrodes with improved usability for wearable systems and to compare signal quality of the custom dry electrodes with traditional wet electrodes in mobile measurements. We have developed comb-shaped, dry electrodes for measuring scalp EEG. This electrode incorporates embedded silver thread for a novel yet simple to fabricate design, which allows for a flexible electrode providing impedances similar or lower to those in literature. Participants were instrumented with an EEG cap containing gold cup, gel, and dry electrodes for brain activity measurement as well as an inertial measurement unit (IMU) mouthguard for head kinematics measurements. Data were collected from 8 participants, and during nearly all trials, dry electrodes showed worse signal correlation with gold cup electrodes compared with gel electrodes. For motion artifact characterization, EEG-IMU coherence was found to be higher for dry electrodes in low motion scenarios (e.g., walking), but similar between the 3 electrode types for high motion scenarios (e.g., jumping). This indicates that dry electrodes may be more prone to motion artifacts at lower levels of activity, but with large enough activity all 3 types of electrodes could be equally affected. In summary, we developed custom flexible dry electrodes with improved usability for wearable systems. However, we showed that dry electrode signal quality may be substantially affected by motion artifacts, and wet electrodes may be more suitable for measurements during motion.

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Mobile robots have recently appeared in public spaces such as shopping malls, airports, and urban sidewalks. These robots are designed with human-aware motion planning capabilities, but they are not designed to communicate with pedestrians. Pedestrians encounter these robots without any training or prior understanding of the robots’ behaviour, which can cause discomfort, confusion, and delay social acceptance. The two studies described in this thesis evaluate robot communication cues designed for human-robot spatial interactions in public spaces. By communicating robot behaviour to pedestrians, these cues aim to increase the social acceptability of mobile robots. Both studies use videos of the robot cues and online surveys to collect data from human participants.Study 1 evaluates two different modalities for communicating a robot’s movement to pedestrians: flashing lights and light projection. Previous reviewed literature had not directly compared these two modalities of motion legibility cues. Study 1 also compares using these cues to communicate path information or goal information, which are contributing factors to legible robot motion. Previous reviewed literature had not compared path and goal information for motion legibility cues using visual modalities. Results show that light projection is a more socially acceptable modality than flashing lights, and that these cues are most socially acceptable when they communicate both path and goal information.Study 2 evaluates different communication cues used by a robot to yield to a pedestrian at a doorway. The experiment compared decelerating, retreating, and rotating motions. These motions had not previously been directly compared in this context. Results show that a robot retreating behaviour was the most socially acceptable cue.The results of this work help guide the development of mobile robots for public spaces. Supplementary materials available at: http://hdl.handle.net/2429/76280

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Over the past two decades, a variety of mobility add-ons for manual wheelchairs have emerged in the assistive technology industry, including pushrim-actuated power-assist wheelchairs, motorized propulsion aids, manual and motorized front-end drive attachments, and passive attachable wheels. These technologies are typically used by long-term lightweight manual wheelchair users including those from spinal cord injury populations, and increase the mobility capabilities of the wheelchair, such as through the addition of all-terrain wheels or power-assistance. Currently, little is known about how mobility add-ons affect the durability, strength, and lifespan of manual wheelchairs, and whether they increase the risk of component failures. In particular, very little research has assessed the likelihood of failures associated with front-attached mobility add-ons. Component failures can lead to wheelchair rider injuries or leave users stranded. Additionally, repairing or replacing damaged frames can incur significant costs. Finite element analysis (FEA) is a technique frequently used in structural analysis. The goal of this thesis is to develop a finite element model of a wheelchair when used with a passive, front-attached mobility add-on that attaches at the footplate. The FEA model was physically validated using strain gauges under static loading scenarios. The validated model was then used to assess stresses and displacements under static loading considering several different design variables and dynamic loads based on experimental use cases, and considers how these factors impact number of cycles to fatigue failure in the system and therefore the overall lifespan of the wheelchair. Results found that the use of a footplate-mounted mobility add-on increased stresses in the horizontal portion near the tube intersections of a D-shaped footplate. The thickness of the tubing in the footplate and the location of the rear axle created high stresses in the footplate under particular customizations. Furthermore, it was found that user mass and increased frontal impacts greatly reduced the hours of use to failure for the chair. Through identifying the location and magnitudes of points of failure, design guidelines such as changes to attachment location or recommendations for reinforcement in manual frames can be provided to minimize risks of component failures.

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Movement events during sleep could be used to infer underlying sleep physiologies and disorders based on their motor presentations. Periodic Limb Movement Disorder (PLMD), for instance, mostly occurs in the lower extremities and usually involves the dorsiflexion of the ankle. Evaluation of sleep disorders is typically done through clinical polysomnography (PSG). While PSG remains the most reliable and comprehensive tool for such assessments, the studies are intensive in terms of time, cost and labor. Certain motor indices might be underestimated due to the nature of PSG instrumentation, and for some populations, these studies could be considered intrusive and uncomfortable. In this work, SleepSmart, a mattress-based sensing system composing of an 8x6 array of 3-D accelerometer sensors, was developed to provide data for machine learning algorithms to classify body movements into different levels of granularity (coarse/fine-grained labels). A study with 10 subjects was conducted. A movement protocol was adapted to simulate movements during sleep. Three classification domains were defined for the movements: a) Domain A – 3 classes inferring general movement characteristics, b) Domain B – 8 classes indicating movements at various body locations, and c) Domain C – 22 classes, where each class corresponds to a specific movement descriptor. Four learning algorithms were tested and compared. Random Forest (RF), Support Vector Machines (SVM), Naïve- Bayes (NB), and the k-Nearest Neighbor (k-NN) algorithms were used. The classification accuracies averaged across all domains were 96.91%, 94.10%, 88.91%, 83.88% for subject-dependent models, and 89.87%, 89.45%, 73.95%, 69.21% for subject-independent models for the RF, SVM, NB and k-NN algorithms, respectively. In RF models, averaged recall and precision measures were 96.29% and 96.74% for subject-dependent models, and 89.23% and 89.91% for subject-independent models. The investigation of the effect of different training sizes revealed small sample requirements for training (as low as 3 training samples per class) to attain accuracies higher or comparable to the baseline value (84%) for each domain. In this work, we have proposed a non-invasive sensor system and demonstrated the generalizability and the effectiveness of the system in classifying movements at different label granularities under subject-dependent and subject-independent considerations.

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A common treatment for people with motor disabilities due to neurological injuries, such as Cerebral Palsy, is physiotherapy. However, the repetitive nature of clinical upper-limb rehabilitation exercises can lead to a decrease in patient adherence, and so, there is a need for more engaging methods of motor task training that can be used in inexpensive at-home programs. One such alternate solution is the use of exergaming technology, which harnesses commercially available motion-tracking gaming hardware to administer rehabilitative exercises that provide automated real-time feedback and engaging game mechanics.In addition to real-time feedback during exercises in the form of binary visual cues, reminders, and scores, Error augmentation (EA), or dynamic feedback based on deviation from desired movement patterns, has been shown to provide an increased rate of improvement in motor ability. The goal of this thesis study was to build upon the idea of training with EA and to evaluate the effectiveness of the amplification of asymmetry between the two upper limbs during a bilateral reaching movement. To test if this mode of error augmentation increases symmetry in the forward-reaching direction, a bimanual training task was developed in an immersive virtual reality environment. A single-session crossover study design was used to test if participants could reach with more bilateral symmetry when training with EA. Participants with hemiplegia and healthy age-matched participants completed training sets with and without EA. Results found a significant difference in symmetry between the two sets in the typically developing participant group. Both primary and secondary outcomes showed high variability in between-participant averages; it is suggested that a future study be conducted with a larger sample size of participants with hemiplegia. While statistically significant differences could not be found for the target clinical population, the effect of EA on the typically developing participant group provides a promising indication of a useful feedback modality that could be used in future upper-limb rehabilitation tools.

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Orthopaedic injury is set to become the 3rd leading cause globally of disability and death by 2030. Despite the cost-effectiveness and efficacy of orthopaedic surgery, there is a significant gap in access to care. Of importance to delivering safe, modern, and timely treatment is access to high quality medical devices. This access however is limited by a mismatch between the technology that industry is developing and what is needed in low- and middle-income country (LMIC) contexts. Through field study in a Ugandan hospital setting, this research examined two methods for participatory design of medical devices in LMICs that seek to overcome gaps in understanding between designers and users across different cultural and professional backgrounds, as well as the "Expert User" problem. The use of Cultural Probes and Outcome-Driven Innovation has proven useful to perform detailed needs finding, filtering and prioritization, which are critical early steps of the design process. The results point to a myriad of challenges across domains: technology, systemic, infrastructure, staff, and patients, which all contribute to difficulty in providing timely, safe surgical care. They also give designers insight into which technology areas are the most underserved, and which attributes of technology might warrant special consideration in the design process. From these results, the design of a bone reduction and alignment device was prototyped, with feedback sought from Ugandan surgeons. The many lessons from this research have been applied in the past five years to the development and commercialization of the DrillCover product through Arbutus Medical. It is our hope that industry, engineers, and designers take the lead in addressing the medical device mismatch by leveraging these participatory design methods when embarking on medical device innovation projects for LMIC users, and importantly, include these users in a collaborative design process for a higher likelihood of success.

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Powered lower limb exoskeletons (LLEs) are wearable robotic aids that provide mobility assistance for people with mobility impairments. Despite their advanced design, LLEs are still far from being effective assistive devices that can be used to perform activities of daily living. The main challenge in the operation of a LLE is to ensure that balance is maintained. However, maintaining an upright stance is not always achievable and regardless of the quality of user skill and training, inevitably falls will occur. Currently, there is no control strategy developed or implemented in LLEs that help reduce the user’s risk of injury in the case of an unexpected fall. In this thesis, an optimization methodology was developed and used to create a safer strategy for exoskeletons falling backwards in a simulation environment. Due to the data available regarding the biomechanics of human falls, the optimization methodology was first developed to study falls with simulation parameters characteristic of healthy people. The resulting optimal fall strategy in this study had similar kinematic and dynamic characteristics to the findings of previous studies on human falls. Rapid knee flexion at the onset of the fall, and knee extension prior to ground contact are examples of these characteristics. Following this, the optimization methodology was extended to include the characteristics of an exoskeleton. The results revealed that the hip impact velocity was reduced by 58% when the optimal fall strategy was employed compared to the case where the exoskeleton fell with locked joints. It was also shown that in both cases of optimal human and human-exoskeleton falls, the models contacted the ground with an upright trunk with a near-zero trunk angular velocity to avoid head impact. These results achieved the thesis goal of developing an effective safe fall control strategy. This strategy was then implemented in a prototype exoskeleton test device. The experimental results validated the simulation outcomes and support the feasibility of implementing this control strategy. Future studies are needed to further examine the effectiveness of applying this strategy in an actual LLE.

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According to the World Health Organization, appropriate medical devices are not sufficiently available in low-resource environments within low and middle income countries. Lack of systematic structures, challenges with entering existing markets and incomplete understanding of design needs within these contexts are the key reasons for this problem. It is challenging to understand the needs for medical device development in low and middle income countries because the problem space has complex socioeconomic, political, technical and clinical constraints to navigate. Existing needs-finding techniques for engineering design do not provide an explicit means of identifying and synthesizing these complex factors. The main contribution of this thesis is development of a novel needs-finding technique for medical device development, specifically for low-resource environments. The proposed novel technique is empirically compared to the needs-finding technique of the well-established Stanford Biodesign Process. In a series of studies, the Activity Theory-based Needs-finding Technique (ATNF), based on Activity Theory, was integrated into the engineering design process. The cultural historical Activity Theory, rooted in Russian psychology, provides a framework for analyzing human activity and social structures. The ATNF proposes a modified activity system that explicitly situates technology within an activity. Mapping activities and identifying tension points within them allow for a fuller understanding of design needs. The ATNF method was initially investigated through its detailed application on a case study in the field of health technology development in low-resource environments. Thereafter, an ethnographic comparative study was completed to investigate the ATNF technique and the Biodesign technique by examining the differences between the needs statements and the process of developing them. The results indicate that the novel ATNF method is more effective in identifying an appropriate scope and desired change. However, the design artefacts from the ATNF and the Biodesign techniques equally cover socioeconomic, clinical and technical issues. This suggests that the strength of the ATNF technique is in creating connections between issues to develop an appropriate scope and in identifying desired change. The research supports that the ATNF technique is a viable needs-finding method and that it has particular strengths that could be leveraged for medical device development in low-resource environments.

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This thesis describes the design and evaluation of wire-tracing task in pCubee, an improved version of hand-held perspective-corrected display that allows the user to observe and interact with 3D content visualized inside the cubic system. In order to overcome visual discontinuity issues identified from previous works, we redesigned pCubee system using OLED panels and FPGA-based display controller to achieve reduced seam size and compact form-factor. We investigated user performance with the new system using a trajectory-based wire-tracing task where users were asked to move a ring along wires. Experiments were conducted to evaluate the impact of ring radius, wire length and curvature. Analysis of results revealed that a linear model similar to the steering law for 2D tunnel task applies to 3D trajectory-based task in pCubee as well, exhibiting an increase of task completion time when smaller ring or longer wire is used. Our study complemented the theory that 3D interaction in virtual reality system follows existing principle for 2D tasks, and also identified a potential method to evaluate interaction designs for geometric displays. This work could help motivate future development of pCubee and guide interaction design for similar systems.

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Due to limited therapeutic resources and the high cost of therapist-administered treatments, victims of neurological trauma such as stroke need alternate solutions, e.g., virtual rehabilitation trainers. Effective virtual trainers can also benefit able-bodied people in tasks involving motor learning or motion refining. Since real-time instructional cues, provided through various sensory channels (visual, auditory, and haptic), function in the same way as human trainers, automated feedback systems are gaining momentum in the motor learning and retraining field. Given that most daily tasks require coordination of both arms, the aim of this thesis is to evaluate the potential of utilizing real-time corrective vibrotactile (vibration) feedback to facilitate a training regime for simultaneous, bilateral arm motions. To address the research goal, the author designed and developed a low-cost, upper-arm, motion training system consisting of a wireless, wearable sleeve-armband device with embedded vibration motors, a vision-based bimanual motion tracker, and real-time stimulus-response control and data logging software. Since there are two logical, but different, movement responses a person might have toward directional vibrotactile cues, i.e., moving towards or away from a stimulus, the first study investigated if an intuitive and consistent response exists among participants. This study also investigated if providing stimuli using different actuator configurations could facilitate a more consistent response among participants. The study results showed high variability in participants’ motion response to vibrotactile cues regardless of the actuator configurations. Thus, researchers should account for the perceptual differences among individuals and avoid training users with an unintuitive vibration response that could affect the outcome measures. The second study evaluated the motion training system with both hemiparetic stroke survivors and healthy adults. Prior to assessing the training system, the experimenter set up a customized game and task path based on each participant’s reaching pattern and trained the participants’ reaction toward vibrotactile cues based on their own preferences. Vibrotactile training was found to successfully alter healthy participants’ original trajectories and to increase the end-position precision of the stroke participants’ affected hand and bimanual coordination. These results suggest the promising use of vibrotactile feedback in bilateral motion training for both healthy and hemiparetic stroke populations.

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We study a supersonic beam of cold, dense, xenon Rydberg atoms as it evolves to an ultracoldplasma. At early times, while the free electron density is low, d-series Rydbergs atoms undergolong-range ℓ-mixing collisions producing states of high orbital angular momentum. These high-ℓstates drive dipole-dipole interactions where Penning ionization provides a seed of electrons in a cloud of Rydberg atoms excited into the 51d state. The electron density increases and reachesthe threshold for avalanche into plasma at 25 μs. After 90 μs the plasma becomes fully formeddeveloping rigidity to a 432 V/cm ionizing field as well as sensitivity to a weak 500 mV/cm field.A shell model was developed to understand the dynamics behind this process.In addition, in collaboration with the Weidemüller group, a model was developed using Penningionization to seed the spontaneous avalanche of a cloud of strongly blockaded Rydberg atoms in a MOT.

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Before robot-assisted therapy regimens can be included in clinical practice, one of the major challenges to overcome is maintaining the patient’s engagement in the therapy during the lengthy functional recovery period. Game designers and psychologists have theorized the mechanics of sustaining an individual’s engagement in a task. In a motor learning context, to maintain motivation to continue an exercise, one must be kept exercising at one’s desirable difficulty by manipulation of the task challenge over the course of treatment. Thus, this work was aimed to design a robotic therapy regimen that can automatically adjust the difficulty to motivate users to continue with the exercise. The main contributions of this thesis are to develop a method to predict the user’s desirable difficulty and validate the effects of adaptively adjusting a robotic exercise on the user’s perception of the task.The theory of desirable difficulty relies on three main factors: meaningful levels of difficulties, knowledge of the user’s challenge preference, and positive effects of exercising a task under the desirable difficulty conditions. Studies to develop implementations of the first two factors in the context of an upper-limb reaching task were conducted, and investigated the effects of practicing this task under the desirable difficulty conditions. The first study implemented five error amplification (EA) methods for a reaching task and validated that users perceive each with a different challenge level. In the second study, users’ physiological and motor performance metrics were collected, as well as self-reports of the user’s challenge preference after exercising with each of the EAs. The efficiency of different machine learning methods in predicting a user’s challenge preference based on different combinations of physiological and motor performance attributes were analyzed. In the third study, the control group received EAs in predefined random order while the experimental group received EAs based on the predictions of the trained machine learning algorithm. The experimental group reported statistically significant higher scores on the metrics that assessed satisfaction, attentiveness, and willingness to continue the task. These results support the approach of designing a robotic system capable of adjusting exercises to prolong individuals’ engagement in stroke therapy.

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Resource conflict arises when people share spaces and objects with each other. People easily resolve such conflicts using verbal/nonverbal communication. With the advent of robots entering homes and offices, this thesis builds a framework todevelop a natural means of managing shared resources in human-robot collaboration contexts. In this thesis, hesitation gestures are developed as a communicative mechanism for robots to respond to human-robot resource conflicts.In the first of the three studies presented in this thesis (Study I), a pilot experiment and six online surveys provided empirical demonstrations that humans perceive hesitations from robot trajectories mimicking human hesitation motions. Using the set of human motions recorded from Study I, a characteristic acceleration profile of hesitation gestures was extracted and distilled into a trajectory design specification representing hesitation, namely the Acceleration-based Hesitation Profile (AHP). In Study II, the efficacy of AHP was tested and validated. In Study III, the impact of AHP-based robot motions was investigated in a Human-Robot Shared-Task (HRST) experiment.The results from these studies indicate that AHP-based robot responses are perceived by human observers to convey hesitation, both in observational and in situ contexts. The results also demonstrate that AHP-based responses, when compared with the abrupt collision avoidance responses typical of industrial robots, do not significantly improve or hinder human perception of the robot and human-robot team performance.The main contribution of this work is an empirically validated trajectory design that can be used to convey a robot’s state of hesitation in real-time to human observers, while achieving the same collision avoidance function as a traditional collision avoidance trajectory.

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