Seeing Our Blind Spots: Smart Glasses-Based Simulation to Increase Design Students’ Awareness of Visual Impairment

As the population ages, many will acquire visual impairments. To improve design for these users, it is essential to build awareness of their perspective during everyday routines, especially for design students. Although several visual impairment simulation toolkits exist in both academia and as commercial products, analog, and static visual impairment simulation tools do not simulate effects concerning the user’s eye movements. Meanwhile, VR and video see-through-based AR simulation methods are constrained by smaller fields of view when compared with the natural human visual field and also suffer from vergence-accommodation conflict (VAC) which correlates with visual fatigue, headache, and dizziness. In this paper, we enable an on-the-go, VAC-free, visually impaired experience by leveraging our optical see-through glasses. The FOV of our glasses is approximately 160 degrees for horizontal and 140 degrees for vertical, and participants can experience both losses of central vision and loss of peripheral vision at different severities. Our evaluation (n =14) indicates that the glasses can significantly and effectively reduce visual acuity and visual field without causing typical motion sickness symptoms such as headaches and or visual fatigue. Questionnaires and qualitative feedback also showed how the glasses helped to increase participants’ awareness of visual impairment

Metrics for monitoring patients progress in a rehabilitation context: a case study based on wearable inertial sensors

Inertial data can represent a rich source of clinically relevant information which can provide details on motor assessment in subjects undertaking a rehabilitation process. Indeed, in clinical and sport settings, motor assessment is generally conducted through simple subjective measures such as a visual assessment or questionnaire given by caregivers. As part of a mobile health application, wireless sensors such as inertial measurement units and associated data sets can help provide an objective and empirical measure of a patient’s progress through rehabilitation using on body sensors. In this publication, several metrics in different domains have been considered and extrapolated from the 3D accelerometer and angular rate data sets collected on an impaired subject with knee injury, via a wearable sensing system developed at the Tyndall National Institute. These data sets were collected for different activities performed across a number of sessions as the subject progressed through the rehabilitation process. Using these data sets, a novel and effective method has been investigated in order to define a single score indicator which can provide accurate quantitative analysis of the improvement of the subject throughout their rehabilitation. The indicator compares impaired and unimpaired limb motor performance. The present work proves that the defined score indicator can be taken into account by clinicians to study the overall patients’ condition and provide accurate clinical feedback as to their rehabilitative progress

LASSO regression for monitoring patients progress following ACL reconstruction via motion sensors: a case study

Inertial data can represent a rich source of clinically relevant information, which can provide details on motor assessment in subjects undertaking a rehabilitation process. Indeed, in clinical and sport settings, motor assessment is generally conducted through simple subjective measures such as a visual assessment or questionnaire given by caregivers. Thus, inertial sensor technology and associated data sets can help provide an objective and empirical measure of a patient’s progress. In this publication, several metrics in different domains have been considered and extrapolated from the three-dimensional accelerometer and angular rate data sets collected on an impaired subject with knee injury, via a wearable sensing system developed at the Tyndall National Institute. These data sets were collected for different activities performed across a number of sessions as the subject progressed through the rehabilitation process. Using these data sets and adopting a combination of techniques (LASSO, elastic net regularization, screening-based approaches, and leave-one-out cross-validation), an automated method has been defined in order to select the most suitable features which could provide accurate quantitative analysis of the improvement of the subject throughout their rehabilitation. The present work confirms that changes in motor ability can be objectively assessed via data-driven methods and that most of the alterations of interest occur on the sagittal plane and may be assessed by an accelerometer worn on the thigh

Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders

The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders

Research on mobility in older adults to improve the fall risk screening in physiotherapy

Hintergrund Sturzprävention ist eine gesundheitspolitische Herausforderung in einer alternden Gesellschaft. Es ist für viele Bereiche der Versorgungsforschung von hohem Interesse, Prä-diktoren für Stürze zu identifizieren, um wiederum die Einleitung geeigneter Präventionsmaß-nahmen zu ermöglichen und die Versorgungsqualität zu verbessern. Die vorliegende Arbeit soll einen Beitrag zum Sturzrisikoscreening bei älteren Menschen in der Physiotherapie leisten. Methodik Drei Publikationen aus drei wissenschaftlichen Projekten wurden in die vorliegende Dissertation einbezogen. Methodisch folgen alle drei Ansätze einem quantitativen Verfahren. Zwei Aspekte der funktionellen Mobilität - das Treppensteigen und das Gehen in der Ebene - sowie ein psychischer Aspekt, die Sturzangst, wurden im Fokus der vorliegenden Dissertation betrachtet: Ziel des ersten Projektes war es, einen Beitrag zur Analyse von Gangmustern mittels moderner Sensortechnologie zu leisten. Hierfür wurde die grundsätzliche Eignung eines intelli-genten Fußbodensensors, des SensFloor® der Firma FutureShape GmbH, für den klinischen Be-reich der Ganganalyse kritisch überprüft. Junge, gesunde Proband*innen gingen wiederholt über den SensFloor®, um ein künstliches neuronales Netzwerk mit diesen Gangdaten zu trainie-ren. Ziel der zweiten Studie war es, die Treppensteigegeschwindigkeit in einer Kohorte älterer stationärer Patient*innen sowie einer Kohorte älterer Menschen ohne funktionelle Beeinträch-tigungen zu untersuchen. Hierfür stiegen die Studienteilnehmer*innen einen Treppenabsatz von 13 Stufen hinauf und wieder hinunter. In der dritten Studie wurden für den „Survey of Acti-vities and Fear of Falling in the Elderly“-Fragebogen Grenzwerte für die Einteilung in niedrige, moderate und hohe Sturzangst ermittelt. Grundlage waren die Daten aus einer Kohorte 98 älte-rer stationärer Patient*innen. Ergebnisse Die SensFloor-Technologie ist lernfähig und geeignet, um zwischen unterschiedli-chen Gangmodi zu differenzieren. Die Test-Retest-Analyse der Treppensteigegeschwindigkeit lieferte moderate bis exzellente Ergebnisse. Die Analyse des Sturzangstscores zeigte, dass die optimalen Grenzwerte zur Klassifikation niedriger, moderater und hoher Sturzangst bei 0,6 und 1,4 liegen. Schlussfolgerungen Mit der Anwendung der Sensfloor-Technologie, der Treppensteige-schwindigkeit in Stufen pro Sekunde sowie der Klassifikation der Sturzangst bietet die vorliegen-de Arbeit drei neue Ansätze, welche beim Sturzrisikoscreening sowohl im klinischen Setting als auch in der Forschung zukünftig eine stärkere Beachtung finden sollten.Background In an aging society fall prevention is a focal point in healthcare policy. It is of high in-terest to identify predictors of falls, in order to initiate appropriate preventive measures and to im-prove the quality of care. It is the purpose of this thesis to make a contribution to fall risk screening in the elderly in physical therapy. Methods Three publications resulting from three scientific projects were included in this disserta-tion. Methodologically, all three approaches follow a quantitative method. Two aspects of func-tional mobility - stair climbing and walking on level ground - as well as a psychological aspect, fear of falling, are in the focus of the present thesis. The aim of the first publication was the examination of gait patterns using modern sensor technology. For this purpose, the eligibility of an intelligent floor, the SensFloor® by the FutureShape company, was critically reviewed for the clinical field of gait analysis. Young healthy participants walked over the SensFloor® repeatedly in order to train an artificial neural network with this gait data. The aim of the second study was to investigate stair climbing speed in a cohort of older hospitalized patients and a cohort of older adults without func-tional impairments. For this purpose, the participants climbed up and down a flight of 13 steps. In the third study classification schemes for low, moderate, and high fear of falling were calculated using the “Survey of Activities and Fear of Falling in the Elderly“ (SAFE). For this, data from a cohort of 98 older hospitalized patients was analyzed. Results The SensFloor technology is capable of learning and able to differentiate various gait modes. Test-retest analysis of stair climbing speed provided moderate to excellent results. Analysis of the fear of falling score for classifying low, moderate, and high fear of falling resulted in optimal cut-off points with .6 and 1.4. Conclusions With the application of SensFloor technology, stair climbing speed in steps per second and classification of fear of falling, the present thesis offers three new approaches that should re-ceive more attention in fall risk screening. The results obtained should be considered in both the clinical setting and clinical research

Life Sciences Program Tasks and Bibliography for FY 1996

This document includes information on all peer reviewed projects funded by the Office of Life and Microgravity Sciences and Applications, Life Sciences Division during fiscal year 1996. This document will be published annually and made available to scientists in the space life sciences field both as a hard copy and as an interactive Internet web page

Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.

peer reviewe

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered