Functional-Material-Based Touch Interfaces for Multidimensional Sensing for Interactive Displays: A Review

Multidimensional sensing is a highly desired attribute for allowing human-machine interfaces (HMIs) to perceive various types of information from both users and the environment, thus enabling the advancement of various smart electronics/applications, e.g., smartphones and smart cities. Conventional multidimensional sensing is achieved through the integration of multiple discrete sensors, which introduces issues such as high energy consumption and high circuit complexity. These disadvantages have motivated the widespread use of functional materials for detecting various stimuli at low cost with low power requirements. This work presents an overview of simply structured touch interfaces for multidimensional (x-y location, force and temperature) sensing enabled by piezoelectric, piezoresistive, triboelectric, pyroelectric and thermoelectric materials. For each technology, the mechanism of operation, state-of-the-art designs, merits, and drawbacks are investigated. At the end of the article, the author discusses the challenges limiting the successful applications of functional materials in commercial touch interfaces and corresponding development trends

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

From plain visualisation to vibration sensing: using a camera to control the flexibilities in the ITER remote handling equipment

Thermonuclear fusion is expected to play a key role in the energy market during the second half of this century, reaching 20% of the electricity generation by 2100. For many years, fusion scientists and engineers have been developing the various technologies required to build nuclear power stations allowing a sustained fusion reaction. To the maximum possible extent, maintenance operations in fusion reactors are performed manually by qualified workers in full accordance with the "as low as reasonably achievable" (ALARA) principle. However, the option of hands-on maintenance becomes impractical, difficult or simply impossible in many circumstances, such as high biological dose rates. In this case, maintenance tasks will be performed with remote handling (RH) techniques. The International Thermonuclear Experimental Reactor ITER, to be commissioned in southern France around 2025, will be the first fusion experiment producing more power from fusion than energy necessary to heat the plasma. Its main objective is “to demonstrate the scientific and technological feasibility of fusion power for peaceful purposes”. However ITER represents an unequalled challenge in terms of RH system design, since it will be much more demanding and complex than any other remote maintenance system previously designed. The introduction of man-in-the-loop capabilities in the robotic systems designed for ITER maintenance would provide useful assistance during inspection, i.e. by providing the operator the ability and flexibility to locate and examine unplanned targets, or during handling operations, i.e. by making peg-in-hole tasks easier. Unfortunately, most transmission technologies able to withstand the very specific and extreme environmental conditions existing inside a fusion reactor are based on gears, screws, cables and chains, which make the whole system very flexible and subject to vibrations. This effect is further increased as structural parts of the maintenance equipment are generally lightweight and slender structures due to the size and the arduous accessibility to the reactor. Several methodologies aiming at avoiding or limiting the effects of vibrations on RH system performance have been investigated over the past decade. These methods often rely on the use of vibration sensors such as accelerometers. However, reviewing market shows that there is no commercial off-the-shelf (COTS) accelerometer that meets the very specific requirements for vibration sensing in the ITER in-vessel RH equipment (resilience to high total integrated dose, high sensitivity). The customisation and qualification of existing products or investigation of new concepts might be considered. However, these options would inevitably involve high development costs. While an extensive amount of work has been published on the modelling and control of flexible manipulators in the 1980s and 1990s, the possibility to use vision devices to stabilise an oscillating robotic arm has only been considered very recently and this promising solution has not been discussed at length. In parallel, recent developments on machine vision systems in nuclear environment have been very encouraging. Although they do not deal directly with vibration sensing, they open up new prospects in the use of radiation tolerant cameras. This thesis aims to demonstrate that vibration control of remote maintenance equipment operating in harsh environments such as ITER can be achieved without considering any extra sensor besides the embarked rad-hardened cameras that will inevitably be used to provide real-time visual feedback to the operators. In other words it is proposed to consider the radiation-tolerant vision devices as full sensors providing quantitative data that can be processed by the control scheme and not only as plain video feedback providing qualitative information. The work conducted within the present thesis has confirmed that methods based on the tracking of visual features from an unknown environment are effective candidates for the real-time control of vibrations. Oscillations induced at the end effector are estimated by exploiting a simple physical model of the manipulator. Using a camera mounted in an eye-in-hand configuration, this model is adjusted using direct measurement of the tip oscillations with respect to the static environment. The primary contribution of this thesis consists of implementing a markerless tracker to determine the velocity of a tip-mounted camera in an untrimmed environment in order to stabilise an oscillating long-reach robotic arm. In particular, this method implies modifying an existing online interaction matrix estimator to make it self-adjustable and deriving a multimode dynamic model of a flexible rotating beam. An innovative vision-based method using sinusoidal regression to sense low-frequency oscillations is also proposed and tested. Finally, the problem of online estimation of the image capture delay for visual servoing applications with high dynamics is addressed and an original approach based on the concept of cross-correlation is presented and experimentally validated

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Retainer-Free Optopalatographic Device Design and Evaluation as a Feedback Tool in Post-Stroke Speech and Swallowing Therapy

Stroke is one of the leading causes of long-term motor disability, including oro-facial impairments which affect speech and swallowing. Over the last decades, rehabilitation programs have evolved from utilizing mainly compensatory measures to focusing on recovering lost function. In the continuing effort to improve recovery, the concept of biofeedback has increasingly been leveraged to enhance self-efficacy, motivation and engagement during training. Although both speech and swallowing disturbances resulting from oro-facial impairments are frequent sequelae of stroke, efforts to develop sensing technologies that provide comprehensive and quantitative feedback on articulator kinematics and kinetics, especially those of the tongue, and specifically during post-stroke speech and swallowing therapy have been sparse. To that end, such a sensing device needs to accurately capture intraoral tongue motion and contact with the hard palate, which can then be translated into an appropriate form of feedback, without affecting tongue motion itself and while still being light-weight and portable. This dissertation proposes the use of an intraoral sensing principle known as optopalatography to provide such feedback while also exploring the design of optopalatographic devices itself for use in dysphagia and dysarthria therapy. Additionally, it presents an alternative means of holding the device in place inside the oral cavity with a newly developed palatal adhesive instead of relying on dental retainers, which previously limited device usage to a single person. The evaluation was performed on the task of automatically classifying different functional tongue exercises from one another with application in dysphagia therapy, whereas a phoneme recognition task was conducted with application in dysarthria therapy. Results on the palatal adhesive suggest that it is indeed a valid alternative to dental retainers when device residence time inside the oral cavity is limited to several tens of minutes per session, which is the case for dysphagia and dysarthria therapy. Functional tongue exercises were classified with approximately 61 % accuracy across subjects, whereas for the phoneme recognition task, tense vowels had the highest recognition rate, followed by lax vowels and consonants. In summary, retainer-free optopalatography has the potential to become a viable method for providing real-time feedback on tongue movements inside the oral cavity, but still requires further improvements as outlined in the remarks on future development.:1 Introduction 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Goals and contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Scope and limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Basics of post-stroke speech and swallowing therapy 2.1 Dysarthria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Dysphagia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 Treatment rationale and potential of biofeedback . . . . . . . . . . . . . . . . . 13 2.4 Summary and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3 Tongue motion sensing 3.1 Contact-based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.1 Electropalatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.2 Manometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.1.3 Capacitive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2 Non-contact based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.1 Electromagnetic articulography . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.2 Permanent magnetic articulography . . . . . . . . . . . . . . . . . . . . 24 3.2.3 Optopalatography (related work) . . . . . . . . . . . . . . . . . . . . . . 25 3.3 Electro-optical stomatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.4 Extraoral sensing techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.5 Summary, comparison and conclusion . . . . . . . . . . . . . . . . . . . . . . . 29 4 Fundamentals of optopalatography 4.1 Important radiometric quantities . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.1.1 Solid angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.1.2 Radiant flux and radiant intensity . . . . . . . . . . . . . . . . . . . . . 33 4.1.3 Irradiance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.1.4 Radiance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.2 Sensing principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.2.1 Analytical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2.2 Monte Carlo ray tracing methods . . . . . . . . . . . . . . . . . . . . . . 37 4.2.3 Data-driven models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.2.4 Model comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.3 A priori device design consideration . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.1 Optoelectronic components . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.2 Additional electrical components and requirements . . . . . . . . . . . . 43 4.3.3 Intraoral sensor layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 5 Intraoral device anchorage 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.1.1 Mucoadhesion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.1.2 Considerations for the palatal adhesive . . . . . . . . . . . . . . . . . . . 48 5.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2.1 Polymer selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2.2 Fabrication method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.2.3 Formulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.4 PEO tablets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.5 Connection to the intraoral sensor’s encapsulation . . . . . . . . . . . . 50 5.2.6 Formulation evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.3.1 Initial formulation evaluation . . . . . . . . . . . . . . . . . . . . . . . . 54 5.3.2 Final OPG adhesive formulation . . . . . . . . . . . . . . . . . . . . . . 56 5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 6 Initial device design with application in dysphagia therapy 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 6.2 Optode and optical sensor selection . . . . . . . . . . . . . . . . . . . . . . . . . 60 6.2.1 Optode and optical sensor evaluation procedure . . . . . . . . . . . . . . 61 6.2.2 Selected optical sensor characterization . . . . . . . . . . . . . . . . . . 62 6.2.3 Mapping from counts to millimeter . . . . . . . . . . . . . . . . . . . . . 62 6.2.4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 6.3 Device design and hardware implementation . . . . . . . . . . . . . . . . . . . . 64 6.3.1 Block diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6.3.2 Optode placement and circuit board dimensions . . . . . . . . . . . . . 64 6.3.3 Firmware description and measurement cycle . . . . . . . . . . . . . . . 66 6.3.4 Encapsulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 6.3.5 Fully assembled OPG device . . . . . . . . . . . . . . . . . . . . . . . . 67 6.4 Evaluation on the gesture recognition task . . . . . . . . . . . . . . . . . . . . . 69 6.4.1 Exercise selection, setup and recording . . . . . . . . . . . . . . . . . . . 69 6.4.2 Data corpus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6.4.3 Sequence pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6.4.4 Choice of classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 6.4.5 Training and evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.4.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 7 Improved device design with application in dysarthria therapy 7.1 Device design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 7.1.1 Design considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 7.1.2 General system overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.1.3 Intraoral sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.1.4 Receiver and controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 7.1.5 Multiplexer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 7.2 Hardware implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 7.2.1 Optode placement and circuit board layout . . . . . . . . . . . . . . . . 87 7.2.2 Encapsulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 7.3 Device characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 7.3.1 Photodiode transient response . . . . . . . . . . . . . . . . . . . . . . . 91 7.3.2 Current source and rise time . . . . . . . . . . . . . . . . . . . . . . . . 91 7.3.3 Multiplexer switching speed . . . . . . . . . . . . . . . . . . . . . . . . . 92 7.3.4 Measurement cycle and firmware implementation . . . . . . . . . . . . . 93 7.3.5 In vitro measurement accuracy . . . . . . . . . . . . . . . . . . . . . . . 95 7.3.6 Optode measurement stability . . . . . . . . . . . . . . . . . . . . . . . 96 7.4 Evaluation on the phoneme recognition task . . . . . . . . . . . . . . . . . . . . 98 7.4.1 Corpus selection and recording setup . . . . . . . . . . . . . . . . . . . . 98 7.4.2 Annotation and sensor data post-processing . . . . . . . . . . . . . . . . 98 7.4.3 Mapping from counts to millimeter . . . . . . . . . . . . . . . . . . . . . 99 7.4.4 Classifier and feature selection . . . . . . . . . . . . . . . . . . . . . . . 100 7.4.5 Evaluation paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 7.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 7.5.1 Tongue distance curve prediction . . . . . . . . . . . . . . . . . . . . . . 105 7.5.2 Tongue contact patterns and contours . . . . . . . . . . . . . . . . . . . 105 7.5.3 Phoneme recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 7.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 8 Conclusion and future work 115 9 Appendix 9.1 Analytical light transport models . . . . . . . . . . . . . . . . . . . . . . . . . . 119 9.2 Meshed Monte Carlo method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 9.3 Laser safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 9.4 Current source modulation voltage . . . . . . . . . . . . . . . . . . . . . . . . . 123 9.5 Transimpedance amplifier’s frequency responses . . . . . . . . . . . . . . . . . . 123 9.6 Initial OPG device’s PCB layout and circuit diagrams . . . . . . . . . . . . . . 127 9.7 Improved OPG device’s PCB layout and circuit diagrams . . . . . . . . . . . . 129 9.8 Test station layout drawing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 Bibliography 152Der Schlaganfall ist eine der häufigsten Ursachen für motorische Langzeitbehinderungen, einschließlich solcher im Mund- und Gesichtsbereich, deren Folgen u.a. Sprech- und Schluckprobleme beinhalten, welche sich in den beiden Symptomen Dysarthrie und Dysphagie äußern. In den letzten Jahrzehnten haben sich Rehabilitationsprogramme für die Behandlung von motorisch ausgeprägten Schlaganfallsymptomatiken substantiell weiterentwickelt. So liegt nicht mehr die reine Kompensation von verlorengegangener motorischer Funktionalität im Vordergrund, sondern deren aktive Wiederherstellung. Dabei hat u.a. die Verwendung von sogenanntem Biofeedback vermehrt Einzug in die Therapie erhalten, um Motivation, Engagement und Selbstwahrnehmung von ansonsten unbewussten Bewegungsabläufen seitens der Patienten zu fördern. Obwohl jedoch Sprech- und Schluckstörungen eine der häufigsten Folgen eines Schlaganfalls darstellen, wird diese Tatsache nicht von der aktuellen Entwicklung neuer Geräte und Messmethoden für quantitatives und umfassendes Biofeedback reflektiert, insbesondere nicht für die explizite Erfassung intraoraler Zungenkinematik und -kinetik und für den Anwendungsfall in der Schlaganfalltherapie. Ein möglicher Grund dafür liegt in den sehr strikten Anforderungen an ein solche Messmethode: Sie muss neben Portabilität idealerweise sowohl den Kontakt zwischen der Zunge und dem Gaumen, als auch die dreidimensionale Bewegung der Zunge in der Mundhöhle erfassen, ohne dabei die Artikulation selbst zu beeinflussen. Um diesen Anforderungen gerecht zu werden, wird in dieser Dissertation das Messprinzip der Optopalatographie untersucht, mit dem Schwerpunkt auf der Anwendung in der Dysarthrie- und Dysphagietherapie. Dies beinhaltet auch die Entwicklung eines entsprechenden Gerätes sowie dessen Befestigungsmethode in der Mundhöhle über ein dediziertes Mundschleimhautadhäsiv. Letzteres umgeht das bisherige Problem der notwendigen Anpassung eines solchen intraoralen Gerätes an einen einzelnen Nutzer. Für die Anwendung in der Dysphagietherapie erfolgte die Evaluation anhand einer automatischen Erkennung von Mobilisationsübungen der Zunge, welche routinemäßig in der funktionalen Dysphagietherapie durchgeführt werden. Für die Anwendung in der Dysarthrietherapie wurde eine Lauterkennung durchgeführt. Die Resultate bezüglich der Verwendung des Mundschleimhautadhäsives suggerieren, dass dieses tatsächlich eine valide Alternative zu den bisher verwendeten Techniken zur Befestigung intraoraler Geräte in der Mundhöhle darstellt. Zungenmobilisationsübungen wurden über Probanden hinweg mit einer Rate von 61 % erkannt, wogegen in der Lauterkennung Langvokale die höchste Erkennungsrate erzielten, gefolgt von Kurzvokalen und Konsonanten. Zusammenfassend lässt sich konstatieren, dass das Prinzip der Optopalatographie eine ernstzunehmende Option für die intraorale Erfassung von Zungenbewegungen darstellt, wobei weitere Entwicklungsschritte notwendig sind, welche im Ausblick zusammengefasst sind.:1 Introduction 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Goals and contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Scope and limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Basics of post-stroke speech and swallowing therapy 2.1 Dysarthria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Dysphagia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 Treatment rationale and potential of biofeedback . . . . . . . . . . . . . . . . . 13 2.4 Summary and conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3 Tongue motion sensing 3.1 Contact-based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.1 Electropalatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.2 Manometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.1.3 Capacitive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2 Non-contact based methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.1 Electromagnetic articulography . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.2 Permanent magnetic articulography . . . . . . . . . . . . . . . . . . . . 24 3.2.3 Optopalatography (related work) . . . . . . . . . . . . . . . . . . . . . . 25 3.3 Electro-optical stomatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.4 Extraoral sensing techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.5 Summary, comparison and conclusion . . . . . . . . . . . . . . . . . . . . . . . 29 4 Fundamentals of optopalatography 4.1 Important radiometric quantities . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.1.1 Solid angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.1.2 Radiant flux and radiant intensity . . . . . . . . . . . . . . . . . . . . . 33 4.1.3 Irradiance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.1.4 Radiance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.2 Sensing principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.2.1 Analytical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2.2 Monte Carlo ray tracing methods . . . . . . . . . . . . . . . . . . . . . . 37 4.2.3 Data-driven models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.2.4 Model comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.3 A priori device design consideration . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.1 Optoelectronic components . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.3.2 Additional electrical components and requirements . . . . . . . . . . . . 43 4.3.3 Intraoral sensor layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 5 Intraoral device anchorage 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.1.1 Mucoadhesion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.1.2 Considerations for the palatal adhesive . . . . . . . . . . . . . . . . . . . 48 5.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2.1 Polymer selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2.2 Fabrication method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.2.3 Formulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.4 PEO tablets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.5 Connection to the intraoral sensor’s encapsulation . . . . . . . . . . . . 50 5.2.6 Formulation evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5.3.1 Initial formulation evaluation . . . . . . . . . . . . . . . . . . . . . . . . 54 5.3.2 Final OPG adhesive formulation . . . . . . . . . . . . . . . . . . . . . . 56 5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 6 Initial device design with application in dysphagia therapy 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 6.2 Optode and optical sensor selection . . . . . . . . . . . . . . . . . . . . . . . . . 60 6.2.1 Optode and optical sensor evaluation procedure . . . . . . . . . . . . . . 61 6.2.2 Selected optical sensor characterization . . . . . . . . . . . . . . . . . . 62 6.2.3 Mapping from counts to millimeter . . . . . . . . . . . . . . . . . . . . . 62 6.2.4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 6.3 Device design and hardware implementation . . . . . . . . . . . . . . . . . . . . 64 6.3.1 Block diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6.3.2 Optode placement and circuit board dimensions . . . . . . . . . . . . . 64 6.3.3 Firmware description and measurement cycle . . . . . . . . . . . . . . . 66 6.3.4 Encapsulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 6.3.5 Fully assembled OPG device . . . . . . . . . . . . . . . . . . . . . . . . 67 6.4 Evaluation on the gesture recognition task . . . . . . . . . . . . . . . . . . . . . 69 6.4.1 Exercise selection, setup and recording . . . . . . . . . . . . . . . . . . . 69 6.4.2 Data corpus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6.4.3 Sequence pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 6.4.4 Choice of classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 6.4.5 Training and evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6.4.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 7 Improved device design with application in dysarthria therapy 7.1 Device design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 7.1.1 Design considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 7.1.2 General system overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.1.3 Intraoral sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 7.1.4 Receiver and controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 7.1.5 Multiplexer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 7.2 Hardware implementation . . . . . . . . . . . . . . . . . . . . .

Haptics: Science, Technology, Applications

This open access book constitutes the proceedings of the 12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020, held in Leiden, The Netherlands, in September 2020. The 60 papers presented in this volume were carefully reviewed and selected from 111 submissions. The were organized in topical sections on haptic science, haptic technology, and haptic applications. This year's focus is on accessibility

Cumulative index to NASA Tech Briefs, 1986-1990, volumes 10-14

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