Active Training and Assistance Device for an Individually Adaptable Strength and Coordination Training

Das Altern der Weltbevölkerung, insbesondere in der westlichen Welt, stellt die Menschheit vor eine große Herausforderung. Zu erwarten sind erhebliche Auswirkungen auf den Gesundheitssektor, der im Hinblick auf eine steigende Anzahl von Menschen mit altersbedingtem körperlichem und kognitivem Abbau und dem damit erhöhten Bedürfnis einer individuellen Versorgung vor einer großen Aufgabe steht. Insbesondere im letzten Jahrhundert wurden viele wissenschaftliche Anstrengungen unternommen, um Ursache und Entwicklung altersbedingter Erkrankungen, ihr Voranschreiten und mögliche Behandlungen, zu verstehen. Die derzeitigen Modelle zeigen, dass der entscheidende Faktor für die Entwicklung solcher Krankheiten der Mangel an sensorischen und motorischen Einflüssen ist, diese wiederum sind das Ergebnis verringerter Mobilität und immer weniger neuer Erfahrungen. Eine Vielzahl von Studien zeigt, dass erhöhte körperliche Aktivität einen positiven Effekt auf den Allgemeinzustand von älteren Erwachsenen mit leichten kognitiven Beeinträchtigungen und den Menschen in deren unmittelbarer Umgebung hat. Diese Arbeit zielt darauf ab, älteren Menschen die Möglichkeit zu bieten, eigenständig und sicher ein individuelles körperliches Training zu absolvieren. In den letzten zwei Jahrzehnten hat die Forschung im Bereich der robotischen Bewegungsassistenten, auch Smarte Rollatoren genannt, den Fokus auf die sensorische und kognitive Unterstützung für ältere und eingeschränkte Personen gesetzt. Durch zahlreiche Bemühungen entstand eine Vielzahl von Ansätzen zur Mensch-Rollator-Interaktion, alle mit dem Ziel, Bewegung und Navigation innerhalb der Umgebung zu unterstützen. Aber trotz allem sind Trainingsmöglichkeiten zur motorischen Aktivierung mittels Smarter Rollatoren noch nicht erforscht. Im Gegensatz zu manchen Smarten Rollatoren, die den Fokus auf Rehabilitationsmöglichkeiten für eine bereits fortgeschrittene Krankheit setzen, zielt diese Arbeit darauf ab, kognitive Beeinträchtigungen in einem frühen Stadium soweit wie möglich zu verlangsamen, damit die körperliche und mentale Fitness des Nutzers so lang wie möglich aufrechterhalten bleibt. Um die Idee eines solchen Trainings zu überprüfen, wurde ein Prototyp-Gerät namens RoboTrainer-Prototyp entworfen, eine mobile Roboter-Plattform, die mit einem zusätzlichen Kraft-Momente-Sensor und einem Fahrradlenker als Eingabe-Schnittstelle ausgestattet wurde. Das Training beinhaltet vordefinierte Trainingspfade mit Markierungen am Boden, entlang derer der Nutzer das Gerät navigieren soll. Der Prototyp benutzt eine Admittanzgleichung, um seine Geschwindigkeit anhand der Eingabe des Nutzers zu berechnen. Desweiteren leitet das Gerät gezielte Regelungsaktionen bzw. Verhaltensänderungen des Roboters ein, um das Training herausfordernd zu gestalten. Die Pilotstudie, die mit zehn älteren Erwachsenen mit beginnender Demenz durchgeführt wurde, zeigte eine signifikante Steigerung ihrer Interaktionsfähigkeit mit diesem Gerät. Sie bewies ebenfalls den Nutzen von Regelungsaktionen, um die Komplexität des Trainings ständig neu anzupassen. Obwohl diese Studie die Durchführbarkeit des Trainings zeigte, waren Grundfläche und mechanische Stabilität des RoboTrainer-Prototyps suboptimal. Deswegen fokussiert sich der zweite Teil dieser Arbeit darauf, ein neues Gerät zu entwerfen, um die Nachteile des Prototyps zu beheben. Neben einer erhöhten mechanischen Stabilität, ermöglicht der RoboTrainer v2 eine Anpassung seiner Grundfläche. Dieses spezifische Merkmal der Smarten Rollatoren dient vor allem dazu, die Unterstützungsfläche für den Benutzer anzupassen. Das ermöglicht einerseits ein agiles Training mit gesunden Personen und andererseits Rehabilitations-Szenarien bei Menschen, die körperliche Unterstützung benötigen. Der Regelungsansatz für den RoboTrainer v2 erweitert den Admittanzregler des Prototypen durch drei adaptive Strategien. Die erste ist die Anpassung der Sensitivität an die Eingabe des Nutzers, abhängig von der Stabilität des Nutzer-Rollater-Systems, welche Schwankungen verhindert, die dann passieren können, wenn die Hände des Nutzers versteifen. Die zweite Anpassung beinhaltet eine neuartige nicht-lineare, geschwindigkeits-basierende Änderung der Admittanz-Parameter, um die Wendigkeit des Rollators zu erhöhen. Die dritte Anpassung erfolgt vor dem eigentlichen Training in einem Parametrierungsprozess, wo nutzereigene Interaktionskräfte gemessen werden, um individuelle Reglerkonstanten fein abzustimmen und zu berechnen. Die Regelungsaktionen sind Verhaltensänderungen des Gerätes, die als Bausteine für unterstützende und herausfordernde Trainingseinheiten mit dem RoboTrainer dienen. Sie nutzen das virtuelle Kraft-Feld-Konzept, um die Bewegung des Gerätes in der Trainingsumgebung zu beeinflussen. Die Bewegung des RoboTrainers wird in der Gesamtumgebung durch globale oder, in bestimmten Teilbereichen, durch räumliche Aktionen beeinflusst. Die Regelungsaktionen erhalten die Absicht des Nutzers aufrecht, in dem sie eine unabhängige Admittanzdynamik implementieren, um deren Einfluss auf die Geschwindigkeit des RoboTrainers zu berechnen. Dies ermöglicht die entscheidende Trennung von Reglerzuständen, um während des Trainings passive und sichere Interaktionen mit dem Gerät zu erreichen. Die oben genannten Beiträge wurden getrennt ausgewertet und in zwei Studien mit jeweils 22 bzw. 13 jungen, gesunden Erwachsenen untersucht. Diese Studien ermöglichen einen umfassenden Einblick in die Zusammenhänge zwischen unterschiedlichen Funktionalitäten und deren Einfluss auf die Nutzer. Sie bestätigen den gesamten Ansatz, sowie die gemachten Vermutungen im Hinblick auf die Gestaltung einzelner Teile dieser Arbeit. Die Einzelergebnisse dieser Arbeit resultieren in einem neuartigen Forschungsgerät für physische Mensch-Roboter-Interaktionen während des Trainings mit Erwachsenen. Zukünftige Forschungen mit dem RoboTrainer ebnen den Weg für Smarte Rollatoren als Hilfe für die Gesellschaft im Hinblick auf den bevorstehenden demographischen Wandel

Interaction Dynamics in Oscillator and Human-in-the-loop Systems.

This dissertation addresses control system analysis and system identification in three areas: error propagation in synchronization of harmonic oscillators, modeling of human active movement, and identification of human control strategies in manual pursuit tracking. 1) While most studies of synchronization in oscillator systems have focused on the existence of synchronous solutions in steady state, many problems pertaining to the transient dynamics have not been fully resolved. We extend the well-established theory of fundamental limitations to study the transient error propagation (string stability) in a string of synchronized harmonic oscillators. We first translate design requirements in terms of time-domain response and hardware limitations into a set of constraints on closed-loop frequency response. We further capture the conflict between string stability on the one hand and time-domain design requirements and hardware limitations on the other through a new Bode integral. 2) Modeling human active movement is a challenging problem not only because muscle has very sophisticated and highly nonlinear dynamics but also because neural and other signals internal to the body are difficult to observe directly. We seek a simple yet general and competent model to describe active movement in object manipulation tasks. Inspired by the Norton equivalent circuit in electrical engineering, we build a model based on the motion and force/torque signals that may be observed at the points of contact between the human body and the environment. The model consists of a motion source to represent a human's motor plan and a spring-mass-damper coupler to capture the time-varying driving point impedance of the human hand. The model is validated using occasional experimental trials in which a participant experiences unexpected loads in a grasp and twist task. 3) Although a large amount of literature has provided methods to identify feedback control in manual tracking tasks, very little research has been undertaken to experimentally identify feedforward control. We capitalize on the theory of fundamental limitations to study the link between a human's ability to simultaneously reject disturbances and perform pursuit tracking. We further develop an identification method to separate human feedback and feedforward control strategies in sinusoidal tracking tasks.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108853/1/ybo_1.pd

Approaches to functional electrical stimulation induced cycling and application for the child with a spinal cord injury

In the hope of more compact and user-friendly approaches to FES-cycling through the incorporation of modern sensor and computing technology, two new hip-angle-based strategies (both of which utilise a limb-mounted sensor) and a “traditional” crank-angle-based strategy have been developed and incorporated into a PDA-based multi-functional FES system. Through both simulation and tricycle-based experiments, all three approaches have been shown to provide practical stimulation activation timing. The second research focus concerns the development of two FES-cycling systems which are suitable for a spinal cord injured child, and methods to facilitate the intended use of both devices. A standard child’s tricycle has been modified with appropriate instrumentation for FES-cycling and testing involving its target population was carried out at a US-based paediatric research hospital. These experiments culminated in the demonstration of FES-cycling by an untrained seven year old T4/T6 (motor complete) subject, and the evolution of the device into one which should be able to meet the specific needs of spinal cord injured children. A second system with integrated motor has also been developed. As well as offering motor assistance, this device incorporates additional instrumentation to allow investigation into exercise and training capabilities. Experiments have been undertaken to validate this equipment and it is now ready for future pilot work involving paediatric subjects. The two research foci in this thesis represent what are, in our opinion, important routes that FES-cycling should take to progress into the home environment and also allow participation of a population who have potentially the most to gain from using it

Haptic Steering Interfaces for Semi-Autonomous Vehicles

Autonomous vehicles are predicted to significantly improve transportation quality by reducing traffic congestion, fuel expenditure and road accidents. However, until autonomous vehicles are reliable in all scenarios, human drivers will be asked to supervise automation behavior and intervene in automated driving when deemed necessary. Retaining the human driver in a strictly supervisory role, however, may make the driver complacent and reduce driver's situation awareness and driving skills which ironically, can further compromise the driver’s ability to intervene in safety-critical scenarios. Such issues can be alleviated by designing a human-automation interface that keeps the driver in-the-loop through constant interaction with automation and continuous feedback of automation's actions. This dissertation evaluates the utility of haptic feedback at the steering interface for enhancing driver awareness and enabling continuous human-automation interaction and performance improvement in semi-autonomous vehicles. In the first part of this dissertation, I investigate a driving scheme called Haptic Shared Control (HSC) in which the human driver and automation system share the steering control by simultaneously acting at the steering interface with finite mechanical impedances. I hypothesize that HSC can mitigate the human factors issues associated with semi-autonomous driving by allowing the human driver to continuously interact with automation and receive feedback about automation action. To test this hypothesis, I present two driving simulator experiments that are focused on the evaluation of HSC with respect to existing driving schemes during induced human and automation faults. In the first experiment, I compare obstacle avoidance performance of HSC with two existing control sharing schemes that support instantaneous transfers of control authority between human and automation. The results indicate that HSC outperforms both schemes in terms of obstacle avoidance, maneuvering efficiency, and driver engagement. In the second experiment, I consider emergency scenarios where I compare two HSC designs that provide high and low control authority to automation and an existing paradigm that decouples the driver input from the tires during collision avoidance. Results show that decoupling the driver invokes out-of-the-loop issues and misleads drivers to believe that they are in control. I also discover a `fault protection tradeoff': as the control authority provided to one agent increases, the protection against that agent's faults provided by the other agent reduces. In the second part of this dissertation, I focus on the problem of estimating haptic feedback from the road, or the road feedback. Road feedback is critical to making the driver aware of the state of the vehicle and road conditions, and its estimates are used in a variety of driver assist systems. However, conventional estimators only estimate road feedback on flat roads. To overcome this issue, I develop three estimators that enable road feedback estimation on uneven roads. I test and compare the performance of the three estimators by performing driving experiments on uneven roads such as road slopes and cleats. In the final part of this dissertation, I shift focus from physical human-automation interaction to human-human interaction. I present the evidence from the literature demonstrating that haptic feedback improves the performance of two humans physically collaborating on a shared task. I develop a control-theoretic model for haptic communication that can describe the mechanism by which haptic interaction facilitates performance improvement. The model creates a promising means to transfer the obtained insights to design robots or automation systems that can collaborate more efficiently with humans.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169975/1/akshaybh_1.pd

Kinematics and Robot Design IV, KaRD2021

This volume collects the papers published on the special issue “Kinematics and Robot Design IV, KaRD2021” (https://www.mdpi.com/journal/robotics/special_issues/KaRD2021), which is the forth edition of the KaRD special-issue series, hosted by the open-access journal “MDPI Robotics”. KaRD series is an open environment where researchers can present their works and discuss all the topics focused on the many aspects that involve kinematics in the design of robotic/automatic systems. Kinematics is so intimately related to the design of robotic/automatic systems that the admitted topics of the KaRD series practically cover all the subjects normally present in well-established international conferences on “mechanisms and robotics”. KaRD2021, after the peer-review process, accepted 12 papers. The accepted papers cover some theoretical and many design/applicative aspects

Safe Haptics-enabled Patient-Robot Interaction for Robotic and Telerobotic Rehabilitation of Neuromuscular Disorders: Control Design and Analysis

Motivation: Current statistics show that the population of seniors and the incidence rate of age-related neuromuscular disorders are rapidly increasing worldwide. Improving medical care is likely to increase the survival rate but will result in even more patients in need of Assistive, Rehabilitation and Assessment (ARA) services for extended periods which will place a significant burden on the world\u27s healthcare systems. In many cases, the only alternative is limited and often delayed outpatient therapy. The situation will be worse for patients in remote areas. One potential solution is to develop technologies that provide efficient and safe means of in-hospital and in-home kinesthetic rehabilitation. In this regard, Haptics-enabled Interactive Robotic Neurorehabilitation (HIRN) systems have been developed. Existing Challenges: Although there are specific advantages with the use of HIRN technologies, there still exist several technical and control challenges, e.g., (a) absence of direct interactive physical interaction between therapists and patients; (b) questionable adaptability and flexibility considering the sensorimotor needs of patients; (c) limited accessibility in remote areas; and (d) guaranteeing patient-robot interaction safety while maximizing system transparency, especially when high control effort is needed for severely disabled patients, when the robot is to be used in a patient\u27s home or when the patient experiences involuntary movements. These challenges have provided the motivation for this research. Research Statement: In this project, a novel haptics-enabled telerobotic rehabilitation framework is designed, analyzed and implemented that can be used as a new paradigm for delivering motor therapy which gives therapists direct kinesthetic supervision over the robotic rehabilitation procedure. The system also allows for kinesthetic remote and ultimately in-home rehabilitation. To guarantee interaction safety while maximizing the performance of the system, a new framework for designing stabilizing controllers is developed initially based on small-gain theory and then completed using strong passivity theory. The proposed control framework takes into account knowledge about the variable biomechanical capabilities of the patient\u27s limb(s) in absorbing interaction forces and mechanical energy. The technique is generalized for use for classical rehabilitation robotic systems to realize patient-robot interaction safety while enhancing performance. In the next step, the proposed telerobotic system is studied as a modality of training for classical HIRN systems. The goal is to first model and then regenerate the prescribed kinesthetic supervision of an expert therapist. To broaden the population of patients who can use the technology and HIRN systems, a new control strategy is designed for patients experiencing involuntary movements. As the last step, the outcomes of the proposed theoretical and technological developments are translated to designing assistive mechatronic tools for patients with force and motion control deficits. This study shows that proper augmentation of haptic inputs can not only enhance the transparency and safety of robotic and telerobotic rehabilitation systems, but it can also assist patients with force and motion control deficiencies

Haptic Guidance for Extended Range Telepresence

A novel navigation assistance for extended range telepresence is presented. The haptic information from the target environment is augmented with guidance commands to assist the user in reaching desired goals in the arbitrarily large target environment from the spatially restricted user environment. Furthermore, a semi-mobile haptic interface was developed, one whose lightweight design and setup configuration atop the user provide for an absolutely safe operation and high force display quality

Technology-supported training of arm-hand skills in stroke

Impaired arm-hand performance is a serious consequence of stroke that is associated with reduced self-efficacy and poor quality of life. Task-oriented arm training is a therapy approach that is known to improve skilled arm-hand performance, even in chronic stages after stroke. At the start of this project, little knowledge had been consolidated regarding taskoriented arm training characteristics, especially in the field of technology-supported rehabilitation. The feasibility and effects of technology-supported client-centred task-oriented training on skilled arm-hand performance had not been investigated but to a very limited degree. Reviewing literature on rehabilitation and motor learning in stroke led to the identification of therapy oriented criteria for rehabilitation technology aiming to influence skilled arm-hand performance (chapter 2). Most rehabilitation systems reported in literature to date are robotic systems that are aimed at providing an engaging exercise environment and feedback on motor performance. Both, feedback and engaging exercises are important for motivating patients to perform a high number of exercise repetitions and prolonged training, which are important factors for motor learning. The review also found that current rehabilitation technology is focussed mainly on providing treatment at a function level, thereby improving joint range of motion, muscle strength and parameters such as movement speed and smoothness of movement during analytical movements. However, related research has found no effects of robot-supported training at the activity level. The review concluded that a challenge exists for upper extremity rehabilitation technology in stroke patients to also provide more patienttailored task-oriented arm-hand training in natural environments to support the learning of skilled arm-hand performance. Besides mapping the strengths of different technological solutions, the use of outcome measures and training protocols needs to become more standardized across similar interventions, in order to help determine which training solutions are most suitable for specific patient categories. Chapter 4 contributes towards such a standardization of outcome measurement. A concept is introduced which may guide the clinician/researcher to choose outcome measures for evaluating specific and generalized training effects. As an initial operationalization of this concept, 28 test batteries that have been used in 16 task-oriented training interventions were rated as to whether measurement components were measured by the test. Future research is suggested that elaborates the concept with information on the relative weighing of components in each test, with more test batteries (which may lead to additional components) and by adding more test properties into the concept (e.g. psychometric properties of the tests, possible floor- or ceiling effects). Task-oriented training is one of the training approaches that has been shown to be beneficial for skilled arm-hand performance after stroke. Important mechanisms for motor learning that are identified are patient motivation for such training, and the learning of efficient goaloriented movement strategies and task-specific problem solving. In this thesis we operationalize task-oriented training in terms of 15 components (chapter 3). A systematic review that included 16 randomized controlled trials using task-oriented training in stroke patients, evaluated the effects of these training components on skilled arm-hand performance. The number of training components used in an intervention aimed at improving arm-hand performance after stroke was not associated with the post-treatment effect size. Distributed practice and feedback were associated with the largest post-intervention effect sizes. Random practice and use of clear functional training goals were associated with the largest follow-up effect sizes. It may be that training components that optimize the storage of learned motor performance in the long-term memory are associated with larger treatment effects. Unfortunately, feedback, random practice and distributed practice were reported in very few of the included randomized controlled trials (in only 6,3 and 1 out of the 17 studies respectively). Client-centred training, i.e. training on exercises that support goals that are selected by the patients themselves, improves patient motivation for training. Motivation in turn has proven to positively influence motor learning in stroke patients, as attention during training is heightened and storage of information in the long-term memory improves. Chapter 5 reports on an interview of 40 stroke patients, investigating into training preferences. A list of 46 skills, ranked according to descending training preference scores, was provided that can be used for implementation of exercises in rehabilitation technology, in order for technologysupported training to be client-centred. Chapter 6 introduces T-TOAT, a technology supported task-oriented arm training method that was developed together with colleagues at Adelante (Hoensbroek, NL). T-TOAT enables the implementation of exercises that support task-oriented training in rehabilitation technology. The training method is applicable for different technological systems, e.g. robot and sensor systems, or in combination with functional electrical stimulation, etc. To enable the use of TTOAT for training with the Haptic Master Robot (MOOG-FCS, NL), special software named Haptic TOAT was developed in Adelante together with colleagues at the Centre of Technology in Care of Zuyd University (chapter 6). The software enables the recording of the patient’s movement trajectories, given task constraints and patient possibilities, using the Haptic Master as a recording device. A purpose-made gimbal was attached to the endeffector, leaving the hand free for the use and manipulating objects. The recorded movement can be replayed in a passive mode or in an active mode (active, active-assisted or activeresisted). Haptic feedback is provided when the patient deviates from the recorded movement trajectory, as the patient receives the sensation of bouncing into a wall, as well as feeling a spring that pulls him/her back to the recorded path. The diameter of the tunnel around the recorded trajectory (distance to the wall), and the spring force can be adjusted for each patient. An ongoing clinical trial in which chronic stroke patients train with Haptic-TOAT examines whether Haptic Master provides additional value compared to supporting the same exercises by video-instruction only. Together with Philips Research Europe (Eindhoven,Aachen), the T-TOAT method has been implemented in a sensor based prototype, called Philips Stroke Rehabilitation Exerciser. This system included movement tracking sensors and an exercise board interacting with real life objects. A very strong feature of the system is that feedback is provided to patients (real-time and after exercise performance), based on a comparison of the patient’s exercise performance to individual targets set by the therapist. Chapter 7 reports on a clinical trial investigating arm-hand treatment outcome and patient motivation for technology-supported task-oriented training in chronic stroke patients. It was found that 8 weeks of T-TOAT training improved arm-hand performance in chronic stroke patients significantly on Fugl-Meyer, Action Research Arm Test, and Motor Activity Log. An improvement was found in health-related quality of life. Training effects lasted at least 6 months post-training. Participants reported feeling intrinsically motivated and competent to use the system. The results of this study showed that T-TOAT is feasible. Despite the small number of stroke patients tested (n=9), significant and clinically relevant improvements in skilled arm-hand performance were found. In conclusion, this thesis has made several contributions. It motivated the need for clientcentred task-oriented training, which it has operationalized in terms of 15 components. Four of these 15 components were identified as most beneficial for the patient. A prioritized inventory of arm-hand training preferences of stroke patients was compiled by means of an interview study of 40 subacute and chronic stroke patients. T-TOAT, a method for technology-supported, client-centred, task-oriented training, was conceived and implemented in two target technologies (Haptic Master and Philips Stroke Rehabilitation Exerciser). Its feasibility was demonstrated in a clinical trial showing substantial and durable benefits for the stroke patients. Finally, the thesis contributes towards the standardization of outcome measures which is necessary for charting progress and guiding future developments of technology-supported stroke rehabilitation. Methodological considerations were discussed and several suggestions for future research were presented. The variety of treatment approaches and the various ways of support and challenge that are offered by existing rehabilitation technologies hold a large potential for offering a variety of extra training opportunities to stroke patients that may improve their arm-hand performance. Such solutions will be of increasing importance, to alleviate therapists and reduce economic pressure on the health care system, as the stroke incidence is increasing rapidly over the coming decades

A virtual hand assessment system for efficient outcome measures of hand rehabilitation

Previously held under moratorium from 1st December 2016 until 1st December 2021.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control

Vehicle and Traffic Safety

The book is devoted to contemporary issues regarding the safety of motor vehicles and road traffic. It presents the achievements of scientists, specialists, and industry representatives in the following selected areas of road transport safety and automotive engineering: active and passive vehicle safety, vehicle dynamics and stability, testing of vehicles (and their assemblies), including electric cars as well as autonomous vehicles. Selected issues from the area of accident analysis and reconstruction are discussed. The impact on road safety of aspects such as traffic control systems, road infrastructure, and human factors is also considered