Sensor based systems for quantification of sensorimotor function and rehabilitation of the upper limb

The thesis presents targeted sensor-based devices and methods for the training and assessment of upper extremity. These systems are all passive (non-actuated) thus intrinsically safe for (semi) independent use. An isometric assessment system is first presented, which uses a handle fixed on a force/torque sensor to investigate the force signal parameters and their relation to functional disability scales. The results from multiple sclerosis and healthy populations establish relation of isometric control and strength measures, its dependence on direction and how they are related to functional scales. The dissertation then introduces the novel platform MIMATE, Multimodal Interactive Motor Assessment and Training Environment, which is a wireless embedded platform for designing systems for training and assessing sensorimotor behaviour. MIMATE’s potential for designing clinically useful neurorehabilitation systems was demonstrated in a rehabilitation technology course. Based on MIMATE, intelligent objects (IObjects) are presented, which can measure position and force during training and assessing of manipulation tasks relevant to activities of daily living. A preliminary study with an IObject exhibits potential metrics and techniques that can be used to assess motor performance during fine manipulation tasks. The IObjects are part of the SITAR system, which is a novel sensor-based platform based on a force sensitive touchscreen and IObjects. It is used for training and assessment of sensorimotor deficits by focusing on meaningful functional tasks. Pilot assessment study with SITAR indicated a significant difference in performance of stroke and healthy populations during different sensorimotor tasks. Finally the thesis presents LOBSTER, a low cost, portable, bimanual self-trainer for exercising hand opening/closing, wrist flexion/extension or pronation/supination. The major novelty of the system relies on exploiting the movement of the unaffected limb to train the affected limb, making it safe for independent use. Study with LOBSTER will determine its usability for home based use.Open Acces

Bio-inspired robotic joint and manipulator : from biomechanical experimentation and modeling to human-like compliant finger design and control

textOne of the greatest challenges in controlling robotic hands is grasping and manipulating objects in unstructured and uncertain environments. Robotic hands are typically too rigid to react against unexpected impacts and disturbances in order to prevent damage. The human hands have great versatility and robustness due, in part, to the passive compliance and damping. Designing mechanical elements that are inspired by the nonlinear joint compliance of human hands is a promising solution to achieve human-like grasping and manipulation. However, the exact role of biomechanical elements in realizing joint stiffness is unknown. We conducted a series of experiments to investigate nonlinear stiffness and damping of the metacarpophalangeal (MCP) joint at the index finger. We designed a custom-made mechanism to integrate electromyography sensors (EMGs) and a motion capture system to collect data from 19 subjects. We investigated the relative contributions of muscle-tendon units and the MCP capsule ligament complex to joint stiffness with subject-specific modeling. The results show that the muscle-tendon units provide limited contribution to the passive joint compliance. This findings indicate that the parallel compliance, in the form of the capsule-ligament complex, is significant in defining the passive properties of the hand. To identify the passive damping, we used the hysteresis loops to investigate the energy dissipation function. We used symbolic regression and principal component analysis to derive and interpret the damping models. The results show that the nonlinear viscous damping depends on the cyclic frequency, and fluid and structural types of damping also exist at the MCP joint. Inspired by the nonlinear stiffness of the MCP joint, we developed a miniaturized mechanism that uses pouring liquid plastic to design energy storing elements. The key innovations in this design are: a) a set of nonlinear elasticity of compliant materials, b) variable pulley configurations to tune the stiffness profile, and c) pretension mechanism to scale the stiffness profile. The design exhibits human-like passive compliance. By taking advantage of miniaturized joint size and additive manufacturing, we incorporated the novel joint design in a novel robotic manipulator with six series elastic actuators (SEA). The robotic manipulator has passive joint compliance with the intrinsic property of human hands. To validate the system, we investigated the Cartesian stiffness of grasping with low-level force control. The results show that that the overall system performs a great force tracking with position feedback. The parallel compliance decreases the motor efforts and can stabilize the system.Mechanical Engineerin

Challenges in motor skill change: unraveling behavioral, cognitive and electrophysiological correlates of proactive interference

Changing automatized motor skills is a challenging endeavor. While often intended to raise or re-establish performance levels, this process is frequently associated with initial performance decrements. In this context, proactive interference has been theorized to play a particular role, as an already well-established and automatized procedural skill often hampers the acquisition or recall of the new target behavior. As a consequence, individuals must usually first overcome this interference which often renders skill change processes time-consuming and effortful. Despite its high practical relevance, there is only little research that has systematically investigated this topic so far. The aim of the present thesis was therefore to scrutinize the underlying mechanisms of proactive interference and its associated performance decrements in motor skill change. A multidisciplinary approach was pursued by empirically examining several individual and task-related factors which have been hypothesized to affect the amount of interference. To this end, a novel experimental paradigm was established which addressed the highly automatized motor skill of typing on a computer keyboard and that allowed to induce interference intentionally via different types of rule changes. In four experimental studies, including behavioral assessments, cognitive tests, eye-tracking and electroencephalography, several factors were identified to be associated with successful interference control in motor tasks: age, proficiency and prepotent response inhibition. Furthermore, there was a tendency towards a benefit of a motor restriction that limits individual motor degrees of freedom which might function as a potential inhibition support. These results provide first insights into the cognitive and electrophysiological mechanisms underlying motor skill modifications which in the long run might help to optimize motor skill change processes

An adaptive 4-week robotic training program of the upper limb for persons with multiple sclerosis

It is suggested that repetitive movements can initiate motor recovery and improve motor learning in populations with neurological impairments and this process can be optimized with robotic devices. The repetitive, reproducible and high dose motor movements that can be delivered by robotics have shown positive results in functional outcomes in stroke patients. However, there is little research on robotic neurorehabilitation for persons with multiple sclerosis (PwMS), more specifically there is lack of literature with focus on the upper extremity. Therefore, the purpose of this work was to use a robotic device to implement an adaptive training program of the forearm and wrist for PwMS. This approach is unique, as it incorporates real time learning from the robotic device to alter the level of assistance/resistance to the individual. This methodology is novel and could prove to be an effective way to properly individualize the therapy process with correct dosage and prescription. 7 individuals with varying levels of MS, placed their most affected limb (forearm) on a robotic device (Wristbot), grasped the handle, and using real-time visual feedback, traced a Lissajous curve allowing the wrist to move in flexion/extension, radial/ulnar directions. Robotic training occurred 3 times per week for 4 consecutive weeks and included 40 minutes of work. Robotic software was adaptive and updated every 3 laps to evaluate the average kinematic performance which modified the robotic assistance/resistance. Outcome measures were taken pre and post intervention. Improvements in performance were quantified by average tracking and figural error, which was significantly reduced from pre – post intervention. Isometric wrist strength and grip force endurance also significantly improved from pre to post intervention. However, maximum grip force, joint position matching, 9-hole peg test, and patient-rated wrist evaluation did not show any significant improvements. To our knowledge, this study was the first adaptive and individualized robotic rehabilitation program providing two opposing forces to the hand/wrist for PwMS. Results of this 4-week training intervention, provide a proof-of-concept that motor control and muscular strength can be improved by this rehabilitation modality. This work acts as a stepping-stone into future investigations of robotic rehabilitation for an MS population

Rethinking Productivity in Software Engineering

Get the most out of this foundational reference and improve the productivity of your software teams. This open access book collects the wisdom of the 2017 "Dagstuhl" seminar on productivity in software engineering, a meeting of community leaders, who came together with the goal of rethinking traditional definitions and measures of productivity. The results of their work, Rethinking Productivity in Software Engineering, includes chapters covering definitions and core concepts related to productivity, guidelines for measuring productivity in specific contexts, best practices and pitfalls, and theories and open questions on productivity. You'll benefit from the many short chapters, each offering a focused discussion on one aspect of productivity in software engineering. Readers in many fields and industries will benefit from their collected work. Developers wanting to improve their personal productivity, will learn effective strategies for overcoming common issues that interfere with progress. Organizations thinking about building internal programs for measuring productivity of programmers and teams will learn best practices from industry and researchers in measuring productivity. And researchers can leverage the conceptual frameworks and rich body of literature in the book to effectively pursue new research directions. What You'll Learn Review the definitions and dimensions of software productivity See how time management is having the opposite of the intended effect Develop valuable dashboards Understand the impact of sensors on productivity Avoid software development waste Work with human-centered methods to measure productivity Look at the intersection of neuroscience and productivity Manage interruptions and context-switching Who Book Is For Industry developers and those responsible for seminar-style courses that include a segment on software developer productivity. Chapters are written for a generalist audience, without excessive use of technical terminology. ; Collects the wisdom of software engineering thought leaders in a form digestible for any developer Shares hard-won best practices and pitfalls to avoid An up to date look at current practices in software engineering productivit