Automation of motor dexterity assessment

Motor dexterity assessment is regularly performed in rehabilitation wards to establish patient status and automatization for such routinary task is sought. A system for automatizing the assessment of motor dexterity based on the Fugl-Meyer scale and with loose restrictions on sensing technologies is presented. The system consists of two main elements: 1) A data representation that abstracts the low level information obtained from a variety of sensors, into a highly separable low dimensionality encoding employing t-distributed Stochastic Neighbourhood Embedding, and, 2) central to this communication, a multi-label classifier that boosts classification rates by exploiting the fact that the classes corresponding to the individual exercises are naturally organized as a network. Depending on the targeted therapeutic movement class labels i.e. exercises scores, are highly correlated-patients who perform well in one, tends to perform well in related exercises-; and critically no node can be used as proxy of others - an exercise does not encode the information of other exercises. Over data from a cohort of 20 patients, the novel classifier outperforms classical Naive Bayes, random forest and variants of support vector machines (ANOVA: p <; 0.001). The novel multi-label classification strategy fulfills an automatic system for motor dexterity assessment, with implications for lessening therapist's workloads, reducing healthcare costs and providing support for home-based virtual rehabilitation and telerehabilitation alternatives

Assessment of manual dexterity in VR: Towards a fully automated version of the box and blocks test

© 2019 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). In recent years, the possibility of using serious gaming technology for the automation of clinical procedures for assessment of motor function have captured the interest of the research community. In this paper, a virtual version of the Box and Blocks Test (BBT) for manual dexterity assessment is presented. This game-like system combines the classical BBT mechanics with a play-centric approach to accomplish a fully automated test for assessing hand motor function, making it more accessible and easier to administer. Additionally, some variants of the traditional mechanics are proposed in order to fully exploit the advantages of the chosen technology. This ongoing research aims to provide the clinical practitioners with a customisable, intuitive, and reliable tool for the assessment and rehabilitation of hand motor function

Assessment of Manual Dexterity in VR: Towards a Fully Automated Version of the Box and Blocks Test

Proceeding of The 27th Australian National Health Informatics Conference (HIC 2019), 12-14 August 2019, Melbourne, AustraliaIn recent years, the possibility of using serious gaming technology for the automation of clinical procedures for assessment of motor function have captured the interest of the research community. In this paper, a virtual version of the Box and Blocks Test (BBT) for manual dexterity assessment is presented. This game-like system combines the classical BBT mechanics with a play-centric approach to accomplish a fully automated test for assessing hand motor function, making it more accessible and easier to administer. Additionally, some variants of the traditional mechanics are proposed in order to fully exploit the advantages of the chosen technology. This ongoing research aims to provide the clinical practitioners with a customisable, intuitive, and reliable tool for the assessment and rehabilitation of hand motor function.Work funded by the Spanish Ministry of Economy and Competitiveness (ROBOESPAS project DPI2017-87562-C2-1-R and mobility grant EST2019-013090), and by the RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (S2018/NMT-4331).Publicad

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

NASA Space Human Factors Program

This booklet briefly and succinctly treats 23 topics of particular interest to the NASA Space Human Factors Program. Most articles are by different authors who are mainly NASA Johnson or NASA Ames personnel. Representative topics covered include mental workload and performance in space, light effects on Circadian rhythms, human sleep, human reasoning, microgravity effects and automation and crew performance

Robot Autonomy for Surgery

Autonomous surgery involves having surgical tasks performed by a robot operating under its own will, with partial or no human involvement. There are several important advantages of automation in surgery, which include increasing precision of care due to sub-millimeter robot control, real-time utilization of biosignals for interventional care, improvements to surgical efficiency and execution, and computer-aided guidance under various medical imaging and sensing modalities. While these methods may displace some tasks of surgical teams and individual surgeons, they also present new capabilities in interventions that are too difficult or go beyond the skills of a human. In this chapter, we provide an overview of robot autonomy in commercial use and in research, and present some of the challenges faced in developing autonomous surgical robots

New Tasks in Old Jobs: Drivers of Change and Implications for Job Quality

This overview report summarises the findings of 20 case studies looking at recent changes in the task content of five manufacturing occupations (car assemblers, meat processing workers, hand-packers, chemical products plant and machine operators and inspection engineers) as a result of factors such as digital transformations, globalisation and offshoring, increasing demand for high quality standards and sustainability. It also discusses some implications in terms of job quality and working life. The study reveals that the importance of physical tasks in manufacturing is generally declining due to automation; that more intensive use of digitally controlled equipment, together with increasing importance of quality standards, involve instead a growing amount of intellectual tasks for manual industrial workers; and that the amount of routine task content is still high in the four manual occupations studied. Overall, the report highlights how qualitative contextual information can complement existing quantitative data, offering a richer understanding of changes in the content and nature of jobs

Detection of bimanual gestures everywhere: why it matters, what we need and what is missing

Bimanual gestures are of the utmost importance for the study of motor coordination in humans and in everyday activities. A reliable detection of bimanual gestures in unconstrained environments is fundamental for their clinical study and to assess common activities of daily living. This paper investigates techniques for a reliable, unconstrained detection and classification of bimanual gestures. It assumes the availability of inertial data originating from the two hands/arms, builds upon a previously developed technique for gesture modelling based on Gaussian Mixture Modelling (GMM) and Gaussian Mixture Regression (GMR), and compares different modelling and classification techniques, which are based on a number of assumptions inspired by literature about how bimanual gestures are represented and modelled in the brain. Experiments show results related to 5 everyday bimanual activities, which have been selected on the basis of three main parameters: (not) constraining the two hands by a physical tool, (not) requiring a specific sequence of single-hand gestures, being recursive (or not). In the best performing combination of modeling approach and classification technique, five out of five activities are recognized up to an accuracy of 97%, a precision of 82% and a level of recall of 100%.Comment: Submitted to Robotics and Autonomous Systems (Elsevier

Space Applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS), phase 2. Volume 1: Telepresence technology base development

The field of telepresence is defined, and overviews of those capabilities that are now available, and those that will be required to support a NASA telepresence effort are provided. Investigation of NASA's plans and goals with regard to telepresence, extensive literature search for materials relating to relevant technologies, a description of these technologies and their state of the art, and projections for advances in these technologies over the next decade are included. Several space projects are examined in detail to determine what capabilities are required of a telepresence system in order to accomplish various tasks, such as servicing and assembly. The key operational and technological areas are identified, conclusions and recommendations are made for further research, and an example developmental program is presented, leading to an operational telepresence servicer