Robot-Aided Systems for Improving the Assessment of Upper Limb Spasticity: A Systematic Review.

Spasticity is a motor disorder that causes stiffness or tightness of the muscles and can interfere with normal movement, speech, and gait. Traditionally, the spasticity assessment is carried out by clinicians using standardized procedures for objective evaluation. However, these procedures are manually performed and, thereby, they could be influenced by the clinician's subjectivity or expertise. The automation of such traditional methods for spasticity evaluation is an interesting and emerging field in neurorehabilitation. One of the most promising approaches is the use of robot-aided systems. In this paper, a systematic review of systems focused on the assessment of upper limb (UL) spasticity using robotic technology is presented. A systematic search and review of related articles in the literature were conducted. The chosen works were analyzed according to the morphology of devices, the data acquisition systems, the outcome generation method, and the focus of intervention (assessment and/or training). Finally, a series of guidelines and challenges that must be considered when designing and implementing fully-automated robot-aided systems for the assessment of UL spasticity are summarized.

Validity of a Fully-Immersive VR-Based Version of the Box and Blocks Test for Upper Limb Function Assessment in Parkinson's Disease.

In recent decades, gaming technology has been accepted as a feasible method for complementing traditional clinical practice, especially in neurorehabilitation; however, the viability of using 3D Virtual Reality (VR) for the assessment of upper limb motor function has not been fully explored. For that purpose, we developed a VR-based version of the Box and Blocks Test (BBT), a clinical test for the assessment of manual dexterity, as an automated alternative to the classical procedure. Our VR-based BBT (VR-BBT) integrates the traditional BBT mechanics into gameplay using the Leap Motion Controller (LMC) to capture the user's hand motion and the Oculus Rift headset to provide a fully immersive experience. This paper focuses on evaluating the validity of our VR-BBT to reliably measure the manual dexterity in a sample of patients with Parkinson's Disease (PD). For this study, a group of twenty individuals in a mild to moderate stage of PD were recruited. Participants were asked to perform the physical BBT (once) and our proposed VR-BBT (twice) system, separately. Correlation analysis of collected data was carried out. Statistical analysis proved that the performance data collected by the VR-BBT significantly correlated with the conventional assessment of the BBT. The VR-BBT scores have shown a significant association with PD severity measured by the Hoehn and Yahr scale. This fact suggests that the VR-BBT could be used as a reliable indicator for health improvements in patients with PD. Finally, the VR-BBT system presented high usability and acceptability rated by clinicians and patients.

Generation and Processing of Simulated Underwater Images for Infrastructure Visual Inspection with UUVs.

The development of computer vision algorithms for navigation or object detection is one of the key issues of underwater robotics. However, extracting features from underwater images is challenging due to the presence of lighting defects, which need to be counteracted. This requires good environmental knowledge, either as a dataset or as a physic model. The lack of available data, and the high variability of the conditions, makes difficult the development of robust enhancement algorithms. A framework for the development of underwater computer vision algorithms is presented, consisting of a method for underwater imaging simulation, and an image enhancement algorithm, both integrated in the open-source robotics simulator UUV Simulator. The imaging simulation is based on a novel combination of the scattering model and style transfer techniques. The use of style transfer allows a realistic simulation of different environments without any prior knowledge of them. Moreover, an enhancement algorithm that successfully performs a correction of the imaging defects in any given scenario for either the real or synthetic images has been developed. The proposed approach showcases then a novel framework for the development of underwater computer vision algorithms for SLAM, navigation, or object detection in UUVs.

3D Exploration and Navigation with Optimal-RRT Planners for Ground Robots in Indoor Incidents.

Navigation and exploration in 3D environments is still a challenging task for autonomous robots that move on the ground. Robots for Search and Rescue missions must deal with unstructured and very complex scenarios. This paper presents a path planning system for navigation and exploration of ground robots in such situations. We use (unordered) point clouds as the main sensory input without building any explicit representation of the environment from them. These 3D points are employed as space samples by an Optimal-RRTplanner (RRT * ) to compute safe and efficient paths. The use of an objective function for path construction and the natural exploratory behaviour of the RRT * planner make it appropriate for the tasks. The approach is evaluated in different simulations showing the viability of autonomous navigation and exploration in complex 3D scenarios.

Correction of Visual Perception Based on Neuro-Fuzzy Learning for the Humanoid Robot TEO.

New applications related to robotic manipulation or transportation tasks, with or without physical grasping, are continuously being developed. To perform these activities, the robot takes advantage of different kinds of perceptions. One of the key perceptions in robotics is vision. However, some problems related to image processing makes the application of visual information within robot control algorithms difficult. Camera-based systems have inherent errors that affect the quality and reliability of the information obtained. The need of correcting image distortion slows down image parameter computing, which decreases performance of control algorithms. In this paper, a new approach to correcting several sources of visual distortions on images in only one computing step is proposed. The goal of this system/algorithm is the computation of the tilt angle of an object transported by a robot, minimizing image inherent errors and increasing computing speed. After capturing the image, the computer system extracts the angle using a Fuzzy filter that corrects at the same time all possible distortions, obtaining the real angle in only one processing step. This filter has been developed by the means of Neuro-Fuzzy learning techniques, using datasets with information obtained from real experiments. In this way, the computing time has been decreased and the performance of the application has been improved. The resulting algorithm has been tried out experimentally in robot transportation tasks in the humanoid robot TEO (Task Environment Operator) from the University Carlos III of Madrid.

Real Evaluations Tractability using Continuous Goal-Directed Actions in Smart City Applications.

One of the most important challenges of Smart City Applications is to adapt the system to interact with non-expert users. Robot imitation frameworks aim to simplify and reduce times of robot programming by allowing users to program directly through action demonstrations. In classical robot imitation frameworks, actions are modelled using joint or Cartesian space trajectories. They accurately describe actions where geometrical characteristics are relevant, such as fixed trajectories from one pose to another. Other features, such as visual ones, are not always well represented with these pure geometrical approaches. Continuous Goal-Directed Actions (CGDA) is an alternative to these conventional methods, as it encodes actions as changes of any selected feature that can be extracted from the environment. As a consequence of this, the robot joint trajectories for execution must be fully computed to comply with this feature-agnostic encoding. This is achieved using Evolutionary Algorithms (EA), which usually requires too many evaluations to perform this evolution step in the actual robot. The current strategies involve performing evaluations in a simulated environment, transferring only the final joint trajectory to the actual robot. Smart City applications involve working in highly dynamic and complex environments, where having a precise model is not always achievable. Our goal is to study the tractability of performing these evaluations directly in a real-world scenario. Two different approaches to reduce the number of evaluations using EA, are proposed and compared. In the first approach, Particle Swarm Optimization (PSO)-based methods have been studied and compared within the CGDA framework: naïve PSO, Fitness Inheritance PSO (FI-PSO), and Adaptive Fuzzy Fitness Granulation with PSO (AFFG-PSO). The second approach studied the introduction of geometrical and velocity constraints within the CGDA framework. The effects of both approaches were analyzed and compared in the "wax" and "paint" actions, two CGDA commonly studied use cases. Results from this paper depict an important reduction in the number of required evaluations.

Experimental Robot Model Adjustments Based on Force-Torque Sensor Information.

The computational complexity of humanoid robot balance control is reduced through the application of simplified kinematics and dynamics models. However, these simplifications lead to the introduction of errors that add to other inherent electro-mechanic inaccuracies and affect the robotic system. Linear control systems deal with these inaccuracies if they operate around a specific working point but are less precise if they do not. This work presents a model improvement based on the Linear Inverted Pendulum Model (LIPM) to be applied in a non-linear control system. The aim is to minimize the control error and reduce robot oscillations for multiple working points. The new model, named the Dynamic LIPM (DLIPM), is used to plan the robot behavior with respect to changes in the balance status denoted by the zero moment point (ZMP). Thanks to the use of information from force-torque sensors, an experimental procedure has been applied to characterize the inaccuracies and introduce them into the new model. The experiments consist of balance perturbations similar to those of push-recovery trials, in which step-shaped ZMP variations are produced. The results show that the responses of the robot with respect to balance perturbations are more precise and the mechanical oscillations are reduced without comprising robot dynamics.

Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour Classifier.

Objective assessment of motor function is an important component to evaluating the effectiveness of a rehabilitation process. Such assessments are carried out by clinicians using traditional tests and scales. The Box and Blocks Test (BBT) is one such scale, focusing on manual dexterity evaluation. The score is the maximum number of cubes that a person is able to displace during a time window. In a previous paper, an automated version of the Box and Blocks Test using a Microsoft Kinect sensor was presented, and referred to as the Automated Box and Blocks Test (ABBT). In this paper, the feasibility of ABBT as an automated tool for manual dexterity assessment is discussed. An algorithm, based on image segmentation in CIELab colour space and the Nearest Neighbour (NN) rule, was developed to improve the reliability of automatic cube counting. A pilot study was conducted to assess the hand motor function in people with Parkinson's disease (PD). Three functional assessments were carried out. The success rate in automatic cube counting was studied by comparing the manual (BBT) and the automatic (ABBT) methods. The additional information provided by the ABBT was analysed to discuss its clinical significance. The results show a high correlation between manual (BBT) and automatic (ABBT) scoring. The lowest average success rate in cube counting for ABBT was 92%. Additionally, the ABBT acquires extra information from the cubes' displacement, such as the average velocity and the time instants in which the cube was detected. The analysis of this information can be related to indicators of health status (coordination and dexterity). The results showed that the ABBT is a useful tool for automating the assessment of unilateral gross manual dexterity, and provides additional information about the user's performance.

Integrating Egocentric and Robotic Vision for Object Identification Using Siamese Networks and Superquadric Estimations in Partial Occlusion Scenarios.

This paper introduces a novel method that enables robots to identify objects based on user gaze, tracked via eye-tracking glasses. This is achieved without prior knowledge of the objects' categories or their locations and without external markers. The method integrates a two-part system: a category-agnostic object shape and pose estimator using superquadrics and Siamese networks. The superquadrics-based component estimates the shapes and poses of all objects, while the Siamese network matches the object targeted by the user's gaze with the robot's viewpoint. Both components are effectively designed to function in scenarios with partial occlusions. A key feature of the system is the user's ability to move freely around the scenario, allowing dynamic object selection via gaze from any position. The system is capable of handling significant viewpoint differences between the user and the robot and adapts easily to new objects. In tests under partial occlusion conditions, the Siamese networks demonstrated an 85.2% accuracy in aligning the user-selected object with the robot's viewpoint. This gaze-based Human-Robot Interaction approach demonstrates its practicality and adaptability in real-world scenarios.

A graphical tuning method for fractional order controllers based on iso-slope phase curves.

Fractional order controllers are widely used in the robust control field. As a generalization of the ubiquitous PID controllers, fractional order controllers are able to reach design specifications their integer counterparts cannot, and as a result they outperform them at particular situations. Their main drawback is that generalization of the design tools is not always evident, and therefore tuning this kind of controller is always a new and different challenge. Existing methods often use numerical computation to find the controller parameters that fit the specifications. This paper describes a graphical solution for fractional order controllers, which avoids the solution by nonlinear equations and helps designer to solve the control problem in a very intuitive way. This approach is tested in the servomotors of a real bio-inspired soft neck and results are compared with those obtained from other control strategies. The experiments show that the controller tuned by this method works as expected from a robust controller and that this approach is very competitive compared to other state of the art methods, while offering a more simplified and direct tuning process.

Test Bench for Evaluation of a Soft Robotic Link.

In this paper we describe the control approaches tested in the improved version of an existing soft robotic neck with two Degrees Of Freedom (DOF), able to achieve flexion, extension, and lateral bending movements similar to those of a human neck. The design is based on a cable-driven mechanism consisting of a spring acting as a cervical spine and three servomotor actuated tendons that let the neck to reach all desired postures. The prototype was manufactured using a 3D printer. Two control approaches are proposed and tested experimentally: a motor position approach using encoder feedback and a tip position approach using Inertial Measurement Unit (IMU) feedback, both applying fractional-order controllers. The platform operation is tested for different load configurations so that the robustness of the system can be checked.

A comparison of the growth performance, carcass traits, and behavior of guinea pigs reared in wire cages and floor pens for meat production.

To achieve efficient production of guinea pigs for meat, it is essential to determine the most suitable housing system. A total of 220 guinea pigs were maintained in either wire cages (n = 11, 10 animals per cage) or floor pens (n = 11, 10 animals per pen) containing a deep litter of woodchips, both housing systems having the same dimensions (2 × 1 × 0.4 m). Growth traits, food intake and feed conversion ratio were recorded weekly and expressed as the difference between the two groups. After 77 days, the animals were slaughtered, and carcass traits were evaluated. Growth performance and carcass trait parameters, as well as mortality and behavior trends, were not affected by the housing system type. Nonetheless, the use of wire cages is recommended for raising guinea pigs since water, urine and feces pass through the wire floor, resulting in cleaner animals.

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

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.

Effectiveness of Serious Games for Leap Motion on the Functionality of the Upper Limb in Parkinson's Disease: A Feasibility Study.

The design and application of Serious Games (SG) based on the Leap Motion sensor are presented as a tool to support the rehabilitation therapies for upper limbs. Initially, the design principles and their implementation are described, focusing on improving both unilateral and bilateral manual dexterity and coordination. The design of the games has been supervised by specialized therapists. To assess the therapeutic effectiveness of the proposed system, a protocol of trials with Parkinson's patients has been defined. Evaluations of the physical condition of the participants in the study, at the beginning and at the end of the treatment, are carried out using standard tests. The specific measurements of each game give the therapist more detailed information about the patients' evolution after finishing the planned protocol. The obtained results support the fact that the set of developed video games can be combined to define different therapy protocols and that the information obtained is richer than the one obtained through current clinical metrics, serving as method of motor function assessment.

Technical Note: Mobile accelerator guidance using an optical tracker during docking in IOERT procedures.

PURPOSE: Intraoperative electron radiation therapy (IOERT) involves the delivery of a high radiation dose during tumor resection in a shorter time than other radiation techniques, thus improving local control of tumors. However, a linear accelerator device is needed to produce the beam safely. Mobile linear accelerators have been designed as dedicated units that can be moved into the operating room and deliver radiation in situ. Correct and safe dose delivery is a key concern when using mobile accelerators. The applicator is commonly fixed to the patient's bed to ensure that the dose is delivered to the prescribed location, and the mobile accelerator is moved to dock the applicator to the radiation beam output (gantry). In a typical clinical set-up, this task is time-consuming because of safety requirements and the limited degree of freedom of the gantry. The objective of this study was to present a navigation solution based on optical tracking for guidance of docking to improve safety and reduce procedure time. METHOD: We used an optical tracker attached to the mobile linear accelerator to track the prescribed localization of the radiation collimator inside the operating room. Using this information, the integrated navigation system developed computes the movements that the mobile linear accelerator needs to perform to align the applicator and the radiation gantry and warns the physician if docking is unrealizable according to the available degrees of freedom of the mobile linear accelerator. Furthermore, we coded a software application that connects all the necessary functioning elements and provides a user interface for the system calibration and the docking guidance. RESULT: The system could safeguard against the spatial limitations of the operating room, calculate the optimal arrangement of the accelerator and reduce the docking time in computer simulations and experimental setups. CONCLUSIONS: The system could be used to guide docking with any commercial linear accelerator. We believe that the docking navigator we present is a major contribution to IOERT, where docking is critical when attempting to reduce surgical time, ensure patient safety and guarantee that the treatment administered follows the radiation oncologist's prescription.

DE-based tuning of PI(λ)D(µ) controllers.

A new method that relies on evolutionary computation concepts is proposed in this paper to tune the parameters of fractional order PI(λ)D(µ) controllers, in which the orders of the integral and derivative parts, λ and µ, respectively, are fractional. The main advantage of the fractional order controllers is that the increase in the number of parameters in the controller allows an increase in the number of control specifications that can be met. A Differential Evolution (DE) algorithm is proposed to make the controlled system fulfill different design specifications in time and frequency domains. This method is based on the minimization of a fitness function. Experiments have been carried out in simulation and in a real DC motor platform. The results illustrate the effectiveness of this method.

Usability assessment of ASIBOT: a portable robot to aid patients with spinal cord injury.

The usability concept refers to aspects related to the use of products that are closely linked to the user's degree of satisfaction. Our goal is to present a functional evaluation methodology for assessing the usability of sophisticated technical aids, such as a portable robot for helping disabled patients with severe spinal cord injuries. The specific manipulator used for this task is ASIBOT, a personal assistance robot totally developed by RoboticsLab at the University Carlos III of Madrid. Our purpose is also to improve some aspects of the manipulator according to the user's perception. For our case study, a population of six patients with spinal cord injury is considered. These patients have been suffering spinal cord injuries for a period of time longer than 1 year before the tests are carried out. The methodology followed for the information gathering is based on the Quebec User Evaluation of Satisfaction with assistive Technology (QUEST). Different daily functions, such as drinking, brushing one's teeth and washing one's face, are considered to assess the user's perception when using ASIBOT as a technical aid. The human factor in this procedure is the main base to establish the specific needs and tools to make the end product more suitable and usable.