Search

Service robotics is a fast-developing sector, requiring embedded intelligence into robotic platforms to interact with the humans and the surrounding environment. One of the main challenges in the field is robust and versatile manipulation in everyday life activities. An appealing opportunity to solve the mentioned task is to leverage on machine learning algorithms to let the robot adapt its behavior to unknown scenarios, defining the best strategy for a grasping and manipulation task in a trial and error process. In parallel, the mechanical design of compliant end-effectors able to adapt to the environment targets the same goal. These grippers represent a clever solution to the problems that arise when using sensorized hands with a high number of actuated joints, which are expensive and difficult to control. Within the described context, this work addresses the problem of finding the best position and joint configuration for a compliant, under-actuated, sensorless robotic hand to grasp an unforeseen deformable object based on collected RGB image and partial point cloud. A Bayesian approach to the problem of the optimization of the grasping strategy is proposed, enhancing it with transfer learning capabilities to exploit the acquired grasping knowledge to (partially) new objects. A PAL Robotics TIAGo (a mobile manipulator with a 7-degrees-of- freedom arm and an anthropomorphic underactuated compliant hand) has been used as a test platform, executing a pouring task while manipulating plastic (i.e., compliant) bottles. The sampling efficiency of the data-driven learning is shown, compared to an evenly spaced grid sampling of the input space. In addition, the generalization capability of the optimized model is tested (exploiting transfer learning) on a set of plastic bottles and other liquid containers, achieving a success rate of the 88%.

Additional file 1: Table S1. Summary of statistical tests performed, relative sample size, correction for multiple comparisons and resulting p-values. NS = Non-significant (p > 0.05); THDS = Tukey's Honestly Significant Difference correction for multiple comparisons.

In this work, we present and validate a novel inverse kinematics method for rehabilitation upper-limb exoskeletons that require joint coordination constraints. Starting from the conventional differential kinematics algorithm based on the inversion of the Jacobian matrix, we describe and test the improved algorithm based on the Projected-Gradient method, which takes into account inter-joint coordination constraints. The Harmony exoskeleton is used as a platform to demonstrate the method. In detail, we address the joint constraints needed to match anatomical shoulder movement and results show good performances of the proposed algorithm., https://youtu.be/x-EkavyAp_E

Aquatic exercises have been demonstrated to benefit the facilitation of motor recovery and the enhancement of well-being in middle-aged adults and the elderly. Personalization, immersiveness, and biofeedback are key for amplifying and accelerating any rehabilitation process in neurological and orthopedic patients. However, a therapist can neither properly visualize nor monitor rehabilitation exercises executed under water, nor can he/she measure them. Therefore, this paper aims to provide adaptive biofeedback during aquatic exercises in order to enhance the training’s effectiveness. A wearable biofeedback suit equipped with wearable underwater-resistant sensor nodes has been designed, produced, and tested. A dedicated algorithm for quantitatively extracting joint angles has been developed and validated against the optical tracking system. Multiple biofeedback modalities are proposed based on visual feedback: amplitude control with set target angles; velocity-amplitude control with set target angles and angular velocity; and velocity tutor with set target angles, a frequency value, and a rest period. Joint angles estimated using the sensor network are compared to those estimated using an optical tracking system with the root-mean-squared angle error between the two systems ranging from 4.0° to 6.3° and a significant correlation coefficient that is always greater than 0.99. Pilot tests during aquatic exercises executed in a thermal environment demonstrate the feasibility and usability of the complete system in the final working environment. The relevant angles are correctly calculated and monitored online during the exercises, and the tested subjects understand the implemented biofeedback modalities easily and follow them well as the SUS evaluation indicates.

Recent literature analysis showed the need for upper-limb assistive devices suitable for patients affected by neuromuscular pathologies. This work presents a novel upper-limb robotic exoskeleton specifically designed for muscular dystrophy patients. The exoskeleton is a 4 degrees of freedom robotic device that supports movement at the shoulder and elbow. The system can be controlled by the user in the task space by driving a joystick or by means of speech recognition. The device has been field-tested on 5 dystrophic patients with the goal of evaluating performances, comfort, safety and usability of the device during daily life activities. Quantitative results on upper-limb functional performances with and without the exoskeleton are presented showing beneficial effects. Usability of the device has been evaluated as good, suggesting promising future developments., https://youtu.be/UMR-OTtNTQE

Background: MUNDUS is an assistive framework for recovering direct interaction capability of severely motor impaired people based on arm reaching and hand functions. It aims at achieving personalization, modularity and maximization of the user’s direct involvement in assistive systems. To this, MUNDUS exploits any residual control of the end-user and can be adapted to the level of severity or to the progression of the disease allowing the user to voluntarily interact with the environment. MUNDUS target pathologies are high-level spinal cord injury (SCI) and neurodegenerative and genetic neuromuscular diseases, such as amyotrophic lateral sclerosis, Friedreich ataxia, and multiple sclerosis (MS). The system can be alternatively driven by residual voluntary muscular activation, head/eye motion, and brain signals. MUNDUS modularly combines an antigravity lightweight and non-cumbersome exoskeleton, closed-loop controlled Neuromuscular Electrical Stimulation for arm and hand motion, and potentially a motorized hand orthosis, for grasping interactive objects. Methods: The definition of the requirements and of the interaction tasks were designed by a focus group with experts and a questionnaire with 36 potential end-users. Five end-users (3 SCI and 2 MS) tested the system in the configuration suitable to their specific level of impairment. They performed two exemplary tasks: reaching different points in the working volume and drinking. Three experts evaluated over a 3-level score (from 0, unsuccessful, to 2, completely functional) the execution of each assisted sub-action. Results: The functionality of all modules has been successfully demonstrated. User’s intention was detected with a 100% success. Averaging all subjects and tasks, the minimum evaluation score obtained was 1.13 ± 0.99 for the release of the handle during the drinking task, whilst all the other sub-actions achieved a mean value above 1.6. All users, but one, subjectively perceived the usefulness of the assistance and could easily control the system. Donning time ranged from 6 to 65 minutes, scaled on the configuration complexity. Conclusions: The MUNDUS platform provides functional assistance to daily life activities; the modules integration depends on the user’s need, the functionality of the system have been demonstrated for all the possible configurations, and preliminary assessment of usability and acceptance is promising.

Background MUNDUS is an assistive framework for recovering direct interaction capability of severely motor impaired people based on arm reaching and hand functions. It aims at achieving personalization, modularity and maximization of the user’s direct involvement in assistive systems. To this, MUNDUS exploits any residual control of the end-user and can be adapted to the level of severity or to the progression of the disease allowing the user to voluntarily interact with the environment. MUNDUS target pathologies are high-level spinal cord injury (SCI) and neurodegenerative and genetic neuromuscular diseases, such as amyotrophic lateral sclerosis, Friedreich ataxia, and multiple sclerosis (MS). The system can be alternatively driven by residual voluntary muscular activation, head/eye motion, and brain signals. MUNDUS modularly combines an antigravity lightweight and non-cumbersome exoskeleton, closed-loop controlled Neuromuscular Electrical Stimulation for arm and hand motion, and potentially a motorized hand orthosis, for grasping interactive objects. Methods The definition of the requirements and of the interaction tasks were designed by a focus group with experts and a questionnaire with 36 potential end-users. Five end-users (3 SCI and 2 MS) tested the system in the configuration suitable to their specific level of impairment. They performed two exemplary tasks: reaching different points in the working volume and drinking. Three experts evaluated over a 3-level score (from 0, unsuccessful, to 2, completely functional) the execution of each assisted sub-action. Results The functionality of all modules has been successfully demonstrated. User’s intention was detected with a 100% success. Averaging all subjects and tasks, the minimum evaluation score obtained was 1.13 ± 0.99 for the release of the handle during the drinking task, whilst all the other sub-actions achieved a mean value above 1.6. All users, but one, subjectively perceived the usefulness of the assistance and could easily control the system. Donning time ranged from 6 to 65 minutes, scaled on the configuration complexity. Conclusions The MUNDUS platform provides functional assistance to daily life activities; the modules integration depends on the user’s need, the functionality of the system have been demonstrated for all the possible configurations, and preliminary assessment of usability and acceptance is promising., Journal of NeuroEngineering and Rehabilitation, 10, ISSN:1743-0003