Background: Although robot therapy is progressively becoming an accepted method of treatment for stroke survivors, few studies have investigated how to adapt the robot/subject interaction forces in an automatic way. The paper is a feasibility study of a novel self-adaptive robot controller to be applied with continuous tracking movements.

Methods: The haptic robot Braccio di Ferro is used, in relation with a tracking task. The proposed control architecture is based on three main modules: 1) a force field generator that combines a non linear attractive field and a viscous field; 2) a performance evaluation module; 3) an adaptive controller. The first module operates in a continuous time fashion; the other two modules operate in an intermittent way and are triggered at the end of the current block of trials. The controller progressively decreases the gain of the force field, within a session, but operates in a non monotonic way between sessions: it remembers the minimum gain achieved in a session and propagates it to the next one, which starts with a block whose gain is greater than the previous one. The initial assistance gains are chosen according to a minimal assistance strategy. The scheme can also be applied with closed eyes in order to enhance the role of proprioception in learning and control.

Results: The preliminary results with a small group of patients (10 chronic hemiplegic subjects) show that the scheme is robust and promotes a statistically significant improvement in performance indicators as well as a recalibration of the visual and proprioceptive channels. The results confirm that the minimally assistive, self-adaptive strategy is well tolerated by severely impaired subjects and is beneficial also for less severe patients.

Conclusions: The experiments provide detailed information about the stability and robustness of the adaptive controller of robot assistance that could be quite relevant for the design of future large scale controlled clinical trials. Moreover, the study suggests that including continuous movement in the repertoire of training is acceptable also by rather severely impaired subjects and confirms the stabilizing effect of alternating vision/no vision trials already found in previous studies.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2850909PMC
http://dx.doi.org/10.1186/1743-0003-7-13DOI Listing

Publication Analysis

Top Keywords

self-adaptive robot
8
stroke survivors
8
continuous tracking
8
force field
8
adaptive controller
8
severely impaired
8
impaired subjects
8
robot training
4
training stroke
4
continuous
4

Similar Publications

Robotic Microcapsule Assemblies with Adaptive Mobility for Targeted Treatment of Rugged Biological Microenvironments.

ACS Nano

January 2025

Center for Innovation & Precision Dentistry, School of Dental Medicine, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.

Microrobots are poised to transform biomedicine by enabling precise, noninvasive procedures. However, current magnetic microrobots, composed of solid monolithic particles, present fundamental challenges in engineering intersubunit interactions, limiting their collective effectiveness in navigating irregular biological terrains and confined spaces. To address this, we design hierarchically assembled microrobots with multiaxis mobility and collective adaptability by engineering the potential magnetic interaction energy between subunits to create stable, self-reconfigurable structures capable of carrying and protecting cargo internally.

View Article and Find Full Text PDF

Electromagnetic metamaterial agent.

Light Sci Appl

January 2025

State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics, Peking University, Beijing, 100871, China.

Metamaterials have revolutionized wave control; in the last two decades, they evolved from passive devices via programmable devices to sensor-endowed self-adaptive devices realizing a user-specified functionality. Although deep-learning techniques play an increasingly important role in metamaterial inverse design, measurement post-processing and end-to-end optimization, their role is ultimately still limited to approximating specific mathematical relations; the metamaterial is still limited to serving as proxy of a human operator, realizing a predefined functionality. Here, we propose and experimentally prototype a paradigm shift toward a metamaterial agent (coined metaAgent) endowed with reasoning and cognitive capabilities enabling the autonomous planning and successful execution of diverse long-horizon tasks, including electromagnetic (EM) field manipulations and interactions with robots and humans.

View Article and Find Full Text PDF

Mechano-Graded Contact-Electrification Interfaces Based Artificial Mechanoreceptors for Robotic Adaptive Reception.

ACS Nano

January 2025

Institute of Functional Nano and Soft Materials (FUNSOM), Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123, P. R. China.

Triboelectrification-based artificial mechanoreceptors (TBAMs) is able to convert mechanical stimuli directly into electrical signals, realizing self-adaptive protection and human-machine interactions of robots. However, traditional contact-electrification interfaces are prone to reaching their deformation limits under large pressures, resulting in a relatively narrow linear range. In this work, we fabricated mechano-graded microstructures to modulate the strain behavior of contact-electrification interfaces, simultaneously endowing the TBAMs with a high sensitivity and a wide linear detection range.

View Article and Find Full Text PDF

Theoretical Design of Smart Bionic Skins with Self-Adaptive Temperature Regulation.

Materials (Basel)

November 2024

Centre for Optical and Electromagnetic Research, State Key Lab of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China.

Article Synopsis
  • Thermal management is a key issue in electric design, especially for compact electronic systems, and this study introduces a bionic skin model that mimics human skin to regulate temperature effectively.
  • The artificial skin features phase change material nanoparticles in a thin matrix on a metallic surface, enabling it to maintain a stable temperature around 340 K despite varying external heat conditions.
  • The design also includes a plasmonic surface made of silver nanocubes to enhance visible light properties while retaining infrared functionality, providing a cost-effective solution for advanced thermal management in robotics and similar technologies.
View Article and Find Full Text PDF

Highly valued subgoal generation for efficient goal-conditioned reinforcement learning.

Neural Netw

January 2025

College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, China. Electronic address:

Article Synopsis
  • Goal-conditioned reinforcement learning helps robots perform specific tasks by maximizing rewards, but it faces challenges due to sparse rewards that hinder the learning process.
  • The proposed method generates meaningful subgoals tailored to the context of tasks, allowing robots to learn more efficiently through better action value learning.
  • Compared to existing methods like Hindsight Experience Replay, this approach improves stability and performance in robotic tasks by creating subgoals that are contextually relevant and appropriately complex.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!