An Overview of Artificial Intelligence-based Soft Upper Limb Exoskeleton for Rehabilitation: A Descriptive Review

• URL: http://arxiv.org/abs/2301.04336v1
• Date: Wed, 11 Jan 2023 07:13:25 GMT
• Title: An Overview of Artificial Intelligence-based Soft Upper Limb Exoskeleton for Rehabilitation: A Descriptive Review
• Authors: Sanjukta Halder, Dr. Amit Kumar
• Abstract summary: The upper limb robotic exoskeleton is an electromechanical device which use to recover a patients motor dysfunction in the rehabilitation field. It can provide repetitive, comprehensive, focused, positive, and precise training to regain the joints and muscles capability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The upper limb robotic exoskeleton is an electromechanical device which use to recover a patients motor dysfunction in the rehabilitation field. It can provide repetitive, comprehensive, focused, positive, and precise training to regain the joints and muscles capability. It has been shown that existing robotic exoskeletons are generally used rigid motors and mechanical structures. Soft robotic devices can be a correct substitute for rigid ones. Soft exosuits are flexible, portable, comfortable, user-friendly, low-cost, and travel-friendly. Somehow, they need expertise or therapist to assist those devices. Also, they cannot be adaptable to different patients with non-identical physical parameters and various rehabilitation needs. For that reason, nowadays we need intelligent exoskeletons during rehabilitation which have to learn from patients previous data and act according to it with patients intention. There also has a big gap between theoretical and practical applications for using those exoskeletons. Most of the intelligent exoskeletons are prototype in manner. To solve this problem, the robotic exoskeleton should be made both criteria as ergonomic and portable. The exoskeletons have to the power of decision-making to avoid the presence of expertise. In this growing field, the present trend is to make the exoskeleton intelligent and make it more reliable to use in clinical practice.

Related papers

• HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation [50.616995671367704]
  We present a high-dimensional, simulated robot learning benchmark, HumanoidBench, featuring a humanoid robot equipped with dexterous hands. Our findings reveal that state-of-the-art reinforcement learning algorithms struggle with most tasks, whereas a hierarchical learning approach achieves superior performance when supported by robust low-level policies.
  arXiv  Detail & Related papers  (2024-03-15T17:45:44Z)
• Intelligent upper-limb exoskeleton integrated with soft wearable bioelectronics and deep-learning for human intention-driven strength augmentation based on sensory feedback [5.447052101190182]
  The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper extremities. Here, we introduce an upper-limb exoskeleton system that uses cloud-based deep learning to predict human intention for strength augmentation. The intent-driven exoskeleton can augment human strength by 5.15 times on average compared to the unassisted exoskeleton.
  arXiv  Detail & Related papers  (2023-09-09T01:30:07Z)
• AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System [51.48191418148764]
  Vision-based teleoperation can endow robots with human-level intelligence to interact with the environment. Current vision-based teleoperation systems are designed and engineered towards a particular robot model and deploy environment. We propose AnyTeleop, a unified and general teleoperation system to support multiple different arms, hands, realities, and camera configurations within a single system.
  arXiv  Detail & Related papers  (2023-07-10T14:11:07Z)
• Teach Me How to Learn: A Perspective Review towards User-centered Neuro-symbolic Learning for Robotic Surgical Systems [3.5672486441844553]
  Recent advances in machine learning allowed robots to identify objects on a perceptual nonsymbolic level. An alternative solution is to teach a robot on both perceptual nonsymbolic and conceptual symbolic levels. This work proposes a concept for this user-centered hybrid learning paradigm that focuses on robotic surgical situations.
  arXiv  Detail & Related papers  (2023-07-07T21:58:28Z)
• Learning body models: from humans to humanoids [2.855485723554975]
  Humans and animals excel in combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth, failures, or using tools. Key foundation is an internal representation of the body that the agent - human, animal, or robot - has developed. mechanisms of operation of body models in the brain are largely unknown and even less is known about how they are constructed from experience after birth.
  arXiv  Detail & Related papers  (2022-11-06T07:30:01Z)
• Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening [59.88594294676711]
  Modern deep learning based motion synthesis approaches barely consider the physical plausibility of synthesized motions. We propose a system Skeleton2Humanoid'' which performs physics-oriented motion correction at test time. Experiments on the challenging LaFAN1 dataset show our system can outperform prior methods significantly in terms of both physical plausibility and accuracy.
  arXiv  Detail & Related papers  (2022-10-09T16:15:34Z)
• GenLoco: Generalized Locomotion Controllers for Quadrupedal Robots [87.32145104894754]
  We introduce a framework for training generalized locomotion (GenLoco) controllers for quadrupedal robots. Our framework synthesizes general-purpose locomotion controllers that can be deployed on a large variety of quadrupedal robots. We show that our models acquire more general control strategies that can be directly transferred to novel simulated and real-world robots.
  arXiv  Detail & Related papers  (2022-09-12T15:14:32Z)
• Learning Control Policies for Fall prevention and safety in bipedal locomotion [0.0]
  We develop learning-based algorithms capable of synthesizing push recovery control policies for two different kinds of robots. Our work can be branched into two closely related directions : 1) Learning safe falling and fall prevention strategies for humanoid robots and 2) Learning fall prevention strategies for humans using a robotic assistive devices.
  arXiv  Detail & Related papers  (2022-01-04T22:00:21Z)
• Future Frame Prediction for Robot-assisted Surgery [57.18185972461453]
  We propose a ternary prior guided variational autoencoder (TPG-VAE) model for future frame prediction in robotic surgical video sequences. Besides content distribution, our model learns motion distribution, which is novel to handle the small movements of surgical tools.
  arXiv  Detail & Related papers  (2021-03-18T15:12:06Z)
• OpenBot: Turning Smartphones into Robots [95.94432031144716]
  Current robots are either expensive or make significant compromises on sensory richness, computational power, and communication capabilities. We propose to leverage smartphones to equip robots with extensive sensor suites, powerful computational abilities, state-of-the-art communication channels, and access to a thriving software ecosystem. We design a small electric vehicle that costs $50 and serves as a robot body for standard Android smartphones.
  arXiv  Detail & Related papers  (2020-08-24T18:04:50Z)
• Robotic Cane as a Soft SuperLimb for Elderly Sit-to-Stand Assistance [24.50702040551235]
  We proposed an explorative design of an ambient SuperLimb system that involves a pneumatically-driven robotic cane for at-home motion assistance. We validated the design feasibility of the proposed ambient SuperLimb system including the biomechanical model. We summarized empirical guidelines to support the ambient design of elderly-assistive SuperLimb systems for lower limb functional augmentation.
  arXiv  Detail & Related papers  (2020-02-29T22:44:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.