Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.

https://www.mdpi.com/1424-8220/21/6/2146/pdf

Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review.

Neuromotor rehabilitation and recovery of upper limb functions are essential to improve the life quality of patients who have suffered injuries or have pathological sequels, where it is desirable to enhance the development of activities of daily living (ADLs). Modern approaches such as robotic-assisted rehabilitation provide decisive factors for effective motor recovery, such as objective assessment of the progress of the patient and the potential for the implementation of personalized training plans. This paper focuses on the design, development, and preliminary testing of a wearable robotic exoskeleton prototype with autonomous Artificial Intelligence-based control, processing, and safety algorithms that are fully embedded in the device. The proposed exoskeleton is a 1-DoF system that allows flexion-extension at the elbow joint, where the chosen materials render it compact. Different operation modes are supported by a hierarchical control strategy, allowing operation in autonomous mode, remote control mode, or in a leader-follower mode. Laboratory tests validate the proper operation of the integrated technologies, highlighting a low latency and reasonable accuracy. The experimental result shows that the device can be suitable for use in providing support for diagnostic and rehabilitation processes of neuromotor functions, although optimizations and rigorous clinical validation are required beforehand.

Design, Development, and Testing of an Intelligent Wearable Robotic Exoskeleton Prototype for Upper Limb Rehabilitation.

Technology plays a vital role in patient rehabilitation, improving the quality of life of an individual. The increase in functional independence of disabled individuals requires adaptive and commercially available solutions. The use of sensor-based technology helps patients and therapeutic practices beyond traditional therapy. Adapting skeletal tracking technology could automate exercise tracking, records, and feedback for patient motivation and clinical treatment interventions and planning. In this paper, an exoskeleton was designed and subsequently developed for patients who are suffering from monoparesis in the upper extremities. The exoskeleton was developed according to the dimensions of a patient using a 3D scanner, and then fabricated with a 3D printer; the mechanism for the movement of the hand is a tendon flexion mechanism with servo motor actuators controlled by an ATMega2560 microcontroller. The exoskeleton was used for force augmentation of the patient’s hand by taking the input from the hand via flex sensors, and assisted the patient in closing, opening, grasping, and picking up objects, and it was also able to perform certain exercises for the rehabilitation of the patient. The exoskeleton is portable, reliable, durable, intuitive, and easy to install and use at any time.

Design of Portable Exoskeleton Forearm for Rehabilitation of Monoparesis Patients Using Tendon Flexion Sensing Mechanism for Health Care Applications

High performance of an exoskeleton depends on torque/force tracking in a proper way according to human motion. Hence, because of the importance of actuator systems in wearable robots which usually include electrical motors and transmissions, this paper proposes a solution for selection of these components for an upper-body exoskeleton. In order to perform an optimization procedure, the dynamic model of the system has been developed analytically. Accordingly, optimization criteria in terms of closed-loop system bandwidth, system power consumption and component weights have been formulated by taking technical limitations into account. Extensive simulation results are presented in order to make comparison among possible optimization results.

On the Optimal Selection of Motors and Transmissions for a Back-Support Exoskeleton

Neuromotor function rehabilitation is a process that implies a considerable recovery period, which can be slow, costly, and ineffective. This paper presents an architecture proposal for the development of intelligent robotic exoskeletons to support upper limb rehabilitation, especially in clinical conditions such as hemiparesis, as well as to address the problems described above. A review of the literature is presented, exploring the intelligent systems of control and signal processing in medical robotics, biosignal acquisition and the integration of technologies into this type of exoskeleton. The niches of scientific research are highlighted which allow for the planning of this architecture, such as the design of control strategies focused on the patient’s clinical condition, the proper data acquisition and the establishment of methodologies that lead to the adoption of technology in the health area. It is emphasized that this proposal focuses on the use of new engineering methods and tools, where mechanical, electronic and computational systems are involved, providing advantages in areas such as medicine, health sciences and physiotherapy. Also, this proposal has a high social impact, since it allows the development of tools that help in the rehabilitation processes carried out in developing countries such as Colombia.

Data Acquisition and Control Architecture for Intelligent Robotic Exoskeletons in Rehabilitation

The topic of paralysis has gained a lot of interest among scientist over the last years. Therefore, many projects were made for patients suffering from paralysis, yet none has succeeded in achieving an effective way to give these patients the ability to control their paralyzed body parts. This paper proposes a robotic upper-limb exoskeleton guided using 3D object detection and recognition and controlled via Electroencephalogram (EEG) signals. The proposed system is dedicated to patients with complete monoplegic, hemiplegic and quadriplegic paralysis. The main objective of the system is to give these patients the ability to move their upper-limb, and control a wheelchair using their thoughts, which offers them independence, better life quality and assist them in leading active roles in the society. This system consists of four main components, namely, EEG module, infrared (IR) depth camera, 3D printed upper-limb exoskeleton and a motorized wheelchair. The former two are used as inputs to the system and the collaboration between them shows the uniqueness of the proposed approach. EEG signals are segmented and classified through Fuzzy Logic technique and the results are used for choosing the desired object for grabbing from the surface of a table. Movement to the desired object is executed based on the 3D coordinates obtained from the IR depth camera, while inertial measurement unit (IMU) sensor is placed on the arm as a feedback element to ensure accurate movement and proper safety measures. System prototype showed sufficient results for the proposed idea.

Khaled Mortazavi

Papers

Intelligent Exoskeleton for Patients with Paralysis