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Proceedings ArticleDOI

Intelligent Exoskeleton for Patients with Paralysis

01 Nov 2018-

TL;DR: A robotic upper-limb exoskeleton guided using 3D object detection and recognition and controlled via Electroencephalogram (EEG) signals to give patients with complete monoplegic, hemiplegic and quadriplegic paralysis the ability to move their upper- Limb and control a wheelchair using their thoughts.
Abstract: 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.
Topics: Exoskeleton (50%)
Citations
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Journal ArticleDOI
18 Mar 2021-Sensors
Abstract: 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.

4 citations


Proceedings ArticleDOI
01 Nov 2019
TL;DR: 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.
Abstract: 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.

3 citations


Cites background from "Intelligent Exoskeleton for Patient..."

  • ...The synchronization between the user's intention and the function of the exoskeleton is a challenge in smart design, seeking a smooth interaction between the user and the device [35], [36]....

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Proceedings ArticleDOI
01 Sep 2019
TL;DR: In order to perform an optimization procedure, the dynamic model of the system has been developed analytically and optimization criteria in terms of closed-loop system bandwidth, system power consumption and component weights have been formulated by taking technical limitations into account.
Abstract: 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.

2 citations


Cites background from "Intelligent Exoskeleton for Patient..."

  • ...Moreover, exoskeletons are recognized as wearable robots that operates mechanically in parallel with the human body and can be actuated in both passive and active mode [2]....

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Journal ArticleDOI
TL;DR: 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.
Abstract: 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.

1 citations


Journal ArticleDOI
10 Aug 2021-Sensors
Abstract: 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.

1 citations


References
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Journal ArticleDOI
Philip H. S. Torr1, Andrew Zisserman2Institutions (2)
TL;DR: A new robust estimator MLESAC is presented which is a generalization of the RANSAC estimator which adopts the same sampling strategy as RANSac to generate putative solutions, but chooses the solution that maximizes the likelihood rather than just the number of inliers.
Abstract: A new method is presented for robustly estimating multiple view relations from point correspondences. The method comprises two parts. The first is a new robust estimator MLESAC which is a generalization of the RANSAC estimator. It adopts the same sampling strategy as RANSAC to generate putative solutions, but chooses the solution that maximizes the likelihood rather than just the number of inliers. The second part of the algorithm is a general purpose method for automatically parameterizing these relations, using the output of MLESAC. A difficulty with multiview image relations is that there are often nonlinear constraints between the parameters, making optimization a difficult task. The parameterization method overcomes the difficulty of nonlinear constraints and conducts a constrained optimization. The method is general and its use is illustrated for the estimation of fundamental matrices, image–image homographies, and quadratic transformations. Results are given for both synthetic and real images. It is demonstrated that the method gives results equal or superior to those of previous approaches.

2,021 citations


"Intelligent Exoskeleton for Patient..." refers methods in this paper

  • ...The MSAC algorithm is a modified version of the Random Sample Consensus (RANSAC) algorithm and was developed by Torr and Zisserman in this research [9]....

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Proceedings ArticleDOI
G. Reshmi1, A. Amal1Institutions (1)
29 Aug 2013
TL;DR: In this work the motor imagery EEG signal is translated into control signal using a five class BCI to control the directional movement of a wheelchair.
Abstract: Motor imagery is the recreation of a motor activity which can be used to design Brain Computer Interfaces (BCI). A BCI bypasses the neuromuscular system and provides a communication link, which directly connects the human brain to an external device. Each individual is able to control his EEG through imaginary motor act it supports to control devices. In this work for designing a BCI system five class motor imagery EEG is used. EEG recorded from the sensory motor cortex is analyzed using wavelet transform. Features extracted from the wavelet coefficients are classified using Support Vector Machine. In this work the motor imagery EEG signal is translated into control signal using a five class BCI to control the directional movement of a wheelchair.

19 citations


"Intelligent Exoskeleton for Patient..." refers background in this paper

  • ...While the authors in [3] introduced motor imagery EEG signals which are translated into control commands to decide the directional movement of a wheelchair....

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01 Jan 2003
Abstract: Research into ergonomics is one of the aspects in the research for human-powered energy systems. In this specific field, data on maximum force exertion and endurance can be found in a large number of publications, mainly originating from sport or military related research. Data on comfortable or sustainable force exertion however prove not to be available. In this research project we attempted to measure comfortable/sustainable force exertion. We mapped one specific movement (one-handed cranking) using the Critical Power test. This test is based on the assumed linear relation between maximal work and time to exhaustion (Morton’s 3parameter critical power model). The experimental set-up consisted of an altered cycleergometer which was adjustable in height. We measured the subjects' (eight young males) maximum power output and the time to exhaustion at different power levels. The research showed a sustained power output from cranking to be: 54 ± 14 Watt (mean ± SD). In the paper we will present the research project and its results and link them to literature in the field of comfort.

15 citations


"Intelligent Exoskeleton for Patient..." refers background in this paper

  • ...Speed should be in range of 10 – 25 RPM to suit the human arm movement [7]....

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Proceedings ArticleDOI
01 Aug 2017
TL;DR: An Electroencephalogram (EEG)-based communication system is developed to facilitate communication of Locked-in Syndrome patients with their caretakers and a prototype system has been developed and successfully tested.
Abstract: Patients who are conscious and aware of their environment but are physically disabled are known to have Locked-in Syndrome. The causes for this medical condition include traffic accidents, drug addiction and brain clots. There are some available solutions nowadays to help them communicate but the down side is the requirement for physical training which can be both time and money consuming. The main objective of this project is to help these patients communicate and engage more effectively in their daily life. In this paper, an Electroencephalogram (EEG)-based communication system is developed to facilitate communication of these patients with their caretakers. The implementation is composed of both hardware and software. The hardware consists of a 14-channels EEG module and a tablet. The software parts are: processing algorithm, online database and an android application. The EEG module on the patients' scalps keeps reading brainwaves continuously. Meanwhile, the tablet in front of them displays six basic needs, namely, food, water, washroom, help, sleep and entrainment. When the patients focus on a specific need, it will be detected when it matches with a predefined reference in the processing algorithm. The processing is done using fuzzy logic pattern recognition based on eye movement and color detection. The database acts as a two-way communication link between the patients and their caretaker. As the message will be sent through it to the android application-which is installed in the caretakers' phones-in the form of a pop-up notification. Interchangeably, a response message can be sent by the caretakers to state they are on their way for instance. Besides, the tablet will generate a voice message to inform the people around the patients about their need. A prototype system has been developed and successfully tested.

3 citations


"Intelligent Exoskeleton for Patient..." refers methods in this paper

  • ...As proposed by the authors of this paper [8], Fuzzy Logic pattern recognition was used to detect the deflection of each sample by tracking the inputs continually....

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Proceedings ArticleDOI
Jackson Joon Shee Wong1, How Ung Ha1Institutions (1)
01 Dec 2014
Abstract: One of the main challenges of exoskeleton design lies in its Human-Machine Interface, where Brain-Machine Interface control systems have yet to provide satisfactory levels of robustness and performance for practical use [1]. A HMIF (Human-Machine Interactive Force) based control system was proposed to provide a relatively simple and potentially more robust alternative for exoskeleton control [1]. To assess the viability of the proposed exoskeleton control system, a realistic CAD (Computer Aided Design) model of a 3-DoF (Degree of Freedom) upper-limb exoskeleton was created to form a realistic basis for the HMIFCS (HMIF Control System) simulation model. Simulation results were used to guide the development of optimized torque assist curves, which were shown to provide safe and effective load attenuation, as well as maximize free-motion performance.

1 citations


Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
20221
20213
20192