Virtual reality training and EMG control of the MANUS hand prosthesis

Pons, José L; Ceres, R.; Rocón, Eduardo; Levin, Sondra W.; Markovitz, I.; Saro, B.; Reynaerts, Dominiek; Moorleghem, W. Van; Bueno, Lívia Maronesi

doi:10.1017/s026357470400133x

Cited by 50 publications

(45 citation statements)

References 7 publications

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“…With the first approach, used by the MANUS hand [52], Fluidhand [53], and Southampton hybrid [54] systems, the user chooses from a set list of grasps. This approach can allow the specific grasp types available to be customized to the user's preference during training [52].…”

Section: Control Requirement 1: Available Grasp Typesmentioning

confidence: 99%

“…The MANUS hand [52] and Fluidhand [53] systems will both continue to hold a grasped object unless given another signal. The Southampton system [54] automatically holds objects as well, but also prevents slipping of objects, which is detected by way of acoustic sensors.…”

Section: Control Requirements 3 and 4: Automatic Holding And Slip Prementioning

confidence: 99%

“…This number varies from hand to hand. For example, the MANUS hand system [52] always requires a three-symbol code to execute any command. In contrast, the Southampton hand system [54] requires a single close signal to perform a simple grasp; however, an additional control signal is required to increase the applied force, which causes some delay in its execution.…”

Section: Control Requirement 5: Grasp Execution Timementioning

confidence: 99%

“…The first gives the user direct control over the force with which the grasp is closed and with which objects are held and is used by the Cyberhand and MANUS hand [52,57]. The second method automatically applies sufficient force but gives the user the option of switching to direct force control during grasping; the Southampton control system [54] features this option.…”

Section: Control Requirement 6: User-controlled Force and Speedmentioning

confidence: 99%

See 3 more Smart Citations

Myoelectric forearm prostheses: State of the art from a user-centered perspective

Peerdeman

Boere²,

Witteveen³

et al. 2011

JRRD

402

303

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Abstract-User acceptance of myoelectric forearm prostheses is currently low. Awkward control, lack of feedback, and difficult training are cited as primary reasons. Recently, researchers have focused on exploiting the new possibilities offered by advancements in prosthetic technology. Alternatively, researchers could focus on prosthesis acceptance by developing functional requirements based on activities users are likely to perform. In this article, we describe the process of determining such requirements and then the application of these requirements to evaluating the state of the art in myoelectric forearm prosthesis research. As part of a needs assessment, a workshop was organized involving clinicians (representing end users), academics, and engineers. The resulting needs included an increased number of functions, lower reaction and execution times, and intuitiveness of both control and feedback systems. Reviewing the state of the art of research in the main prosthetic subsystems (electromyographic [EMG] sensing, control, and feedback) showed that modern research prototypes only partly fulfill the requirements. We found that focus should be on validating EMG-sensing results with patients, improving simultaneous control of wrist movements and grasps, deriving optimal parameters for force and position feedback, and taking into account the psychophysical aspects of feedback, such as intensity perception and spatial acuity.

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Section: Control Requirement 1: Available Grasp Typesmentioning

confidence: 99%

Section: Control Requirements 3 and 4: Automatic Holding And Slip Prementioning

confidence: 99%

Section: Control Requirement 5: Grasp Execution Timementioning

confidence: 99%

Section: Control Requirement 6: User-controlled Force and Speedmentioning

confidence: 99%

See 2 more Smart Citations

Myoelectric forearm prostheses: State of the art from a user-centered perspective

Peerdeman

Boere²,

Witteveen³

et al. 2011

JRRD

402

303

View full text Add to dashboard Cite

show abstract

“…The training systems in the literature included the following types of devices: signal strength displays [13], myoelectrically controlled video games [14][15][16][17], robotic arms [18], and computer simulations [19][20][21][22][23][24]. The devices in the literature were found to have an emphasis on being platforms for researching new myoelectric control methods rather than a training focus on helping patients learn to use the conventional control schemes commonly used in commercial prostheses.…”

Section: Review Of Training Systemsmentioning

confidence: 99%

The Development of a Myoelectric Training Tool for Above-Elbow Amputees

Dawson¹,

Fahimi²,

Carey³

2012

TOBEJ

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The objective of above-elbow myoelectric prostheses is to reestablish the functionality of missing limbs and increase the quality of life of amputees. By using electromyography (EMG) electrodes attached to the surface of the skin, amputees are able to control motors in myoelectric prostheses by voluntarily contracting the muscles of their residual limb. This work describes the development of an inexpensive myoelectric training tool (MTT) designed to help upper limb amputees learn how to use myoelectric technology in advance of receiving their actual myoelectric prosthesis. The training tool consists of a physical and simulated robotic arm, signal acquisition hardware, controller software, and a graphical user interface. The MTT improves over earlier training systems by allowing a targeted muscle reinnervation (TMR) patient to control up to two degrees of freedom simultaneously. The training tool has also been designed to function as a research prototype for novel myoelectric controllers. A preliminary experiment was performed in order to evaluate the effectiveness of the MTT as a learning tool and to identify any issues with the system. Five able-bodied participants performed a motor-learning task using the EMG controlled robotic arm with the goal of moving five balls from one box to another as quickly as possible. The results indicate that the subjects improved their skill in myoelectric control over the course of the trials. A usability survey was administered to the subjects after their trials. Results from the survey showed that the shoulder degree of freedom was the most difficult to control.

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Virtual Rehabilitation Service for Upper Amputees Based on Computer-Aided Environment

Liu

2022

Communications in Computer and Information Science

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Virtual reality training and EMG control of the MANUS hand prosthesis

Cited by 50 publications

References 7 publications

Myoelectric forearm prostheses: State of the art from a user-centered perspective

Myoelectric forearm prostheses: State of the art from a user-centered perspective

The Development of a Myoelectric Training Tool for Above-Elbow Amputees

Virtual Rehabilitation Service for Upper Amputees Based on Computer-Aided Environment

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