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Wrist

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Minimize Tracking Occlusion in Collaborative Pick-and-Place Tasks: An Analytical Approach for Non-Wrist-Partitioned Manipulators.

Sensors (Basel, Switzerland)
Several industrial pick-and-place applications, such as collaborative assembly lines, rely on visual tracking of the parts. Recurrent occlusions are caused by the manipulator motion decrease line productivity and can provoke failures. This work provi...

A new robot-based proprioceptive training algorithm to induce sensorimotor enhancement in the human wrist.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Afferent proprioceptive signals, responsible for body awareness, have a crucial role when planning and executing motor tasks. Increasing evidence suggests that proprioceptive sensory training may improve motor performance. Although this topic had bee...

Design Feasibility of an Energy-efficient Wrist Flexion-Extension Exoskeleton using Compliant Beams and Soft Actuators.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Passive and active exoskeletons have been used over recent decades. However, regarding many physiological systems, we see that the majority explore both active and passive elements to minimize energy consumption while retaining proper motion control....

Towards a Closed-loop Neuro-Robotic Approach to DBS Electrode Implantation based on Real-Time Wrist Rigidity Evaluation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The iHandU system is a wearable device that quantitatively evaluates changes in wrist rigidity during Deep Brain Stimulation (DBS) surgery, allowing clinicians to find optimal stimulation settings that reduce patient symptoms. Robotic accuracy is als...

Analysis of the Leap Motion Controller's Performance in Measuring Wrist Rehabilitation Tasks Using an Industrial Robot Arm Reference.

Sensors (Basel, Switzerland)
The Leap Motion Controller (LMC) is a low-cost markerless optical sensor that performs measurements of various parameters of the hands that has been investigated for a wide range of different applications. Research attention still needs to focus on t...

Corticomuscular integrated representation of voluntary motor effort in robotic control for wrist-hand rehabilitation after stroke.

Journal of neural engineering
The central-to-peripheral voluntary motor effort (VME) in the affected limb is a dominant force for driving the functional neuroplasticity on motor restoration post-stroke. However, current rehabilitation robots isolated the central and peripheral in...

[Management of the nondisplaced type Herbert D1 scaphoid fracture with robot navigation combined with wrist arthroscopy].

Zhonghua yi xue za zhi
To investigate the feasibility and the clinical efficiency of robot navigation combined with wrist arthroscopy in minimally invasive treatment of nondisplaced type Herbert D1 scaphoid fracture. A retrospective analysis was performed on 9 patients who...

Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models.

Sensors (Basel, Switzerland)
Hospitals, especially their emergency services, receive a high number of wrist fracture cases. For correct diagnosis and proper treatment of these, images obtained from various medical equipment must be viewed by physicians, along with the patient's ...

Detecting Distal Radial Fractures from Wrist Radiographs Using a Deep Convolutional Neural Network with an Accuracy Comparable to Hand Orthopedic Surgeons.

Journal of digital imaging
In recent years, fracture image diagnosis using a convolutional neural network (CNN) has been reported. The purpose of the present study was to evaluate the ability of CNN to diagnose distal radius fractures (DRFs) using frontal and lateral wrist rad...

Enhanced Recognition of Amputated Wrist and Hand Movements by Deep Learning Method Using Multimodal Fusion of Electromyography and Electroencephalography.

Sensors (Basel, Switzerland)
Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based ...