Posts Tagged Pattern recognition

[Abstract + References] Design and Development of a Robotic Platform Based on Virtual Reality Scenarios and Wearable Sensors for Upper Limb Rehabilitation and Visuomotor Coordination – Conference paper

Abstract

The work reintegration following shoulder biomechanical overload illness is a multidimensional process, especially for those tasks requiring strength, movement control and arm dexterity. Currently different robotic devices used for upper limb rehabilitation are available on the market, but these devices are not based on activities focused on the work reintegration. Furthermore, the rehabilitation programmes aimed to the work reintegration are insufficiently focused on the recovery of the necessary skills for the re-employment.

In this study the details of the design of an innovative robotic platform integrated with wearable sensors and virtual reality scenarios for upper limbs motor rehabilitation and visuomotor coordination is presented. The design of control strategy will also be introduced. The robotic platform is based on a robotic arm characterized by seven degrees of freedom and by an adaptive control, wearable sensorized insoles, virtual reality (VR) scenarios and the Leap Motion device to track the hand gestures during the rehabilitation training. Future works will address the application of deep learning techniques for the analysis of the acquired big amount of data in order to automatically adapt both the difficulty level of the VR serious games and amount of motor assistance provided by the robot.

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[Abstract] On the use of wearable sensors to enhance motion intention detection for a contralaterally controlled FES system.

During the last years, there has been a relevant progress in motor learning and functional recovery after the occurrence of a brain lesion. Rehabilitation of motor function has been associated to motor learning that occurs during repetitive, frequent and intensive training.

Contralaterally controlled functional electrical stimulation (CCFES) is a new therapy designed to improve the recovery of paretic limbs after stroke, that could provide repetitive training-based therapies and has been developed to control the upper and lower limbs movements in response to user’s intentionality.

Electromyography (EMG) signals reflect directly the human motion intention, so it can be used as input information to control a CCFES system. Implementation of the EMG-based pattern recognition is not easy to be accomplished due to some difficulties, among them that the activity level of each muscle for a certain motion is different between each person. Inertial Measurement Units (IMU) is a kind of wearable sensors that are used to gather movement data. IMUs could provide valuable kinematic information in an EMG-based pattern recognition process to improve classification.

This work describes the use of IMUS to improve detecting motion intention from EMG data. Results shows that myoelectric algorithm using information from IMUs was better in classification of seven movements at the upper-limb level that algorithm using only EMG data.

Source: IEEE Xplore Abstract (Abstract) – On the use of wearable sensors to enhance motion intention detection for a contralaterally controlle…

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