Posts Tagged Wrist

[Abstract] An extended kinematic model for arm rehabilitation training and assessment

Abstract

In the rehabilitation training and assessment of upper limbs, the conventional kinematic model treats the arm as a serial manipulator and maps the rotations in the joint space to movements in the Cartesian space. While this model brings simplicity and convenience, and thus has been overwhelming used, its accuracy is limited, especially for the distal parts of the upper limb that execute dexterous movements.

In this paper, a novel kinematic model of the arm has been proposed, which has been inspired by the biomechanical analysis of the forearm and wrist anatomy. One additional parameter is introduced into the conventional arm model, and then both the forward and inverse kinematic models of five parameters are derived for the motion of upper arm medial/lateral rotation, elbow flexion/extension, forearm pronation/supination, wrist flexion/extension and ulnar/radial deviation. Then, experiments with an advanced haptic interface have been designed and performed to examine the presented arm kinematic model. Data analysis revealed that accuracy and robustness can be significantly improved with the new model.

This extended arm kinematic model will help device development, movement training and assessment of upper limb rehabilitation.

Published in: Advanced Robotics and Mechatronics (ICARM), International Conference on

Source: An extended kinematic model for arm rehabilitation training and assessment – IEEE Xplore Document

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[ARTICLE] Classification of EEG signals for wrist and grip movements using echo state network – Full Text

Abstract

Brain-Computer Interface (BCI) is a multi-disciplinary emerging technology being used in medical diagnosis and rehabilitation. In this paper, different techniques of classification and feature extraction are applied to analyse and differentiate the wrist and grip flexion and extension for synchronized stimulation using sensory feedback in neuro-rehabilitation of paralyzed persons. We have used an optimized version of Echo State Network (ESN) to identify as well as differentiate the wrist and grip movements. In this work, the classification accuracy obtained is greater than 96% in a single trial and 93% in discrimination of four movements in real and imagination.

Introduction

The popularity of analysing brain rhythms and its applications in healthcare is evident in rehabilitation engineering. Motor disabilities as a consequence of stroke require rehabilitation process to regain the motor learning and retrieval. The classification of EEG signals obtained by using a low cost Brain Computer Interface (BCI) for wrist and grip movements is used for recovery. Using Movement Related Cortical Potential (MRCP) associated with imaginary movement as detected by the BCI, an external device can be synchronized to provide sensory feedback from electrical stimulation [1]. The timely detection, classification of movement and the real time triggering of the electrical stimulation as a function of brain activity is desirable for neuro-rehabilitation [2,3]. Thus, BCI has an active role in helping out the paralyzed persons who are not able to move their hand or leg [4]. Using BCI system, EEG data is recorded and processed. The acquired data should have the least component of environmental noise and artifacts for effective classification [5]. EEG signals acquired from the invasive method are found to exhibit least noise components and higher amplitude. However, in most applications, a non-invasive method is preferred. The human brain contains a number of neuron networks. EEG provides a measurement of brain activity as voltage fluctuations which are recorded as a result of ionic current within neurons present inside the brain [6]. Many people have motor disabilities due to the nerve system breakdown or accidental failure of nerve system. There are different methods to resolve this problem, e.g. neuro-prosthetics (neural prosthetics) and BCI [3,79]. In neuro-prosthetics, a solution of the problem is in the form of connecting brain nerve system with the device and in BCI connecting brain nerve system with computer [2]. BCI produce a communication between brain and computer via EEG, ECOG or MEG signals. These signals contain information of any of our body activity [10]. Moreover, in addition to neuro-rehabilitation, assistive robotics and brain control mobile robots also utilizes similar technologies as reported recently [11,12]. The signal processing of these low amplitude and noisy EEG signals require special care during data acquisition and filtering. After recording EEG measurements, these signals are processed via filtration, feature extraction, and classification. Simple first or second order Chebyshev or Butterworth filter can be used as a low pass, high pass or a notch filter. Some features can be extracted by using one of the techniques from time analysis, frequency analysis, time-frequency analysis or time-space-frequency analysis [13,14]. Extracted EEG signal further classify by using one of the techniques like LDA, QDA, SVM, KNN etc. [15,16].

We aim to classify the wrist and grip movements using EEG signals. This research will be helpful for convalescence of persons having disabilities in wrist or grip. Our work is based on offline data-sets, in which the EEG data is collected multiple times from 4 subjects. We present the following major contributions in this paper: First, the differentiation between the wrist and grip movements has been performed by using imaginary data as well as the real movements. Secondly, we have tested multiple algorithms for feature extraction and classification and used ESN with optimized parameters for best results. This paper is organized as follows: section 2 describes a low-cost BCI setup for EEG, section 3 deals with the DAQ protocol, section 4 explains the echo state network and its optimization while section 5 discusses results obtained in this research. Section 6 concludes the paper.

Brain Computer Interface Design

Brain-Computer Interface (BCI) design requires a multi-disciplinary approach for engineers to observe EEG data. Today, a number of sensing platforms are available which provide a low-cost solution for high-resolution data acquisition. Developing a BCI interface requires a two-step approach namely the acquisition and the real-time processing. In off-line processing, the only requirement is to do the acquisition. The data is acquired via a wireless network from the pick-off electrodes arranged on the scalp of the subjects [17]. One such available system is Emotiv, which is easy to install and use. Emotiv headset with 14 electrodes and 2 reference electrodes, CMD and DRL, is used to collect data as shown in Figure 1. All electrodes have potential with respect to the reference electrode. Emotiv headset is a non-invasive device to collect the EEG data as preferred in most of the diagnosis and rehabilitation applications [18].

biomedres-Emotiv-EEG

Figure 1. Emotiv EEG acquisition using P-300 standard.

It is important to understand the EEG signal format and frequency content for pre-processing and offline classification. Table 1 shows some of the indications of physical movements and mind actions associated with different brain rhythms in somewhat overlapping frequency bands. It is obvious that the motor imagery tasks are associated with the μ-rhythm in 8-13 Hz frequency band [19].

Rhythm Frequency
(Hz)
Indication Diagnosis
Δ 0-4 Deep sleep stage Hypoglycaemia, Epilepsy
υ 4-7 Initial sleep stage
α 8-12 Closure of eyes Migraine, Dementia
β 12-30 Busy/Anxious thinking Encephalopathies, Tonic seizures
γ 30-100 Cognitive/motor function
µ 8-13 Motor imagery tasks Autism Spectrum Disorder

Table 1. Brain frequency bands and their significance.

biomedres-Grip-movement

 

Continue —> Classification of EEG signals for wrist and grip movements using echo state network

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[VIDEO] Left Hand and Wrist Spasticity – YouTube

This patient demonstrates finger and wrist spasticity. She has trouble pinching and opening her hand, though she has relatively good motor control.

 

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[Abstract] Biofeedback Signals for Robotic Rehabilitation: Assessment of Wrist Muscle Activation Patterns in Healthy Humans

Abstract:

Electrophysiological recordings from human muscles can serve as control signals for robotic rehabilitation devices. Given that many diseases affecting the human sensorimotor system are associated with abnormal patterns of muscle activation, such biofeedback can optimize human-robot interaction and ultimately enhance motor recovery. To understand how mechanical constraints and forces imposed by a robot affect muscle synergies, we mapped the muscle activity of 7 major arm muscles in healthy individuals performing goal-directed discrete wrist movements constrained by a wrist robot. We tested 6 movement directions and 4 force conditions typically experienced during robotic rehabilitation. We analyzed electromyographic (EMG) signals using a space-by-time decomposition and we identified a set of spatial and temporal modules that compactly described the EMG activity and were robust across subjects. For each trial, coefficients expressing the strength of each combination of modules and representing the underlying muscle recruitment, allowed for a highly reliable decoding of all experimental conditions. The decomposition provides compact representations of the observable muscle activation constrained by a robotic device. Results indicate that a low-dimensional control scheme incorporating EMG biofeedback could be an effective add-on for robotic rehabilitative protocols seeking to improve impaired motor function in humans.

Source: Biofeedback Signals for Robotic Rehabilitation: Assessment of Wrist Muscle Activation Patterns in Healthy Humans – IEEE Xplore Document

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[VIDEO] Examination Of The Hand, Tests – Everything You Need To Know – Dr. Nabil Ebraheim – YouTube

Δημοσιεύτηκε στις 21 Φεβ 2013

Educational video describing examination test of the hand.

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[Abstract] An Interactive System for Fine Motor Rehabilitation – Rehabilitation Nursing

Abstract

Purpose

One of the most important aspects in neuromotor rehabilitation is the need of feedback for patients. The rehabilitation system’s efficiency relies on the therapist’s judgment, who tells the patient whether he/she is performing the exercises correctly. This process may be quite subjective, because it depends on the therapist’s personal opinion. On the other hand, recent studies have shown that vibrotactile biofeedback can improve the effectiveness of interaction as it is a very helpful tool in the physiological process of neuromotor rehabilitation.

Design

We designed an interactive system focused on rehabilitation of the upper limbs using active markers and image processing, which consists of drawing activities in both augment and virtual reality.

Methods

System gives to the user a correction through multimodal stimuli feedback (vibrotactile, visual and sound stimulus) and force measurement to let the patients know if they are not achieving the tasks’ goals.

Findings

The developed system could be used by nursing assistants to better help patients. The purpose of this system was assisting patients with injuries to shoulders, elbows or wrists, providing an audio-vibrotactile feedback as a factor of correction in the movements of the patient. To examine our system, 11 participants were asked to participate in an experiment where they performed activities focussed to strengthen their fine motor movements.

Conclusions and Clinical Relevance

Our results show that patients’ fine motor skills improved 10% on average by comparing their error rates throughout the sessions.

Articles related to the one you are viewing

Source: An Interactive System for Fine Motor Rehabilitation – Posada-Gómez – 2016 – Rehabilitation Nursing – Wiley Online Library

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[Abstract] Design of a spring-assisted exoskeleton module for wrist and hand rehabilitation

Abstract:

This paper reports on the development of a low-profile exoskeleton module to enable training of the fingers and thumb in grasp and release tasks. The design has been made as an add-on module for use with the ArmAssist arm rehabilitation system (Tecnalia, Spain). Variable-position springs and adjustable link lengths provide adaptability to fit a variety of users. Additive manufacturing has been utilized for the majority of components allowing easy modifications. A few structural components were machined from aluminum or steel to produce a functional prototype with sufficient strength for direct evaluation. The design includes independent and adjustable assistance in finger and thumb extension using various width elastic bands, and measurement of user grasp/release forces in finger flexion/extension, thumb flexion/extension, and thumb adduction/abduction using low-profile force sensitive resistors.

Source: IEEE Xplore Document – Design of a spring-assisted exoskeleton module for wrist and hand rehabilitation

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[VIDEO] SaeboGlove Presentation – YouTube

Published on Aug 12, 2014

Saebo, Inc., is a leading global provider of affordable evidenced-based therapy solutions for individuals suffering from impaired mobility and function. Headquartered in Charlotte, NC, the company was founded in 2001 by two occupational therapists specializing in upper limb recovery.

Saebo’s products and programs are now offered as a treatment option at over 2,000 clinics and hospitals nationwide, including 22 of the Top 25 Rehabilitation Hospitals as ranked by U.S. News & World Report. With a network of over 8,000 trained clinicians spanning four continents, Saebo is committed to helping clients around the globe achieve a new level of independence.

http://www.saebo.com

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[BOOK] Musculoskeletal Physical Examination: An Evidence-Based Approach – Chapter 6 – Examination of the Wrist and Hand – Google Books

Musculoskeletal Physical Examination: An Evidence-Based Approach

Front Cover
Elsevier Health Sciences, Jul 27, 2016Medical350 pages

From an interdisciplinary author team now including orthopedic surgeons, PM&R specialists, and primary care and sports medicine experts, the second edition of Musculoskeletal Physical Examination: An Evidence-Based Approach educates physicians on how to give the most thorough physical examinations by understanding the “why” behind each type of exam. In-depth coverage of today’s newest tests and techniques keeps you current in practice, and a new section titled “Author’s Preferred Approach” guides you through difficult areas of examination.

  • Provides complete coverage of every musculoskeletal physical examination.
  • Easy-to-use tables summarize and compare the evidence for specificity and sensitivity of each test for each condition.
  • Utilizes over 200 illustrations to clearly depict each test.
  • Includes in-depth coverage of today’s newest tests, including the Thessaly test, Milking test, and Bear hug test.
  • Distinguished author team now includes orthopedic surgeons, PM&R specialists, and primary care sports medicine experts.
  • New section titled “Author’s Preferred Approach” guides readers through difficult areas of examination.
  • Thorough updates and revisions made throughout each chapter keep you current in the field.
  • Full-color figures enhance visual clarity.

 

Source: Musculoskeletal Physical Examination: An Evidence-Based Approach – Gerard A. Malanga, Kenneth Mautner – Google Books

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[VIDEO] PABLO System Hand-Arm-Rehabilitation (English) – YouTube

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