scholarly journals Brain–computer interfaces for human gait restoration

2021 ◽  
Author(s):  
Zoran Nenadic
Keyword(s):  
Stroke Rehabilitation ◽  
Low Cost ◽  
Human Gait ◽  
Virtual Reality Environment ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Cord Injury ◽  
Neurological Injuries ◽  
Online Tests ◽  
Bci System

AbstractIn this review article, we present more than a decade of our work on the development of brain–computer interface (BCI) systems for the restoration of walking following neurological injuries such as spinal cord injury (SCI) or stroke. Most of this work has been in the domain of non-invasive electroencephalogram-based BCIs, including interfacing our system with a virtual reality environment and physical prostheses. Real-time online tests are presented to demonstrate the ability of able-bodied subjects as well as those with SCI to purposefully operate our BCI system. Extensions of this work are also presented and include the development of a portable low-cost BCI suitable for at-home use, our ongoing efforts to develop a fully implantable BCI for the restoration of walking and leg sensation after SCI, and our novel BCI-based therapy for stroke rehabilitation.

Non-Invasive Brain–Computer Interfaces: Development and Rehabilitation for Motor Disability

2021 ◽  
Vol 65 (1) ◽  
pp. 1264-1268
Author(s):  
Ayushi Das ◽  
Dan Nathan-Roberts
Keyword(s):  
Motor Impairment ◽  
The United States ◽  
Brain Computer Interfaces ◽  
Motor Disability ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Communication Disabilities ◽  
The Face ◽  
Disability Research ◽  
Bci System

Around 61 million people in the United States suffer from different forms of disability; of these, 13.7% suffer from a motor disability. Research in brain–computer interfaces (BCIs) has focused on curbing communication disabilities due to motor impairment. Many challenges and future developments lie ahead in the BCI world. The research on paradigms that guide the way to make communication easy for a person with motor impairment is the pillar of the BCI system. The purpose of this paper is to synthesize the developments in non-invasive brain–computer interface and evaluate them. The authors discuss components of the BCI system and how it is formed. Neurofeedback based on different modalities is also analyzed. The results from initial studies have been successful, but paradigms and neurofeedback technologies have immense development potential, which can change the face of BCI systems for rehabilitation of motor disability.

Objective clinical assessment of motor function after experimental spinal cord injury in the rat

1977 ◽  
Vol 47 (4) ◽  
pp. 577-581 ◽  
Author(s):  
Alex S. Rivlin ◽  
Charles H. Tator
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Motor Function ◽  
Clinical Assessment ◽  
Low Cost ◽  
Content Type ◽  
Non Invasive ◽  
Experimental Spinal Cord Injury ◽  
Maximum Angle ◽  
Cord Injury

✓ A new method was developed for the clinical assessment of motor function in rats after experimental spinal cord injury. The method consists of placing the animal on an inclined plane which can be adjusted to provide a slope of varying grade, and then assessing the maximum angle of the plane at which the animal can maintain its position without falling. The method was used to quantitate motor function in normal rats and in rats subjected to myelectomy, and consistently showed major differences between the two groups. The method has many positive features: the plane is easy to construct and of low cost; and the test is rapid, non-invasive, repeatable, and consistent.

scholarly journals Single-Channel Region-Based Speller for Controlling Home Appliances

2022 ◽  
pp. 541-569
Author(s):  
Praveen Kumar Shukla ◽  
Rahul Kumar Chaurasiya ◽  
Shrish Verma
Keyword(s):  
Single Channel ◽  
Low Cost ◽  
P300 Speller ◽  
Cord Injury ◽  
Control Translation ◽  
Ann Classifier ◽  
The Brain ◽  
Channel Region ◽  
Bci System ◽  
Lateral Sclerosis

The brain-computer interface (BCI) system uses electroencephalography (EEG) signals for correspondence between the human and the outside world. This BCI communication system does not require any muscle action; hence, it can be controlled with the help of brain activities only. Therefore, this kind of system is helpful for patients, who are completely paralyzed or suffering from diseases like ALS (Amyotrophic Lateral Sclerosis), and spinal cord injury, etc., but having a normal functioning brain. A region-based P300 speller system for controlling home electronic appliances is proposed in this article. With the help of the proposed system, users can control and use appliances like an electronic door, fan, light, system, etc., without carrying out any physical movement. The experiments are conducted for five, ten, and fifteen trails for each subject. Among all classifiers, the ANN classifier provides the best off-line experiment accuracy of the order of 80% for fifteen flashes. Moreover, for the control translation, the Arduino module is also designed which is low cost and low power-based and physically controlled a device.

scholarly journals Single-Channel Region-Based Speller for Controlling Home Appliances

2020 ◽  
Vol 11 (4) ◽  
pp. 65-89
Author(s):  
Praveen Kumar Shukla ◽  
Rahul Kumar Chaurasiya ◽  
Shrish Verma
Keyword(s):  
Single Channel ◽  
Low Cost ◽  
P300 Speller ◽  
Cord Injury ◽  
Control Translation ◽  
Ann Classifier ◽  
The Brain ◽  
Channel Region ◽  
Bci System ◽  
Lateral Sclerosis

The brain-computer interface (BCI) system uses electroencephalography (EEG) signals for correspondence between the human and the outside world. This BCI communication system does not require any muscle action; hence, it can be controlled with the help of brain activities only. Therefore, this kind of system is helpful for patients, who are completely paralyzed or suffering from diseases like ALS (Amyotrophic Lateral Sclerosis), and spinal cord injury, etc., but having a normal functioning brain. A region-based P300 speller system for controlling home electronic appliances is proposed in this article. With the help of the proposed system, users can control and use appliances like an electronic door, fan, light, system, etc., without carrying out any physical movement. The experiments are conducted for five, ten, and fifteen trails for each subject. Among all classifiers, the ANN classifier provides the best off-line experiment accuracy of the order of 80% for fifteen flashes. Moreover, for the control translation, the Arduino module is also designed which is low cost and low power-based and physically controlled a device.

A Simple Device for Measurement of Load-Bearing Forces during Walking

1994 ◽  
Vol 208 (3) ◽  
pp. 159-162
Author(s):  
A J Harrison ◽  
R M Atkins ◽  
H J F Scott
Keyword(s):  
Low Cost ◽  
Pressure Gauge ◽  
Femoral Shaft ◽  
Elastic Tube ◽  
Human Gait ◽  
Small Data ◽  
Data Logger ◽  
Load Bearing ◽  
Non Invasive ◽  
General Mobility

A low-cost, portable, unobtrusive and non-invasive system has been developed, capable of measuring human gait forces over a 16 how-period, and characterized and proven in limited trials. The device comprises a foam insole containing a coiled air-filled elastic tube whose internal pressure (which increases under load) is measured by a pressure gauge. This signal is recorded by a small data logger worn around the patient's waist, and subsequently downloaded to a personal computer for analysis. Typical results are presented. The system will initially be used with patients recovering from tibial and femoral shaft fractures to monitor their day-long load-bearing patterns. The results of this study will be used to investigate a causal relationship between load bearing and the extent and rate of osteoporosis, which will be measured separately. In the future, additional studies of gait, load bearing and general mobility of patients suffering from conditions such as arthritis and amputation will be undertaken.

Abstract #29: Scaling Spinal Cord injury Models for Non-invasive Stimulation

2019 ◽  
Vol 12 (2) ◽  
pp. e10-e11
Author(s):  
Dennis Q. Truong ◽  
Catherine Maglione ◽  
Yishai Valter ◽  
Louis Zannou ◽  
A. Duke Shereen ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Non Invasive ◽  
Cord Injury ◽  
Injury Models

scholarly journals Non-invasive approaches to functional recovery after spinal cord injury: Therapeutic targets and multimodal device interventions

2021 ◽  
Vol 339 ◽  
pp. 113612
Author(s):  
Claudio Pizzolato ◽  
Mehmet A. Gunduz ◽  
Dinesh Palipana ◽  
Jingnan Wu ◽  
Gary Grant ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Functional Recovery ◽  
Therapeutic Targets ◽  
Non Invasive ◽  
Cord Injury

scholarly journals Development of priorities for a Canadian strategy to advance activity-based therapies after spinal cord injury

Spinal Cord ◽  
2021 ◽  
Author(s):  
Kristin E. Musselman ◽  
Kristen Walden ◽  
Vanessa K. Noonan ◽  
Hope Jervis-Rademeyer ◽  
Nancy Thorogood ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Stroke Rehabilitation ◽  
Participatory Design ◽  
Best Practice ◽  
Optimal Timing ◽  
Activity Levels ◽  
Cord Injury ◽  
Healthcare Administrators ◽  
Psychosocial Benefits

Abstract Study Design Participatory design. Objectives Activity-based therapies (ABT) have physical and psychosocial benefits for individuals with spinal cord injury (SCI). A Canadian ABT summit was held to: (1) identify methods used in stroke rehabilitation that may be appropriate for SCI; (2) understand the current state of ABT activities in Canada; and (3) identify priorities for ABT research and care for the next five years. Setting Stakeholder-engaged meeting at a tertiary rehabilitation hospital. Methods Thirty-nine stakeholders, including individuals with SCI, frontline clinicians, healthcare administrators, researchers, funders and health policy experts, attended. Two participants were note-takers. Priority identification occurred through input from stakeholder groups, followed by individual voting. Conventional content analysis was used to synthesize the information in the meeting notes. Results The strengths of ABT in stroke rehabilitation included clear and clinically feasible definitions, measurements and interventions, and recognized requirements for implementation (e.g. behavior change, partnerships). Knowledge gaps concerning ABT activities in Canada were identified for acute and community settings, non-traumatic populations, and the interventions, equipment and standardized measures (i.e. upper limb, activity levels) used. Five priorities for ABT across the continuum of care were identified: (1) Identify current ABT activities; (2) Create a network to facilitate dialog; (3) Track engagement in ABT activities; (4) Develop and implement best practice recommendations; and (5) Study optimal timing, methods, and dose of ABT. Working groups were formed to address priorities 1–3. Conclusions The priorities will guide SCI research and care activities in Canada over the next five years. Sponsorship Praxis Spinal Cord Institute.

scholarly journals The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Supakorn Harnsoongnoen ◽  
Nuananong Jaroensuk
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Low Cost ◽  
Measuring System ◽  
Coefficient Of Determination ◽  
Water Displacement ◽  
Non Invasive ◽  
Future Potential ◽  
Non Destructive ◽  
Egg Freshness

AbstractThe water displacement and flotation are two of the most accurate and rapid methods for grading and assessing freshness of agricultural products based on density determination. However, these techniques are still not suitable for use in agricultural inspections of products such as eggs that absorb water which can be considered intrusive or destructive and can affect the result of measurements. Here we present a novel proposal for a method of non-destructive, non-invasive, low cost, simple and real—time monitoring of the grading and freshness assessment of eggs based on density detection using machine vision and a weighing sensor. This is the first proposal that divides egg freshness into intervals through density measurements. The machine vision system was developed for the measurement of external physical characteristics (length and breadth) of eggs for evaluating their volume. The weighing system was developed for the measurement of the weight of the egg. Egg weight and volume were used to calculate density for grading and egg freshness assessment. The proposed system could measure the weight, volume and density with an accuracy of 99.88%, 98.26% and 99.02%, respectively. The results showed that the weight and freshness of eggs stored at room temperature decreased with storage time. The relationship between density and percentage of freshness was linear for the all sizes of eggs, the coefficient of determination (R2) of 0.9982, 0.9999, 0.9996, 0.9996 and 0.9994 for classified egg size classified 0, 1, 2, 3 and 4, respectively. This study shows that egg freshness can be determined through density without using water to test for water displacement or egg flotation which has future potential as a measuring system important for the poultry industry.

scholarly journals Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 789
Author(s):  
David Kreuzer ◽  
Michael Munz
Keyword(s):  
Gait Analysis ◽  
Time Series Data ◽  
Low Cost ◽  
Analysis Data ◽  
Gait Disorders ◽  
Human Gait ◽  
Series Data ◽  
Ageing Society ◽  
Time Motion ◽  
Gait Phase

With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis data often alters surgical recommendations. For that reason, the early and systematic diagnostic treatment of gait disorders can spare a lot of suffering. As modern gait analysis systems are, in most cases, still very costly, many patients are not privileged enough to have access to comparable therapies. Low-cost systems such as inertial measurement units (IMUs) still pose major challenges, but offer possibilities for automatic real-time motion analysis. In this paper, we present a new approach to reliably detect human gait phases, using IMUs and machine learning methods. This approach should form the foundation of a new medical device to be used for gait analysis. A model is presented combining deep 2D-convolutional and LSTM networks to perform a classification task; it predicts the current gait phase with an accuracy of over 92% on an unseen subject, differentiating between five different phases. In the course of the paper, different approaches to optimize the performance of the model are presented and evaluated.

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