Application of Bio-Feedback in Neurodevelopmental Disorders

2019 ◽  
pp. 211-235
Author(s):  
Srinivasan Venkatesan ◽  
Hariharan Venkataraman
Keyword(s):  
Cost Effectiveness ◽  
Neurodevelopmental Disorders ◽  
Conclusive Evidence ◽  
Self Control ◽  
Computer Assisted ◽  
Evidence Based ◽  
Confounding Factors ◽  
Placebo Effects ◽  
Non Invasive ◽  
Bodily Function

Biofeedback is a non-invasive process to electronically monitor normal automatic bodily function to acquire its voluntary control. Traditional medical models place the onus on the physician to “cure” the illness. Biofeedback places responsibility on the patient to gain self-control. Its application as evidence-based practice in neurodevelopmental disorders is a nascent, unexplored, and debated area of study. This chapter outlines the meaning, nature, types, protocols, procedure, practices, challenges, benefits, and limitations in its use. Its history is traced for efficacy vis-à-vis other treatments, and other issues like cost-effectiveness, certification of professionals, gadget-enabled, and computer-assisted variants. Studies have attempted, albeit with methodological limitations, to validate its utility for neurodevelopmental disorders without any definitive or conclusive evidence for or against its use given the inability to replicate results, control or exclude confounding factors, placebo effects, and/or bias. An agenda for prospective research is given.

scholarly journals Incentives and Support Programs to Improve Adherence to Tuberculosis Treatment

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Charlotte Wells ◽  
Melissa Severn
Keyword(s):  
High Risk ◽  
Methodological Quality ◽  
Clinical Effectiveness ◽  
Conclusive Evidence ◽  
Active Tuberculosis ◽  
Tuberculosis Treatment ◽  
Support Programs ◽  
Evidence Based ◽  
Evidence Based Guidelines

Three overviews of reviews and 11 systematic reviews were identified regarding the clinical effectiveness of adherence incentives in those who require assistance to complete their tuberculosis treatment. Four evidence-based guidelines were identified that provided recommendations regarding the use of adherence incentives in those who require assistance completing their tuberculosis treatment. The reported clinical effectiveness of adherence incentives for patients with tuberculosis was mixed. There were no detrimental effects of providing incentives, but there was also no conclusive evidence pointing to a clinical benefit. The overall quality of the included reviews was moderate to high. The included guidelines recommended that incentives and enablers be included as a part of a patient-centred strategy for treatment and for patients with active tuberculosis or patients at high risk; however, the evidence formulating these recommendations was of low certainty or quality. Two of the included guidelines were of high methodological quality, and 2 were of lower methodological quality.

Cost-effectiveness of an Evidence-Based Childhood Asthma Intervention in Real-World Primary Care Settings

2018 ◽  
Vol 41 (3) ◽  
pp. 213-224 ◽  
Author(s):  
Avi Dor ◽  
Qian Luo ◽  
Maya Tuchman Gerstein ◽  
Floyd Malveaux ◽  
Herman Mitchell ◽  
...  
Keyword(s):  
Primary Care ◽  
Cost Effectiveness ◽  
Real World ◽  
Childhood Asthma ◽  
Evidence Based ◽  
Primary Care Settings ◽  
Asthma Intervention

ICA as Pattern Recognition Technique for Gesture Identification

2013 ◽  
pp. 530-549
Author(s):  
Ganesh Naik ◽  
Dinesh Kant Kumar ◽  
Sridhar Arjunan
Keyword(s):  
Pattern Recognition ◽  
Marked Improvement ◽  
Mitigation Strategy ◽  
The Other ◽  
Computer Assisted ◽  
Pattern Recognition Techniques ◽  
Non Invasive ◽  
Robust System ◽  
Network Weight ◽  
Recognition Technique

In recent times there is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and other computer assisted devices. Surface Electromyogram (sEMG) is a non-invasive measure of the muscle activities but is not reliable because there are multiple simultaneously active muscles. This research first establishes the conditions for the applicability of Independent Component Analysis (ICA) pattern recognition techniques for sEMG. Shortcomings related to order and magnitude ambiguity have been identified and a mitigation strategy has been developed by using a set of unmixing matrix and neural network weight matrix corresponding to the specific user. The experimental results demonstrate a marked improvement in the accuracy. The other advantages of this system are that it is suitable for real time operations and it is easy to train by a lay user.

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