Using Ensemble Machine Learning: A Multifaceted Approach to Predicting Rehabilitation Outcomes | Chapter 2 |Disease and Health Research: New Insights Vol. 8

The field of rehabilitation for individuals with neuromuscular diseases has witnessed significant advancements, driven by the convergence of robotics, assistive technologies, and machine learning. These innovations are reshaping how rehabilitation is approached, offering new possibilities for personalized, effective, and efficient treatments that were previously unattainable. This chapter delves into the transformative role of these technologies, emphasizing the development and application of ensemble machine learning models specifically designed to predict rehabilitation outcomes. The study provides a comprehensive overview of the current state of rehabilitation technologies, focusing on the integration of robotics and orthotics into therapeutic practices. Robotics and assistive technologies, such as powered exoskeletons and smart orthoses, are becoming increasingly vital in enhancing the mobility and autonomy of patients with neuromuscular impairments. These devices are not only extending the capabilities of traditional rehabilitation methods but are also enabling more precise and controlled therapeutic interventions that can be tailored to the unique needs of each patient. In the following sections, this study explores the intricate design and functionality of orthoses and assistive robots, illustrating how these devices are engineered to support and augment human movement. The chapter discusses various types of orthotic devices, from simple mechanical braces to advanced robotic systems that interact seamlessly with the human body, adapting to the user's movements and providing real-time feedback. These innovations are shown to significantly improve the effectiveness of rehabilitation exercises, accelerate recovery times, and enhance overall patient outcomes. Central to this discussion is the introduction of a novel, multifaceted approach to predicting rehabilitation outcomes using ensemble machine learning models. These models, which combine the strengths of multiple algorithms, are designed to capture complex patterns in patient data, thereby providing more accurate and reliable predictions of rehabilitation success. This study present a detailed analysis of the development process of these models, including the selection of relevant features, the training and validation of the models, and the implementation of advanced visualization techniques to interpret the results. Through rigorous cross-validation and real-world testing, the robustness of these ensemble models is demonstrated in predicting outcomes across diverse patient populations. The results indicate that these models outperform traditional predictive methods, offering superior accuracy and the ability to generalize across different patient scenarios. The chapter also includes a series of visualizations that illustrate the importance of various features in the prediction process, the relationship between actual and predicted outcomes, and the distribution of model residuals. Furthermore, this study discusses the implications of these findings for clinical practice. The ability to predict rehabilitation outcomes with high precision allows clinicians to tailor interventions more effectively, optimizing treatment plans for individual patients and potentially reducing recovery times. The chapter concludes by considering the broader impact of these technologies on the healthcare system, highlighting the potential for machine learning models to drive innovation in personalized medicine and rehabilitation. Looking forward, several key areas for future research were identified including the integration of real-time data analytics into rehabilitation devices, the development of more sophisticated models that can adapt to changing patient conditions, and the exploration of new forms of human-machine interaction that can further enhance the efficacy of rehabilitation. The ongoing evolution of these technologies promises to open new frontiers in the treatment of neuromuscular diseases, ultimately improving the quality of life for millions of patients worldwide.

 

Author (s) Details

Rocco de Filippis
Institute of Psychopathology, Rome, Italy.

 

Abdullah Al Foysal
Computer Engineering (AI), University of Genova, Genova, Italy.

 

Please see the book here:- https://doi.org/10.9734/bpi/dhrni/v8/2486

 

Disaster Challenges for Persons with Disabilities: Risks, Barriers, and Inclusive Strategies | Chapter 11 | New Ideas Concerning Arts and Social Studies Vol. 1

Persons with disabilities face various challenges globally in arrears to insufficient policies and moralities, restricted resources, approachability issues, deleterious perceptions, an absence of information and communication, deficient financial aid, and rejection from decisions that considerably impact their lives. In specific, disasters can make disabled people even more susceptible. It may be additional grim for them to abscond jeopardy during emergencies, and they run the menace of trailing indispensable assistive technology like spectacles, hearing assistance, mobility aids, and prescription drugs. People with disabilities experience an assortment of effects from catastrophes, counting an augmented mortality risk, damage, and property loss. They are frequently less expected to obtain initial cautions formerly a disaster, discovery means of evacuation and public shelters are dreadful to navigate and face a deficiency of sufficient care and shelter choices, as well as being unnoticed in relief and recovery efforts. Beside with statistics on the figures of disabled people exterminated and incapacitated during calamities around the world, this study demonstrates the various problems stumble upon by people with disabilities through disaster situations. This chapter is based on reviewing the reports and studies endorsed in disability and disaster in a descriptive nature. It describes the difficulties confronted by various disability communities during and after diverse catastrophes across the world. It also looks at several policy initiatives preordained to address these glitches on a global scale.

 

Author (s) Details

A. Puvi Lakshmi
Independent Researcher, Research Fellow at University of Madras, Tiruvallur, Tamil Nadu, India.

 

Please see the book here: - https://doi.org/10.9734/bpi/nicass/v1/4885

Determining the Factors Influencing the Use of Assistive Technology in Teaching Mathematics to Visually Impaired Learners in Kenyan Special Primary Schools | Chapter 4 | Selected Topics in Humanities and Social Sciences Vol. 4

 In Kenyan primary schools, pupils with visual impairments (VI) do poorly in Mathematics when compared to other examinable subjects. In this regard, the goal of this research was to find out what factors influence the use of assistive technology (AT) at teaching mathematics to VI students in Kenya's special primary schools for the blind. The study recruited 76 VI students in grades seven and eight, as well as 10 Mathematics teachers from five special primary schools, using a descriptive research approach. The ten teachers were picked through a planned sample process, whereas the 20 VI students were chosen through a random selection process. Simple random sampling was used to choose pupils. An observation checklist and interview instructions were used to collect data. Insufficient time for syllabus coverage, high costs of AT devices, insufficient teacher training in AT device use, limited curricula, and negative attitudes of students were highlighted as major factors influencing AT device use in the study. According to the report, the government should spend more money on assistive technology equipment and conduct regular classroom monitoring to guarantee that AT gadgets are used effectively.

Author (S) Details

Dr. Chege Mary Wairimu
Department of Special Needs Education, Kenyatta University, Kenya.

Dr. Joel M. Chomba
Department of Special Needs Education, Kenyatta University, Kenya.

Dr. Beatrice Bunyasi Awori
Department of Special Needs Education, Kenyatta University, Kenya.

View Book :- https://stm.bookpi.org/STHSS-V4/article/view/2857

Sole Mate an Applicative Approach: Safe Pathfinding by Obstacle Detection and Distance Estimation for the Blind | Chapter 5 | Recent Developments in Engineering Research Vol.2

 

The world has increased its demand for assistive technology (AT). There are a lot of researches and
developments going on with respect to AT. Among the AT devices which are being developed, the
need for a reliable and less expensive device which serves as an assistance for a visually challenged
person is in serious demand all around the world. We, therefore, intend to provide a solution for this
by constructing a device that has the capability to detect the obstacles within a given range for a
visually challenged person and alerting the person about the obstacles. This involves various
components like a camera for image detection, an ultrasonic distance sensor for distance estimation
and a vibration motor which works on the principle of Haptic feedback and rotates with varied
intensities depending on how far the obstacle is from the user. This paper presents a model which is a
part of the footwear of the user and hence, no additional device is required to hold onto for assistance.
The model involves the use of a microcontroller, a camera, to dynamically perceive the obstacles and
a haptic feedback system to alert the person about the same. The camera dynamically acquires the
real time video footage which is further processed by the microcontroller to detect the obstacles.
Simultaneously, one more algorithm is being executed to estimate the distance with the help of an
ultrasonic distance sensor. Depending on the distance, the frequency of the vibration motor, which
acts as the output for notifying the user about the obstacle, is varied (haptic feedback). With this
system, a visually challenged person will be able to avoid the obstacles successfully without the use
of any additional device.

Author(s) Details

Abhigna R.
Department of Electronics and Communication Engineering, BNM Institute of Technology, Bengaluru, India.

Amith K. Shinde
Department of Electronics and Communication Engineering, BNM Institute of Technology, Bengaluru, India.

Ashwin Sundaresh
Department of Electronics and Communication Engineering, BNM Institute of Technology, Bengaluru, India.

Dheeraj P. R.
Department of Electronics and Communication Engineering, BNM Institute of Technology, Bengaluru, India.

Dr. P. A. Vijaya
Department of Electronics and Communication Engineering, BNM Institute of Technology, Bengaluru, India.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/236