The Role of Music Therapy in Supporting Intellectually Disabled Youth in Senegal

Introduction: Music therapy is a practice for helping and supporting people with intellectual and relational difficulties. This study illustrated the benefits of music therapy for young people living with intellectual disabilities (YLID) in an African context. Methodology: This study investigated six young individuals with intellectual disabilities who had undergone three years of music therapy. They were participants in the inclusive non-academic training program at the National School of Arts in Dakar from 2017 to 2019. Data collection utilized individual interviews with the youths, evaluation grids from teachers and psychiatrists. Guardians provided informed consent along with the assent of the young participants. Results: The six young were aged between 18 and 30 years old, with an average age of 24.6 years. Four of the YLID were male. Three young people with intellectual disabilities had delayed psychomotor development. Observations revealed the beneficial influence of music therapy on the health and well-being of young individuals. Music played a role in alleviating stress and anxiety among youth with intellectual disabilities (YLID), enhancing their mood and mental health. It assisted in navigating challenging situations and heightened alertness among YLID. Additionally, music therapy contributed to improvements in dyslexia, fine and gross motor skills, and memory development among intellectually disabled youth, ultimately facilitating their integration into society. Conclusion: In light of our results, music therapy makes a major contribution to the empowerment of YLID. Engaging in musical activities helps young people connect with others through instrumental expression and a sense of accomplishment. By facilitating music therapy, it becomes possible to combat discrimination and stigmatization, thus promoting the social inclusion of intellectually disabled youth. Therefore, it is important to promote music therapy in Senegal to meet the needs of YLID.

Associated Factors of Dental Caries among Intellectually Disabled Persons in Specialized Centers of Ouagadougou

Introduction: Several studies have reported a high prevalence of dental caries among the people with intellectual disabilities. In order to identify the sociodemographic, clinical and behavioral factors associated with it, we conducted a study in specialized centers welcoming subjects with intellectual disabilities in Ouagadougou (Burkina Faso). Objectives: The objective of this study was to identify the factors associated with it among intellectually disabled in the specialized centers of Ouagadougou. Methods: The study was cross-sectional analytical. The survey took place between November 2020 and January 2021 among people with intellectual disabilities in reception centers receiving a grant from the Ministry of Women, National Solidarity, Family and Humanitarian Action. Data collection was done with a questionnaire adapted from the World Health Organization (WHO) and dental caries was recorded according to WHO criteria in decayed, missing or filled teeth generating a DMF index. Logistic regression was used to identify factors associated with the presence of dental caries. Results: A total of 193 participants were included in the study with an average age of 12.4 years ± 7.9. The overall prevalence of caries measured with the DMF index was 58.6% 95% CI [51.2 - 65.8]. It was significantly associated with female gender (OR = 4.2;95% CI [1.4 - 12.5], p = 0,01), the clinical form of mental illness (epilepsy OR = 3.8 95%;CI [1.2 - 12.7], p = 0,02, trisomy OR = 5.0;95% CI [1.1 - 22.9], p = 0,03, motor autonomy OR = 0.2 95%;CI [0.1 - 0.7], p = 0,01) and at use of toothpaste OR = 9.33;95% CI [1.05 - 82.7], p = 0,04. Conclusion: Dental caries remains a very present pathology in most people living with an intellectual disability. Also, it is necessary to put in place prevention strategies to reduce its prevalence and improve access to oral care for these people.

Deep Transfer Learning Driven Automated Fall Detection for Quality of Living of Disabled Persons

Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities,a fall event is an important cause of mortality and post-traumatic complications.Therefore,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)classifier.In the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection.This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes.These sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during emergencies.Falling is one of the most important problems confronted by older people and people with movement disabilities.Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people.But,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor environments.Currently,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements.Against this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)model.The presented IWODL-FDDP model aims to identify the fall events to assist disabled people.The presented IWODLFDDP model applies an image filtering approach to pre-process the image.Besides,the EfficientNet-B0 model is utilized to generate valuable feature vector sets.Next,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall events.Finally,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the work.The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.

Parameter-Tuned Deep Learning-Enabled Activity Recognition for Disabled People

Elderly or disabled people can be supported by a human activity recognition(HAR)system that monitors their activity intervenes and pat-terns in case of changes in their behaviors or critical events have occurred.An automated HAR could assist these persons to have a more indepen-dent life.Providing appropriate and accurate data regarding the activity is the most crucial computation task in the activity recognition system.With the fast development of neural networks,computing,and machine learning algorithms,HAR system based on wearable sensors has gained popularity in several areas,such as medical services,smart homes,improving human communication with computers,security systems,healthcare for the elderly,mechanization in industry,robot monitoring system,monitoring athlete train-ing,and rehabilitation systems.In this view,this study develops an improved pelican optimization with deep transfer learning enabled HAR(IPODTL-HAR)system for disabled persons.The major goal of the IPODTL-HAR method was recognizing the human activities for disabled person and improve the quality of living.The presented IPODTL-HAR model follows data pre-processing for improvising the quality of the data.Besides,EfficientNet model is applied to derive a useful set of feature vectors and the hyperparameters are adjusted by the use of Nadam optimizer.Finally,the IPO with deep belief network(DBN)model is utilized for the recognition and classification of human activities.The utilization of Nadam optimizer and IPO algorithm helps in effectually tuning the hyperparameters related to the EfficientNet and DBN models respectively.The experimental validation of the IPODTL-HAR method is tested using benchmark dataset.Extensive comparison study highlighted the betterment of the IPODTL-HAR model over recent state of art HAR approaches interms of different measures.

Hand Gesture Recognition for Disabled People Using Bayesian Optimization with Transfer Learning

Sign language recognition can be treated as one of the efficient solu-tions for disabled people to communicate with others.It helps them to convey the required data by the use of sign language with no issues.The latest develop-ments in computer vision and image processing techniques can be accurately uti-lized for the sign recognition process by disabled people.American Sign Language(ASL)detection was challenging because of the enhancing intraclass similarity and higher complexity.This article develops a new Bayesian Optimiza-tion with Deep Learning-Driven Hand Gesture Recognition Based Sign Language Communication(BODL-HGRSLC)for Disabled People.The BODL-HGRSLC technique aims to recognize the hand gestures for disabled people’s communica-tion.The presented BODL-HGRSLC technique integrates the concepts of compu-ter vision(CV)and DL models.In the presented BODL-HGRSLC technique,a deep convolutional neural network-based residual network(ResNet)model is applied for feature extraction.Besides,the presented BODL-HGRSLC model uses Bayesian optimization for the hyperparameter tuning process.At last,a bidir-ectional gated recurrent unit(BiGRU)model is exploited for the HGR procedure.A wide range of experiments was conducted to demonstrate the enhanced perfor-mance of the presented BODL-HGRSLC model.The comprehensive comparison study reported the improvements of the BODL-HGRSLC model over other DL models with maximum accuracy of 99.75%.

Automated Disabled People Fall Detection Using Cuckoo Search with Mobile Networks

Falls are the most common concern among older adults or disabled peo-ple who use scooters and wheelchairs.The early detection of disabled persons’falls is required to increase the living rate of an individual or provide support to them whenever required.In recent times,the arrival of the Internet of Things(IoT),smartphones,Artificial Intelligence(AI),wearables and so on make it easy to design fall detection mechanisms for smart homecare.The current study devel-ops an Automated Disabled People Fall Detection using Cuckoo Search Optimi-zation with Mobile Networks(ADPFD-CSOMN)model.The proposed model’s major aim is to detect and distinguish fall events from non-fall events automati-cally.To attain this,the presented ADPFD-CSOMN technique incorporates the design of the MobileNet model for the feature extraction process.Next,the CSO-based hyperparameter tuning process is executed for the MobileNet model,which shows the paper’s novelty.Finally,the Radial Basis Function(RBF)clas-sification model recognises and classifies the instances as either fall or non-fall.In order to validate the betterment of the proposed ADPFD-CSOMN model,a com-prehensive experimental analysis was conducted.The results confirmed the enhanced fall classification outcomes of the ADPFD-CSOMN model over other approaches with an accuracy of 99.17%.

A Smart Room to Promote Autonomy of Disabled People due to Stroke

A cerebral vascular accident,known as common language stroke,is one of the main causes of mortality and remains the primary cause of acquired disabilities in adults.Those disabled people spend most of their time at home in their living rooms.In most cases,appliances of a living room(TV,light,cooler/heater,window blinds,etc.)are generally controlled by direct manipulation of a set of remote controls.Handling many remote controls can be disturbing and inappropriate for these people.In addition,in many cases these people could be alone at home and must open the door for visitors after their identification by either moving to the door or using an intercom system which requires in both cases a physical activity.Furthermore,these people need a continuous health monitoring especially blood pressure to avoid a recurrent stroke.Smart spaces and assisted technologies would be beneficial to assist person with disabilities to live indepen-dently,enhance their quality of life and empower their autonomy.A complete sys-tem which improves and facilitates the daily life and covers all aspects such as appliances automation,appropriate interaction mode and health monitoring of these people is still lacking.The aim of this work is to create a safe and high-quality living environment for persons with disabilities to enable them to live more independently by automating the operation of a living room appliances according to the current context without the need to use remote control devices,the use of a suitable interaction modality with appliances that require direct interaction and a remote health monitoring system which can alert relatives and caregivers in case of an emergency.

Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

Assistive devices for disabled people with the help of Brain-Computer Interaction(BCI)technology are becoming vital bio-medical engineering.People with physical disabilities need some assistive devices to perform their daily tasks.In these devices,higher latency factors need to be addressed appropriately.Therefore,the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation.The proposed architecture is capable to communicate between different modules of the system by adopting an automotive,intelligent data processing and classification approach.Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion.Think-Net Convolutional Neural Network(TN-CNN)architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification.Data collection and processing are the responsibility of the central integrated server for system load minimization.Testing of implemented architecture and deep learning model shows excellent results.The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism.The training and testing results are 99%and 93%for custom model implementation based on TN-CNN.The proposed real-time architecture is capable of intelligent data processing unit with fewer errors,and it will benefit assistive devices working on the local server and cloud server.

Achievements in Safeguarding Disabled People's Rights——An Interview with Shen Zhifei,deputy general director of the China Disabled Persons' Federation

There are about 83 million disabled people in China, accounting for 6.34% of the total population, according to a national survey in 2006. As a disadvantaged group, the rights and interests of the disabled are arousing more concerns from all walks of society in the country. Following is Human Rights＇ interview with Shen Zhifei, deputy general director of the China Disabled Persons＇ Federation, on the legal guarantee of the special group＇s rights and interests, their rehabilitation, education and employment.

The Need and Its Influence Factors for Community-based Rehabilitation Services for Disabled Persons in One District in Beijing

Purpose To explore the needs for basic community-based rehabilitation services for disabled persons in Xuanwu District, Beijing, China, and to identify factors which influence disabled persons to accept rehabilitation services. Method One hundred and eight disabled persons were selected by systematic sampling and simple random sampling to assess their needs for community-based rehabilitation services. Results Of the interviewees, 57A% needed the community-based rehabilitation services, but only 13.9% took advantage of it. The main factors influencing the interviewees to accept these services were cost （P〈0.05）, knowledge about rehabilitation medicine （P〈0.05）; and the belief in the therapeutic benefit of the community-based rehabilitation service （P〈0.05）. Conclusion A considerable gap exists between the supply of community-based rehabilitation services in Beijing and the needs for these services by disabled residents underscoring the need for improved availability, and for additional research.

Factors associated with activities of daily living among the disabled elders with stroke

Objective:To determine the factors associated with activities of daily living(ADL)among the disabled elders post-stroke.Methods:A total of 158 patients were chosen from community health service stations in eighteen regions of Tianjin city by convenience sampling from March to November in 2013.The Barthel Index(BI)and the short-formmini-nutritional assessment(MNA-SF)were used to evaluate the ADL,the nutritional status respectively.Statistical analysis was performed using independent sample t-test,one-way ANOVA,Pearson correlation and multiple linear regression analysis.Barthel ADL index was the main outcome.Results:The mean score of ADL was 50.50±27.125.The multiple linear regression showed that the factors which had significant impact on ADL were stroke frequency,types of stroke,nutritional status,financial status,and age.Conclusions:Disabled elders with recurrent strokes,hemorrhagic stroke,dependent financial resources,older age,worse nutritional status and living with family had poorer strokerelated outcome.Healthcare providers can discover the high-risk groups of disability and implement individualized preventive interventions in accordance with the related factors.

Investigation and Countermeasures on the Health-Promoting Lifestyle of the Disabled Elderly in a Rural Area

Objective： To investigate the level of health-promoting lifestyle in the disabled elderly in a rural area and to provide useful countermeasures for health care workers and the government to improve the elderly＇ s lifestyle behaviors. Methods： A total of 446 disabled elderly people were recruited and examined using the General Questionnaire and Health-Promoting Lifestyle Profile II. Results： The mean score of the health-promoting lifestyle was 109.73（ SD = 16.80）, in which 90.6 percent of the disabled elderly demonstrated unhealthy lifestyle behaviors. The average score for each of the subscales includedthe following： interpersonal relations, stress management, nutrition, self-actualization, health responsibility, and sports activity. Conclusions： The health-promoting lifestyle of the disabled elderly needs to be improved; health care workers and the government should pay more attention to the countermeasures that can improve the lifestyle behaviors and promote the health of the disabled elderly.

Physical Accessibility for Disabled People: Analysis of Toilet Facilities in Primary Health Care Units

Background: Accessibility enables the effective participation of disabled persons in public and private areas and the use of urban equipment and street furniture. Objective: This study aimed to analyze the physical accessibility of toilet facilities in urban and rural primary health care units. Methods: It was a quantitative descriptive study conducted in 157 Primary Health Care Units of 16 municipalities in the Baturité Massif region, Ceará, Brazil. The study took place from August 2014 to May 2015, with a checklist type form, designed from the Technical Standard 9050 of the Brazilian National Standards Organization, specifying the ideal access conditions established by law to allow mobility of persons with physical disabilities. Data were processed through the Statistical Package for the Social Sciences software and organized into tables. Results: Toilets designed for physically disabled people were accessible as the location and signs (59.9%), identified with symbols for males and females (57.3%);however, the doorway width was smaller than needed to accommodate a wheelchair (77.7%). Inside the bathroom, only the forward approach was possible (59.9%). Grab bars positioned on the side and rear walls were inadequate or nonexistent (67.6%);toilet seats (91.1%) and toilet paper dispensers (96.2%) were mostly in inaccessible heights;flush controls in appropriate height (59.2%) and activated by light pressure (58%). Sinks without pedestal (51%), but higher than recommended (80.3%) and without single handle faucets (95.6%). It was verified that the toilets of basic health units located in urban areas had better accessibility conditions compared to those in rural areas. Conclusion: Results showed that the analyzed units presented physical inaccessibility in some toilet facilities, making it difficult or even impossible the accessibility for the disabled. The inclusion of accessibility features in health services for this clientele provides equal opportunities and social inclusion.

THE CHARACTERISTIC， DISTRIBUTION AND CAUSING FACTORC OF THE DISABLED IN HENAN PROVINCE

Henan Province is not only one of the provinces with the most POpulation, but also one of the provinces with the most disabled people in China. The characteristics of the disabled People, the distribution law of six typeS of the disabled, and the reason c

Sign Language Recognition and Classification Model to Enhance Quality of Disabled People

Sign language recognition can be considered as an effective solution for disabled people to communicate with others.It helps them in conveying the intended information using sign languages without any challenges.Recent advancements in computer vision and image processing techniques can be leveraged to detect and classify the signs used by disabled people in an effective manner.Metaheuristic optimization algorithms can be designed in a manner such that it fine tunes the hyper parameters,used in Deep Learning(DL)models as the latter considerably impacts the classification results.With this motivation,the current study designs the Optimal Deep Transfer Learning Driven Sign Language Recognition and Classification(ODTL-SLRC)model for disabled people.The aim of the proposed ODTL-SLRC technique is to recognize and classify sign languages used by disabled people.The proposed ODTL-SLRC technique derives EfficientNet model to generate a collection of useful feature vectors.In addition,the hyper parameters involved in EfficientNet model are fine-tuned with the help of HGSO algorithm.Moreover,Bidirectional Long Short Term Memory(BiLSTM)technique is employed for sign language classification.The proposed ODTL-SLRC technique was experimentally validated using benchmark dataset and the results were inspected under several measures.The comparative analysis results established the superior performance of the proposed ODTL-SLRC technique over recent approaches in terms of efficiency.

Poverty Alleviation by Helping the Disabled in Impoverished Mountainous Areas of Western China:Taking Luquan Yi and Miao Autonomous County in Yunnan Province as an Example

Disabled people are the poorest in the rural poor with the biggest difficulty in getting rid of poverty and the highest rate of returning to poverty.They are one of the key groups in rural poverty alleviation work.This is the key and difficult point of winning the fight against poverty.Luquan County is a national poverty-stricken county with a large number of disabled people.Since the implementation of the fight against poverty in 2016,the county has introduced a series of policies and measures to help the disabled to get rid of poverty from housing,medical care,schooling for children of the disabled and income increase,and good results have been achieved in poverty alleviation,making important contributions to the poverty alleviation of the county.Based on field research and interviews,this paper analyzes and condenses the specific practices,main results and successful experience of the poverty alleviation model in Luquan County,Yunnan Province to provide necessary reference for the targeted poverty alleviation by helping the disabled in Yunnan Province and other provinces.

Psychoactive Substance Use and Disabled People: An Integrative Review

This study aimed to analyze the evidence available in the literature about psychoactive substance use by persons with disabilities. We used integrative review of literature in SciELO, PubMed, SCOPUS, BDENF, and PsychINFO databases, with the descriptors: pessoas com deficiência;transtornos relacionados a uso de substancias;disabled persons;and substance-related disorders. Twelve articles were included in the sample. Data were analyzed and discussed descriptively. The studies’ results, with scientific evidence, were synthesized on two main themes: substance use by people with disabilities, and access of people with disabilities to treatment for substance-related disorders. The scientific evidences identified in studies reported disability as a risk factor for substance abuse, and also reported that disabled persons did not have access to treatment and rehabilitation services for substance abuse. Finally, the studies analyzed pointed to the need for scientific research on this topic.

Path Exploration for Long-term Care of Rural Disabled Elderly: A Case Study of G Town in South Shaanxi

Field survey of G Town in South Shaanxi indicates that current situations and existing problems of rural disabled elderly. According to these situations and problems,this paper analyzes difficulties in long-term care of rural disabled elderly. From the perspective of welfare pluralism,it comes up with development paths for long-term care of rural disabled elderly with joint participation of government,communities,non-profit organizations on the basis of the family endowment.