Hearing Impaired People Research Articles

Utilization of Detection of Non-Speech Sound for Sustainable Quality of Life for Deaf and Hearing-Impaired People: A Systematic Literature Review

Several assistive technologies (ATs) have been manufactured and tested to alleviate the challenges of deaf or hearing-impaired people (DHI). One such technology is sound detection, which has the potential to enhance the experiences of DHI individuals and provide them with new opportunities. However, there is a lack of sufficient research on using sound detection as an assistive technology, specifically for DHI individuals. This systematic literature review (SLR) aims to shed light on the application of non-verbal sound detection technology in skill development for DHI individuals. This SLR encompassed recent, high-quality studies from the prestigious databases of IEEE, ScienceDirect, Scopus, and Web of Science from 2014 to 2023. Twenty-six articles that met the eligibility criteria were carefully analyzed and synthesized. The findings of this study underscore the significance of utilizing sound detection technology to aid DHI individuals in achieving independence, access to information, and safety. It is recommended that additional studies be conducted to explore the use of sound detection tools as assistive technology, to enhance DHI individual’s sustainable quality of life.

STATIC SIGN LANGUAGE TRANSLATOR USING HAND GESTURE AND SPEECH RECOGNITION

Communication between ordinary and deaf people often has issues because ordinary people lack knowledge of sign language. This research aims to help ordinary people communicate with hearing impaired (deaf) people using sign language. This research aims to produce an Android-based mobile app that can translate static sign language using hand movements into text and also convert the spoken voice into sign language using speech recognition. The framework in this research for hand gesture detection uses the MediaPipe software program. This framework allows the creation of apps that translate hand movements into text that help ordinary people understand sign language. Speech recognition in this research uses the Android speech library. This research succeeded in detecting static letters of the alphabet from the a-z and numbers 0-9. Tests of 540 hand gestures carried out in the morning, afternoon, evening, and night had an average detection time of 4.37 seconds. The fastest object detection times were in the morning at a distance of 30 cm with an average detection time of 2.5 seconds. Based on acceptance testing, 83.13% of the features in this static sign language translator have met the users ’users’ needs when communicating with the deaf using sign language.

Evaluating on the spatial localization cues of binaural sound pressure recorded via behind-the-ear microphones in a hearing aid

Spatial hearing ability enables people with normal hearing to localize a sound source and utilizes the cocktail party effect to detect the target speech information in a noise environment. Hearing-impairment causes not only hearing loss but also the degradation of spatial auditory ability. Hearing aid is desired to improve the spatial auditory ability of some hearing-impaired people, in addition to compensating hearing loss. However, the microphones for conventional behind-the-ear (BTE) hearing aid are located behind the pinnae rather than in the entrances of ear canal, which may cause distortion in spatial cues in the recorded binaural signals. The present work comprehensively evaluates the spatial hearing cues recorded by BTE-microphones. The head-related transfer functions (HRTF) for a KEMAR artificial head and eight human subjects, at three BTE positions and the entrances of blocked ear-canal, are measured respectively. Analysis from the HRTF data indicates that BTE microphone recording basically reserves the interaural time difference and interaural level difference as lateral-direction information. It also reserves the dynamic cues, but obvious distortions appear in the spectral cues at high frequencies. However, due to the redundancy among different spatial information, it can be inferred that dynamic cues alone may provide appropriate front-back and vertical-direction information.

자막 제공 서비스 앱의 UI/UX 개선 연구 - 청각 장애인을 위한 자막 자동 생성 앱 ‘이어줌’의 사용성 향상을 위한 제안

This study aims to improve users' video content accessibility, usability, and aesthetic satisfaction by analyzing the UI/UX of <Eeojum>, an app that provides an automatic subtitle creation service for the hearing impaired, in terms of functionality and usability. To this end, we conducted preliminary research on automatic subtitle generation apps, analyzed the environment and actual conditions of mobile use by hearing impaired people, and analyzed domestic and international app accessibility policies and public institution UI design guidelines. Based on this, we looked at the UI/UX characteristics of <Eeojum>, derived UI/UX improvement items to improve app accessibility quality and usability, and proposed improvements to the voice conversion app UI/UX design draft. The biggest goal was to simplify the structure of IA and strengthen the visual formability of the display to improve usability, presenting design guidelines aimed at improving usability for the hearing impaired, the main users.

Derived Amharic alphabet sign language recognition using machine learning methods

Hearing-impaired people use sign language as a means of communication with those with no hearing disability. It is therefore difficult to communicate with hearing impaired people without the expertise of a signer or knowledge of sign language. As a result, technologies that understands sign language are required to bridge the communication gap between those that have hearing impairments and those that dont. Ethiopian Amharic alphabets sign language (EAMASL) is different from other countries sign languages because Amharic Language is spoken in Ethiopia and has a number of complex alphabets. Presently in Ethiopia, just a few studies on AMASL have been conducted. Previous works, on the other hand, only worked on basic and a few derived Amharic alphabet signs. To solve this challenge, in this paper, we propose Machine Learning techniques such as Support Vector Machine (SVM) with Convolutional Neural Network (CNN), Histogram of Oriented Gradients (HOG), and their hybrid features to recognize the remaining derived Amharic alphabet signs. Because CNN is good for rotation and translation of signs, and HOG works well for low quality data under strong illumination variation and a small quantity of training data, the two have been combined for feature extraction. CNN (Softmax) was utilized as a classifier for normalized hybrid features in addition to SVM. SVM model using CNN, HOG, normalized, and non-normalized hybrid feature vectors achieved an accuracy of 89.02%, 95.42%, 97.40%, and 93.61% using 10-fold cross validation, respectively. With the normalized hybrid features, the other classifier, CNN (sofmax), produced a 93.55% accuracy.

CSLNSpeech: Solving the extended speech separation problem with the help of Chinese sign language

Previous audio-visual speech separation methods synchronize the speaker's facial movement and speech in the video to self-supervise the speech separation. In this paper, we propose a model to solve the speech separation problem assisted by both face and sign language, which we call the extended speech separation problem. We design a general deep learning network to learn the combination of three modalities, audio, face, and sign language information, to solve the speech separation problem better. We introduce a large-scale dataset named the Chinese Sign Language News Speech (CSLNSpeech) dataset to train the model, in which three modalities coexist: audio, face, and sign language. Experimental results show that the proposed model performs better and is more robust than the usual audio-visual system. In addition, the sign language modality can also be used alone to supervise speech separation tasks, and introducing sign language helps hearing-impaired people learn and communicate. Last, our model is a general speech separation framework and can achieve very competitive separation performance on two open-source audio-visual datasets. The code is available at https://github.com/iveveive/SLNSpeech

Lip reading using deep learning in Turkish language

<p>Computer vision is one of the most important areas of artificial intelligence and lip reading is one of the most important areas of computer vision. Lip-reading, which is more important in noisy environments or where there is no sound flow, is one of the working areas that can help the hearing-impaired people. There is no dataset in Turkish for lip reading, which there are different datasets at alphabet, word, and sentence level in different languages. The dataset of this study was created by the author and video data were collected from 72 people for 71 words. Audio streams were removed from the collected videos and a dataset was created using only images. Due to the small size of the dataset, the data was replicated with the Camtasia application. After the model of the research was designed and trained, the model was tested on adjectives, nouns, and verbs dataset and success rates of 71.8%, 71.88%, and 79.69% were obtained, respectively.</p>

Improved Arithmetic Optimization Algorithm with Transfer Learning based Arabic Sign Language Identification System

The Arabic sign language (ArSL) has witnessed ground-breaking research activities to identify hand gestures and signs through the deep learning (DL) model. SL is a unique communication tool that bridges the gaps between people with hearing impairment and ordinary people. The ArSL recognition system is of immense importance for different groups of people since it enables individuals with hearing impairment to communicate effectively. In SLs, signs are characterized by discrepancies in hand positions, shapes, motions, body parts, and facial expressions, posing a crucial threat to visual recognition in computer vision (CV). An automated sign detection technique needs two primary courses of action: the recognition of specific features and the classification of the input dataset. Previously, several approaches for detecting and classifying SLs had been proposed. In this study, an Improved Metaheuristics with Transfer Learning based Arabic Sign Language Identification System (IMTL-ArSL) method is developed. The primary objective of the IMTL-ArSL method is to detect and classify the existence of various signs in the Arabic language. In the IMTL-ArSL model, the bilateral filtering (BF) model is initially used for preprocessing. Besides, the Residual Network (ResNet50v2) model is applied for extracting features, and an improved arithmetic optimization algorithm (IAOA) model is utilized for hyperparameter tuning. Finally, a gated recurrent unit (GRU) network is exploited to identify sign languages. The empirical analysis of the IMTL-ArSL approach is tested using a benchmark dataset. The experimental values of the IMTL-ArSL approach portrayed a superior accuracy value of 93.87% over other techniques.

Proximal auditory AR rehabilitation: system integration and wellness applications-from hearing support up toward vestibular rehabilitation.

We present a new rehabilitation system based on novel principles, which consists of an auditory augmented reality (AR) headset we originated. The auditory AR headset, which does not cover both ears, allows users to hear both Real and Virtual environmental sounds at the same time. It can also be used in combination with Hearing Aids. We have studied a system to support hearing-impaired people and conducted a test evaluation. The system was able to provide convenience akin to "reading glasses for sound" to those who had mild hearing disabilities. Furthermore, by combining the system with surrounding speakers, a completely novel virtual auditory illusion was created in which the sound image jumps into the ear and runs away. We name this "proximal auditory AR (PAAR)" system. This system directly affects the unconscious level of reflexes for maintaining a standing position and can generate very subtle body motion disturbance. Using this system, we can modulate the standing posture and observe the autonomic nerve system's ability to subliminally compensate for the disturbance, using a stabilometer that measures body sways by center of pressure (COP). We observed a significant difference in the declination of COP only when using the PAAR, which is combined with array speakers and the auditory AR headphone, compared using a conventional closed-type and a bone-conduction headphone. By analyzing such big data of physical movement through machine learning, we expect to realize new systems for diagnosis, rehabilitation, function maintenance, and fall prevention.

Legal Protection of Persons with Hearing Disabilities Associated with the Obligation to Read Deeds (Verlijden) For Notaries in Making Notarial Deeds

The Security Office Act (UUJN) contains legal provisions regarding the authenticity of recorded documents that are read but not heard by persons with disabilities; especially Article 16, Section 1, paragraph m of Law No. 16. Law No. 2 of 2014 on the Notary Public (UUJN) Regulation. The research questions of this study are: (1) How are certified texts read but not heard by deaf people; In this study, research methods based on law and theory are used. The data collection process was carried out through literature research. The results of the research show that: (1) Reading the work by the author with the help of a translator is valid if the actual production still complies with the regulations determined by the notary law. (2) The notary must be legal and impromptu, must include information on the official translation promise that the notary knows how to translate, and the face document at the end of the residence document. Making the work done by the notary on behalf of the hearing impaired people realistic, thus obtaining the legal rights of the hearing impaired as a service to the users and binding the legal goods on both parties, especially third parties.

New haptic systems for elicit emotions in audio-visual events for hearing impaired people.

In this research we have developed devices for hearing impaired people that can transmit the emotions evoked by music. In this way, we will all achieve the full inclusion of impairment people in all social activities. As this is an issue that goes beyond pure engineering research, it has been necessary to create a multidisciplinary group within the Spanish Centre for Captioning and Audio Description (CESyA), in direct collaboration with the Display and Photonic Applications Research Group (GDAF) and the Human Language and Accessibility Technologies Group (HULAT) of the Carlos III University (UC3M) and the Psychiatry and Applied Communication Departments of the Complutense University of Madrid. For this research, a pair of haptic gloves were developed, which transmit different characteristics extracted from music by means of vibrational sequences, intended to drive an emotional reinforcement. Another device, a “music box”, a vibration box, was also developed from the idea of using vibration as an instrument.Overall, the acceptance of the glove was positive, with an 80% of users accepting the negative video, repeatedly stating in the surveys that they "felt more when the glove vibrated". The music box was also well received. Volunteers felt that the system gave them a pleasant sensation and that they could feel the music. However, some participants associated the vibration with the sensation of an alarm clock, which was expectedly negative.Considering the obtained EEG results, we can conclude that the integration of simple multimodal stimuli, visual and vibrotactile, helps to enhance the activation of emotional processes linked with musical stimulation. It can also be concluded that multimodal visual and vibrotactile stimulation enhances both attentional and emotional processes. These results pave the way for future developments that will allow a hearing-impaired person to fully enjoy an audiovisual event, which will have a great impact on this group.

Flexible Conformally Bioadhesive MXene Hydrogel Electronics for Machine Learning-Facilitated Human-Interactive Sensing.

Wearable epidermic electronics assembled from conductive hydrogels are attracting various research attention for their seamless integration with human body for conformally real-time health monitoring, clinical diagnostics and medical treatment, and human-interactive sensing. Nevertheless, it remains a tremendous challenge to simultaneously achieve conformally bioadhesive epidermic electronics with remarkable self-adhesiveness, reliable ultraviolet (UV) protection ability, and admirable sensing performance for high-fidelity epidermal electrophysiological signals monitoring, along with timely photothermal therapeutic performances after medical diagnostic sensing, as well as efficient antibacterial activity and reliable hemostatic effect for potential medical therapy. Herein, a conformally bioadhesive hydrogel-based epidermic sensor, featuring superior self-adhesiveness and excellent UV-protection performance, is developed by dexterously assembling conducting MXene nanosheets network with biological hydrogel polymer network for conformally stably attaching onto human skin for high-quality recording of various epidermal electrophysiological signals with high signal-to-noise ratios (SNR) and low interfacial impedance for intelligent medical diagnosis and smart human-machine interface. Moreover, a smart sign language gesture recognition platform based on collected electromyogram (EMG) signals is designed for hassle-free communication with hearing-impaired people with the help of advanced machine learning algorithms. Meanwhile, the bioadhesive MXene hydrogel possesses reliable antibacterial capability, excellent biocompatibility, and effective hemostasis properties for promising bacterial-infected wound bleeding.

A Canvas of Air and Signs: Integrating Voice Activated Hand Sign Recognition and Air Canvas for Hearing Impaired and Non-Verbal People

Moving hands in air and watching your screen writes for you and at the same time making different Signs with hands and the system displays it as well as speak what Sign you are making.This seems to be very futuristic approach towards the realm of Image Processing and Gesture Recognition . In this paper, we present a very interesting and a novel approach towards an interactive learning platform where one can draw the content on screen while moving their hand in air and can also use hand sign language to communicate with an ease with the Hearing Impaired and Dumb Community. Our system combines both technologies to create a smooth and engaging experience for users. It can be used in interactive art setups or virtual reality setups. Air canvas enables users to draw and manipulate digital content in mid-air with object tracking using Computer Vision and Mediapipe framework, while hand gesture recognition allows for real-time interpretation of Hand Signs to perform actions or commands within the system. This Model not only recognizes the sign but also speaks it loud using pyttsx3 a text-to-speech conversion Library, ensuring a good communication between a normal human and people with Non-Verbal and Hearing Impaired disability. To enhance the performance of the model We validate the model with a real dataset trained by us. This training was essential for refining the accuracy and efficiency of the model.

Investigating the ¬career maturity prediction model based on social adaptation, shame and ¬communication skills in hearing impaired and normal people

The purpose of the research Present "Review Model Forecast­​ puberty job According to Social adjustment, Feeling shame And skills­​ communicational At Persons With damage hearing And It was normal. Career maturity plays a very important role in choosing and staying in a job in people. This research, in terms of its purpose, was among applied ­research ­and in terms of its method, it was descriptive-correlation type. The statistical population of the study included all ­normal and ­hearing-impaired people ( ­hearing-impaired and deaf people who are members of the Isfahan Center for the Deaf, numbering 213 people). sample size using the ­available sampling method; For ­the hearing impaired and normal people (paired ­with the deaf group in terms of ­demographic characteristics ­), a total number of 262 people were estimated (131 people each for healthy people and deaf people). The research data collection tool includes 4 types of job maturity questionnaire ­by Savix and Porfli (2011); Bell's social adjustment (1961); Queendam's communication ­skills ­(2004) and guilt and shame tendency scale were from Cohen et al. (2011 ) ­. The results of the investigation using ­spss and Smart pls-3 statistical software showed that in both groups, the components ­of social adaptation and ­communication skills are able to predict ­the component ­of professional maturity ­. In the healthy group, shame was not able to predict ­professional maturity ­. Social adjustment can fully predict ­career maturity ­. Among the components ­of shame; Withdrawal is ­able to predict ­career maturity, but the negative self-evaluation component ­does not have the power to predict career maturity. Among ­communication skills, listening skills ­and communication process; has been able to predict ­career maturity; However, the skills ­of receiving messages, emotional control and communication combined with determination; has not been able to predict ­career maturity. In general, the components of communication ­skills ­, social adaptation and feeling of shame ­have the power to predict professional maturity ­. You should try to increase the amount of these variables by creating workshops ­and educational and consulting centers ­and finally, the growth of career maturity in people.

LERMO: A Novel Web Game for AI-Enhanced Sign Language Recognition

Sign language is a visual and gestural communication system used by deaf and hearing-impaired people. Despite numerous deep learning methods proposed for automatic interpretation, a gap persists in developing applications that effectively utilize these models for assisting sign language studies and inclusion. We introduce LERMO (https://lermo.app/), a web game merging machine learning and gamification to enhance sign language fingerspelling. Inspired by Wordle™, LERMO offers an interactive word-guessing game where users can play using a video camera. We create a new dataset of labeled landmark fingerspelling and design our model to ensure optimal speed and efficiency to run on a web browser. We survey approximately 40 users, which find LERMO user-friendly and innovative. From those, 95% believe LERMO could be used to enhance fingerspelling skills.

An Expert System for Indian Sign Language Recognition Using Spatial Attention–based Feature and Temporal Feature

Sign Language (SL) is the only means of communication for the hearing-impaired people. Normal people have difficulty understanding SL, resulting in a communication barrier between hearing impaired people and hearing community. However, the Sign Language Recognition System (SLRS) has helped to bridge the communication gap. Many SLRs are proposed for recognizing SL; however, a limited number of works are reported for Indian Sign Language (ISL). Most of the existing SLRS focus on global features other than the Region of Interest (ROI). Focusing more on the hand region and extracting local features from the ROI improves system accuracy. The attention mechanism is a widely used technique for emphasizing the ROI. However, only a few SLRS used the attention method. They employed the Convolution Block Attention Module and temporal attention but Spatial Attention (SA) is not utilized in previous SLRS. Therefore, a novel SA based SLRS named Spatial Attention-based Sign Language Recognition Module (SASLRM) is proposed to recognize ISL words for emergency situations. SASLRM recognizes ISL words by combining convolution features from a pretrained VGG-19 model and attention features from a SA module. The proposed model accomplished an average accuracy of 95.627% on the ISL dataset. The proposed SASLRM is further validated on LSA64, WLASL, and Cambridge Hand Gesture Recognition datasets where, the proposed model reached an accuracy of 97.84%, 98.86%, and 98.22%, respectively. The results indicate the effectiveness of the proposed SLRS in comparison with the existing SLRS.

DEVELOPMENT OF FLEXIBILITY IN 9-10 YEARS-OLD HARD-HEARING SCHOOLCHILDREN ENGAGED IN AIR STRETCHING

The article discusses the eff ectiveness of using a set of exercises, including stretching in hammocks or aerostretching, as an innovative way to correct fl exibility in hearing-impaired schoolchildren aged 9–10 years. Relevance. According to the World Health Organization for 2022, there are about 10% of hearing-impaired people in Russia, which is 14.6 mil-lion people, of which 220 thousand people are hearing-impaired. According to forecasts, by 2050 the number of people with hearing impairments will reach 900 million. The lack of auditory information worsens the perception of space, af-fects the violation of regulation and coordination of movements, reduces the indicators of general physical development, activity and dexterity. According to N. A. Bernstein, the most important task of education is physical improvement. The decrease in the child’s motor capabilities, confi dence in the successful execution of movements, is primarily associated with the development of fl exibility. Moreover, fl exibility is a favorable prerequisite for improving other psychological and speed-strength qualities. Evseev S. P., summarizing the available research, noted that in hard-of-hearing children, compared with their peers, fl exibility indicators are lower, which justifi es the goal: to develop a set of exercises aimed at developing fl exibility in hard-of-hearing schoolchildren 9–10 years old. Research methods: In the process of research, methods of scientifi c literature analysis, logical modeling, pedagogical experiment and methods of mathematical statis-tics. Conclusion. The data obtained as a result of the study prove the eff ectiveness of the use of aero stretching for the development of fl exibility in hearing-impaired schoolchildren. The introduction of a set of exercises in extracurricular stretching classes in hammocks for hearing-impaired children, allows to increase fl exibility, improve mobility of the shoulder, hip, ankle joints and spine.

Investigation of sports participation motivation in people with disabilities: a cross-sectional study of individuals with physical and hearing impairments

While reading the literature, it is seen that there are not enough studies on the motivation of disabled individuals to participate in sports. This study aims to examine the sports participation motivations of hearing impaired and physically disabled athletes. This study was a cross-sectional study. The research group of the study consists of physically and hearing-impaired individuals between the ages of 18–47. The participants of this research group consisted of 253 volunteer disabled individuals, 150 of whom were men and 103 of whom were women. Sports participation motivation scale was used for disabled individuals. The scale consists of 3 dimensions and is a 5-point Likert type. The results of the study showed that hearing-impaired people have a higher high school rate and physically person with disability have a higher bachelor’s degree rate, but the primary education rate did not change between hearing and physically person with disability. Physical activity participation differed between hearing and physically person with disability, and it was observed that hearing-impaired people participated in more physical activities. The level of well-being of the physically disabled was significantly better than the hearing impaired. As conclusion, it is observed that the people with the lowest motivation to participate in sports are primary school graduates and those with high welfare have a high motivation to participate in sports.

The Serbian Sign Language Alphabet: A Unique Authentic Dataset of Letter Sign Gestures

Language barriers and the communication difficulties of individuals with developmental disabilities are two major causes of communication problems that societies worldwide encounter. A particularly challenging group is hearing-impaired people who have difficulties with communication, reading, writing, learning, and social interactions, which have a substantial impact on their quality of life. This article focuses on detailing a Serbian Sign Language alphabet database and the method for creating it in order to provide a foundation for answering the various societal challenges of persons who use the Serbian language. In front of a computer camera, 41 people performed Serbian Sign Language sign movements that replicated the Serbian alphabet for this study’s aims. Hand and body key points were identified using the recorded video clips, and the numerical values of the identified key points were then stored in a database for further processing. In total, 8.346 video clips of people making recognized hand gestures were gathered, processed, classed, and archived. This paper provides a thorough technique that may be applied to comparable tasks and details the process of constructing a dataset based on Serbian Sign Language alphabet signs. This dataset was created using custom-made Python 3.11 software. Data regarding dynamic video clips that capture entire subject movement were incorporated into this dataset to fill in the gaps in other similar efforts based on static photographs. Thus, the purpose of this investigation is to employ innovative technology to support the community of hearing-impaired people in areas such as general inclusion, education, communication, and empowerment.

The Impact of Hearing Aids on Listening Effort and Listening-Related Fatigue - Investigations in a Virtual Realistic Listening Environment.

Participation in complex listening situations such as group conversations in noisy environments sets high demands on the auditory system and on cognitive processing. Reports of hearing-impaired people indicate that strenuous listening situations occurring throughout the day lead to feelings of fatigue at the end of the day. The aim of the present study was to develop a suitable test sequence to evoke and measure listening effort (LE) and listening-related fatigue (LRF), and, to evaluate the influence of hearing aid use on both dimensions in mild to moderately hearing-impaired participants. The chosen approach aims to reconstruct a representative acoustic day (Time Compressed Acoustic Day [TCAD]) by means of an eight-part hearing-test sequence with a total duration of approximately 2½ h. For this purpose, the hearing test sequence combined four different listening tasks with five different acoustic scenarios and was presented to the 20 test subjects using virtual acoustics in an open field measurement in aided and unaided conditions. Besides subjective ratings of LE and LRF, behavioral measures (response accuracy, reaction times), and an attention test (d2-R) were performed prior to and after the TCAD. Furthermore, stress hormones were evaluated by taking salivary samples. Subjective ratings of LRF increased throughout the test sequence. This effect was observed to be higher when testing unaided. In three of the eight listening tests, the aided condition led to significantly faster reaction times/response accuracies than in the unaided condition. In the d2-R test, an interaction in processing speed between time (pre- vs. post-TCAD) and provision (unaided vs. aided) was found suggesting an influence of hearing aid provision on LRF. A comparison of the averaged subjective ratings at the beginning and end of the TCAD shows a significant increase in LRF for both conditions. At the end of the TCAD, subjective fatigue was significantly lower when wearing hearing aids. The analysis of stress hormones did not reveal significant effects.