Assistive Communication Research Articles

Assistive Communication Device Used During Pediatric Noninvasive Ventilation.

Noninvasive ventilation therapy is a common approach in acute respiratory failure as an alternative to invasive mechanical ventilation. Assistive communication strategies are necessary to overcome communication impairments and anxiety caused by oronasal obstruction in pediatric intensive care unit patients undergoing noninvasive ventilation. A 7-year-old girl was admitted with a history of pulmonary arterial hypertension and recurrent respiratory failure requiring bilevel positive airway pressure intervention. The patient experienced initial lack of bilevel positive airway pressure mask compliance due to oronasal mask discomfort. The trial of a novel communication device (SPEAX, Ataia Medical) involving a microphone that attaches to the patient's bilevel positive airway pressure mask for enhanced communication. Use of the assistive communication device was associated with increased intelligibility to familiar listeners, mask compliance, enhanced mobility, and reduced patient anxiety levels throughout hospitalization and in later episodes of readmission. The implementation of assistive communication devices can alleviate communication barriers in pediatric bilevel positive airway pressure users, contributing to increased compliance and diminished anxiety for patients and caregivers. Providing a pathway for effective communication may reduce sedation use, minimizing risks of sedation-related delirium and improving overall quality of life in these patients.

Revolutionizing Communication for Children with Autism Spectrum Disorder through Generative AI

This article explores the potential of Generative Artificial Intelligence (GenAI) in supporting communication for children with Autism Spectrum Disorder (ASD). With the prevalence of ASD increasing, there is an urgent need for innovative interventions. The article discusses how GenAI can offer personalized, adaptive communication tools that interpret social cues, simplify language, and provide real-time support. It examines the key benefits of GenAI in ASD support, including personalized communication assistance, enhanced learning experiences, and data-driven intervention planning. The article also delves into GenAI-enabled conversation tools, explaining their mechanisms and potential impact. Finally, it addresses the ethical considerations and challenges associated with implementing these technologies, emphasizing the need for a multidisciplinary approach in their development and deployment.

Brain-computer interface applications in the aging population with Alzheimers and Parkinsons disease

Abstract. The rapidly aging population faces increasing prevalence of neurodegenerative conditions like Alzheimer's Disease (AD) and Parkinson's Disease (PD), which significantly impair cognitive and motor functions. There is an urgent need for increased research, awareness, and development of effective treatments to mitigate their impact on the affected individuals and their families. This paper delves into the history, mechanisms, and applications of brain-computer interfaces (BCIs) as innovative solutions to mitigate these impacts. BCIs enable direct brain-to-device communication, circumventing conventional neuromuscular routes. We highlight how neurofeedback and neuroplasticity facilitated by BCIs not only restore motor skills and cognitive functions but also enhance the overall quality of life for sufferers. Through applications in cognitive training, motor assistance, and advanced communication aids, BCIs present a significant promise despite the ethical and practical challenges they pose. The continual advancement of this technology promises to a future when its incorporation into treatment plans will be widely used and provide significant advantages for those with PD and AD.

Machine learning for predictive AAC: Improving speech and gesture-based communication systems

Augmentative and alternative communication (AAC) systems play a crucial role in supporting individuals with severe communication disabilities by providing accessible means of expression and engagement. However, many conventional AAC devices rely on manual input or basic predictive functions, which can limit communication efficiency and responsiveness. The application of machine learning (ML) to AAC offers new opportunities to enhance these systems, enabling them to provide faster, more accurate, and contextually relevant communication assistance. Advances in ML, particularly in predictive text, speech recognition, and gesture interpretation, allow AAC systems to adapt more intuitively to user needs, predicting intent based on usage patterns and multimodal data, such as voice and gestures. Current research highlights the potential of ML to address key gaps in AAC technology by creating more responsive, personalized systems that align with individual user behaviours. This study proposes a novel ML framework designed to integrate these capabilities, promising improvements in communication speed, user autonomy, and accuracy. By addressing the challenges and limitations of traditional AAC devices, this research aims to advance accessible communication solutions that empower users and improve quality of life.

GAO–FCNN–Enabled Beamforming of the RIS–Assisted Intelligent Communication System

The joint beamforming optimization from the perspective of the bit error rate (BER) in a reconfigurable intelligent surface (RIS)–assisted intelligent communication system is studied in this paper. A genetic algorithm (GA) is investigated to address the bottleneck of the system performance based on the dynamic adaptability theory. However, the bottleneck is caused by the interaction between the active and passive beamforming. To tackle the constraints of conventional optimization approaches, the hybrid scheme is proposed to combine the GA optimization (GAO) and fully connected neural network (FCNN) strategy. Specifically, the intelligent collaborative tuning of system parameters is achieved using this proposed technique. Simulation findings indicate that the hybrid scheme not only simplifies the calculation process to obtain the optimal network parameters, but also effectively optimizes the system structure by dynamically adjusting the RIS reflection configuration. Based on this, the signal transmission quality is improved, interference is reduced, and the stable and efficient operation of the RIS–assisted intelligent communication system is ensured in the complex wireless transmission scenario.

Resource optimization of multi-UAV assisted communication system based on user scheduling

In order to improve the transmission rate of the multi-user mobile communication downlink wireless transmission system, a resource optimization method for multi-unmanned aerial vehicle (UAV) assisted communication system based on user scheduling and trajectory optimization is proposed. The proposed method establishes an optimization problem with the maximization of the total multi-user throughput as the criterion under the constraints of user scheduling, total energy consumption of UAVs and user service quality requirements. In order to solve this non-convex problem, the original non-convex problem is decomposed into three easy-to-handle non-convex sub-problems by using the block coordinate descent method (BCD). The sub-problems are transformed and solved by introducing slack variables, first-order Taylor expressions, continuous convex approximation (SCA) and other methods, and then alternately iteratively optimized to obtain an approximate suboptimal solution to the original non-convex problem. Simulation results show that the proposed algorithm can effectively improve the total system throughput and has good convergence in both single and multi-UAV communication systems.

Multi-Agent DRL for Air-to-Ground Communication Planning in UAV-Enabled IoT Networks.

In this paper, we present a novel method to enhance the sum-rate effectiveness in full-duplex unmanned aerial vehicle (UAV)-assisted communication networks. Existing approaches often couple uplink and downlink associations, resulting in suboptimal performance, particularly in dynamic environments where user demands and network conditions are unpredictable. To overcome these limitations, we propose a decoupling of uplink and downlink associations for ground-based users (GBUs), significantly improving network efficiency. We formulate a comprehensive optimization problem that integrates UAV trajectory design and user association, aiming to maximize the overall sum-rate efficiency of the network. Due to the problem's non-convexity, we reformulate it as a Partially Observable Markov Decision Process (POMDP), enabling UAVs to make real-time decisions based on local observations without requiring complete global information. Our framework employs multi-agent deep reinforcement learning (MADRL), specifically the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, which balances centralized training with distributed execution. This allows UAVs to efficiently learn optimal user associations and trajectory controls while dynamically adapting to local conditions. The proposed solution is particularly suited for critical applications such as disaster response and search and rescue missions, highlighting the practical significance of utilizing UAVs for rapid network deployment in emergencies. By addressing the limitations of existing centralized and distributed solutions, our hybrid model combines the benefits of centralized training with the adaptability of distributed inference, ensuring optimal UAV operations in real-time scenarios.

Transfusion burden and willingness to pay for temporary alleviation of anemia status in transfusion-dependent beta-thalassemia patients in China

BackgroundTransfusion-dependent β-thalassemia (TDT) is one of the global public health concerns highlighted by the World Health Organization. Patients with TDT require regular blood transfusion to survive. However, the availability of blood resources is extremely limited. The purpose of this study was to investigate transfusion burden and willingness to pay (WTP) for temporary remission of anemia status among patients with TDT and to explore the associated factors.MethodsAdult patients with TDT were recruited through cluster sampling across several high-incidence provinces in China. Consenting patients completed online questionnaires on demographic information, transfusion burden and WTP with real-time WeChat communication assistance from researchers. The guiding techniques of double-bounded dichotomous choices and open-ended questions in the contingent valuation method (CVM) were used to obtain participants’ WTP for 1 unit of leukocyte-depleted red blood cells. WTP calculations were performed using maximum likelihood estimation, with further insights gained through subgroup analysis based on gender, family monthly income level and convenience of blood transfusion.ResultsThe analysis included 149 TDT patients from five high-incidence provinces, with an average monthly income of $198.5. Patients received an average of 3.7 units per transfusion, 15.4 times annually, with an average WTP of $70.4 per unit (95% CI [62.0, 78.9]). Estimated WTP for temporary anemia alleviation per transfusion totaled $260.6, exceeding monthly income by 1.32 times. Higher WTP was observed among males, higher-income households, and those with at least junior education. Lower WTP was noted among patients with lower transfusion volumes and those needing to travel for transfusion or during hospitalization for blood transfusion.ConclusionHigh WTP indicated a strong desire for temporary anemia relief. Most TDT patients faced significant economic and transfusion burden. The evident gap in meeting clinical needed underscores the urgent demand for innovative treatments to reduce transfusion dependency, potentially transforming TDT care and improving socioeconomic well-being and clinical outcomes. These findings supported evidence-based decision-making for TDT pharmacoeconomics and efficient healthcare resource allocation in China.

An ultra lightweight neural network for automatic modulation classification in drone communications

Unmanned aerial vehicle (UAV)-assisted communication based on automatic modulation classification (AMC) technology is considered an effective solution to improve the transmission efficiency of wireless communication systems, as it can adaptively select the most suitable modulation method according to the current communication environment. However, many existing deep learning (DL)-based AMC methods cannot be directly applied to UAV platform with limited computing power and storage space, because of the contradiction between accuracy and efficiency. This paper mainly studies the lightweight of DL-based AMC networks to improve adaptability in resource-constrained scenarios. To address this challenge, we propose an ultra-lightweight neural network (ULNN). This network incorporates a lightweight convolutional structure, attention mechanism, and cross-channel feature fusion technique. Additionally, we introduce data augmentation (DA) based on signal phase offsets during the model training process, aimed at improving the model’s generalization ability and preventing overfitting. Through experimental validation on the public dataset RML2016.10 A, the ULNN we proposed achieves an average precision of 62.83% with only 8815 parameters and reaches a peak classification accuracy of 92.11% at SNR = 10 dB. The experimental results show that ULNN can achieve high recognition accuracy while keeping the model lightweight, and is suitable for UAV platform with limited resources.

Jamming assisted covert wireless communication in multi-channel systems

Jamming assisted covert wireless communication in multi-channel systems

Penghulu's Assistance in Providing Premarital Guidance to Realize a Sakinah Mawaddah Warahmah (Samara) Family at KUA Tanjung Morawa

This assistance aims to analyze Penghulu's Islamic Communication Assistance Strategy in Providing Premarital Guidance to Create a Sakinah Mawaddah Warahmah (Samara) Family in Kua Tanjung Morawa. The approach uses PAR (Participatory Action Research). The Islamic communication assistance strategy implemented by the headmen at KUA Tanjung Morawa in providing pre-marital guidance plays a very important role in creating a Sakinah Mawaddah Warahmah (SAMARA) family. The approach used includes a deep understanding of Islamic values, effective communication techniques, as well as empathy and emotional support for prospective bride and groom couples. Through structured guidance and based on Islamic principles, the bride and groom are able to help prospective bride and groom couples understand the importance of commitment, mutual understanding and cooperation in building a harmonious family, so that the goal of achieving a sakinah, mawaddah and warahmah family can be realized more fully real.

Using structural equation modeling to investigate students’ satisfaction with an undergraduate tutorial system

BackgroundThe undergraduate tutorial system (UTS) is a crucial measure in China for adhering to the principle of prioritizing foundational education, innovating the undergraduate talent training mode, and building a powerful country of higher education. This study investigated undergraduate students’ satisfaction with UTS and the influencing factors, aiming to promote the healthy and sustainable development of UTS and provide practical implications and suggestions for universities.MethodsBased on relevant theories, we conducted a survey study and leveraged structural equation modeling to assess students’ satisfaction with UTS and explore the influencing factors.ResultsOur Pearson correlation analysis showed that students’ satisfaction with mentors was positively correlated with dimensions such as humanistic care (r = 0.844, P < 0.05), mentor assistance (r = 0.906, P < 0.05), and mentor-student communication (r = 0.908, P < 0.05). Path analysis showed that mentor-student communication (β = 0.486, P < 0.01), mentor assistance (β = 0.228, P < 0.05), humanistic care (β = 0.105, P < 0.05) were positive factors affecting students’ satisfaction with mentors, while satisfaction with mentors (β = 0.923, P < 0.01) had a positive impact on students’ satisfaction with UTS. Students’ satisfaction with mentors explained 73.4% of the variation in students’ satisfaction with UTS, indicating that satisfaction with mentors was an important intermediary variable of UTS students.ConclusionThe sustainable implementation of UTS requires the effort to improve student satisfaction, and the breakthrough of strengthening the targeted mentorship in “transmitting wisdom, imparting knowledge, and resolving doubts” for students. Efforts should also be devoted to fostering students’ comprehensive skills and better serving the cultivation of talents in the new era.

Synergistic Integration of Self-Powered, Flexible Wearables with Advanced Gesture Recognition for Deaf-Mute Communication

This research confronts two pivotal challenges in advancing assistive communication devices for the deaf and mute community: enhancing the longevity of device operation through self-powered sensor technology and improving gesture recognition accuracy with sophisticated algorithms. The study introduces a novel design for flexible, wearable devices that harness kinetic and thermal energy, converting them into electrical energy to ensure continuous operation. This approach eliminates the need for frequent recharging, thereby increasing the autonomy of these assistive devices. In addressing the second challenge, the research refines deep learning algorithms to significantly boost the precision and accuracy of gesture recognition. These algorithms are trained on an extensive dataset that includes a broad spectrum of sign language gestures, captured by sensors on the wearable device. This methodology not only promises a breakthrough in the self-sufficiency of wearable assistive devices but also improves the effectiveness and usability of sign language to speech translation. Such advancements facilitate more seamless, barrier-free communication for individuals with hearing and speech impairments. Preliminary results demonstrate the potential of integrating self-powering capabilities with advanced gesture recognition technology, marking a considerable advancement in assistive technology.

Disability Expertise and Large Language Models: A Qualitative Study of Autistic TikTok Creators’ Use of ChatGPT

The purpose of this study was to describe how autistic TikTok creators are using ChatGPT across various domains of their lives, their motivations for doing so, and resulting impacts. Using a framework of “disability expertise,” we document the knowledge that creators acquired through use of ChatGPT and then shared with peers via social media. We used deductive qualitative methods to analyze 25 TikTok videos from 25 unique creators. Themes were identified in connection with motivations for the use of ChatGPT, settings in which it was used, applications of this technology, and resulting impacts for creators. Findings indicate that autistic creators were motivated to use ChatGPT to navigate neurotypical environments, manage features of their neurodivergence, and unmask, with the technology often serving as a digital coach, communication assistant, and conversational partner. Use of ChatGPT resulted in harm reduction, time and energy savings, positive emotional experiences, and meaningful accomplishments in both personal and professional settings. These findings indicate that ChatGPT serves as an important resource for many autistic individuals, facilitating accommodations to often inaccessible environments and helping users manage stressors and pursue goals. The study also highlights the significance of social media platforms for disseminating disability expertise related to the use of large language models to improve quality of life.

Secure energy efficiency maximization for joint ITS and IRS assisted satellite downlink communications

AbstractSatellite communications (SatCom) have been viewed as a promising technique to achieve ubiquitous global coverage in next‐generation wireless systems. This article investigates the secure energy efficient downlink transmission for SatCom where an intelligent transmissive surface‐aided transmitter is deployed at the satellite to perform energy efficient beamforming and an intelligent reflecting surface as well as a cooperative jammer are deployed on the ground to enhance the secure performance. The aim is to maximize the secure energy efficiency by jointly designing ITS beamforming, IRS phase shift, jammer precoding vector, and transmit power allocation. To develop a low‐complexity solution,first, an approximated concave lower bound of the non‐concave objective function is derived by utilizing Dinkelbach's method and a novel successive convex approximation technique. Then the reformulated problem is decoupled using alternating optimization algorithm and the subproblems are solved by adopting Riemannian manifold optimization, element‐wise block coordinate descent, Lagrange dual method and Karush–Kuhn–Tucker conditions. At last, simulation results demonstrate the effectiveness of the proposed scheme.

Unmanned Aerial Vehicle-Assisted Terahertz-Visible Light Communication Systems: An In-Depth Performance Analysis.

This paper investigates the performance of dual-hop unmanned aerial vehicle (UAV)-assisted communication channels, employing a decode-and-forward (DF) relay architecture. The system leverages terahertz (THz) communication for the primary hop and visible light communication (VLC) for the secondary hop. We conduct an in-depth analysis by deriving closed-form expressions for the end-to-end (E2E) bit error rate (BER). Additionally, we use a Monte Carlo simulation approach to generate best-fitting curves, validating our analytical expressions. A performance evaluation through BER and outage probability metrics demonstrates the effectiveness of the proposed system. Specifically, our results indicate that the proposed system outperforms Free-Space Optics (FSO)-VLC and Radio-Frequency (RF)-VLC at a higher signal-to-noise ratio (SNR). The results of this study provide valuable insights into the feasibility and limitations of UAV-assisted THz-VLC communication systems.

Unmanned aerial vehicle assisted communication: applications, challenges, and future outlook

Unmanned aerial vehicle assisted communication: applications, challenges, and future outlook

INVESTIGATING STUDENT'S LEVEL OF SELF-EFFICACY IN SPEAKING PERFORMANCE OF THAI EFL LEARNERS

The psychological notion of self-efficacy plays a key role in evaluating students' linguistic aptitude and fluency in English. The purpose of this study is to ascertain Thai students learning English as a foreign language (EFL)'s degree of self-efficacy in oral communication. In order to assess students' confidence in their ability to speak English fluently, the study looks into their sense of self-efficacy. Both quantitative and qualitative methods are used in this investigation. A questionnaire was distributed to 28 students who attended an Islamic high school in Satun, Southern Thailand, using a quantitative methodology. Several kids from the school are interviewed as part of the qualitative technique. Participants are asked particular questions that allow them to give in-depth answers based on their experiences and circumstances. Using the three elements of self-efficacy—magnitude, generality, and strength—the research findings show that students in Thailand consistently have low levels of self-efficacy. As a result, the study's findings imply that Thai students still require assistance in oral English communication. There are numerous uses for this subject in psychology, education, and language instruction. Instructors could come up with strategies to help Thai students feel more confident about learning English as a second language and improve their oral communication abilities. Techniques that enhance English communication skills can be incorporated into language programs and curriculum designs. The results can be used by psychologists to address issues with self-efficacy in language learning. This study takes a fresh approach by examining Thai English as a Foreign Language (EFL) learners' self-efficacy, particularly with regard to speaking abilities. It advances existing knowledge by highlighting the specific challenges these students face when speaking. This research provides novel insights that can guide interventions aimed at enhancing students' self-efficacy in oral English communication.

Assistive Communication Devices in Rett Syndrome: A Case Report and Narrative Review

Assistive Communication Devices in Rett Syndrome: A Case Report and Narrative Review

How do GenZ speakers use and process emoji in chatbot conversations: An eye-tracking study

This study investigates use and processing of emoji in chatbot conversations. 32 GenZ participants engaged in semi-naturalistic chats in an eye-tracker for ten minutes with a ChatGPT bot that used specific emoji from two personas (GenZ and Millennial). Eye-tracking and sentiment analysis revealed that the GenZ bot was typically positively perceived, while the Millennial bot showed one of two patterns: adaptation or hyperfocus on the ‘wrong’ emoji. The study sheds light on how emoji are used compared to words and their impact on AI assistant communication styles. The findings offer insights for studying open-ended conversations and linguistic patterns using ChatGPT.