Assistive Applications Research Articles

Validation of the screening test for at-risk drinking in an emergency department using a tablet computer

BackgroundThe Alcohol Use Disorder Identification Test (AUDIT) is widely used and validated in primary care settings for alcohol screening, yet practical challenges in conducting it in crowded clinics exist. Recently, a new abbreviated version of the AUDIT, was presented: the Screening Test for At-risk Drinking (STAD). This study aimed to evaluate the performance of STAD compared to other abbreviated versions of AUDIT for patients visiting the emergency department (ED). MethodsThis cross-sectional survey was conducted with 543 patients in the urban tertiary academic hospital ED in South Korea We diagnosed at-risk drinking using the entire AUDIT score. The optimal cut-off values, sensitivity, specificity, and the area under the receiver operating characteristics (AUROC) of the STAD were analyzed. We compared the AUROC with AUDIT-C and AUDIT-QF, which are previously abbreviated versions of AUDIT. ResultsFor males, the optimal cut-off value in the STAD test was 3 points with 83.1% sensitivity (95% CI: 75.3–89.2) and 95.9% specificity (95% CI: 91.2–98.5). For females, the optimal cut-off value was 2 points with 95.9% sensitivity (95% CI: 88.5–99.1) and 89.1% specificity (95% CI: 83.9–93.0). The AUROC curves for STAD were 0.964 (95% CI: 0.934–0.983) for males and 0.980 (95% CI: 0.965–0.993) for females. ConclusionsThe STAD is a simple and useful test to screen at-risk drinking in the ED, and its assisted applications will enable faster and more efficient screening and management of at-risk drinking.

A Wearable Soft Fabric Sleeve for Upper Limb Augmentation.

Soft actuators (SAs) have been used in many compliant robotic structure and wearable devices, due to their safe interaction with the wearers. Despite advances, the capability of current SAs is limited by scalability, high hysteresis, and slow responses. In this paper, a new class of soft, scalable, and high-aspect ratio fiber-reinforced hydraulic SAs is introduced. The new SA uses a simple fabrication process of insertion where a hollow elastic rubber tube is directly inserted into a constrained hollow coil, eliminating the need for the manual wrapping of an inextensible fiber around a long elastic structure. To provide high adaptation to the user skin for wearable applications, the new SAs are integrated into flexible fabrics to form a wearable fabric sleeve. To monitor the SA elongation, a soft liquid metal-based fabric piezoresistive sensor is also developed. To capture the nonlinear hysteresis of the SA, a novel asymmetric hysteresis model which only requires five model parameters in its structure is developed and experimentally validated. The new SAs-driven wearable robotic sleeve is scalable, highly flexible, and lightweight. It can also produce a large amount of force of around 23 N per muscle at around 30% elongation, to provide useful assistance to the human upper limbs. Experimental results show that the soft fabric sleeve can augment a user’s performance when working against a load, evidenced by a significant reduction on the muscular effort, as monitored by electromyogram (EMG) signals. The performance of the developed SAs, soft fabric sleeve, soft liquid metal fabric sensor, and nonlinear hysteresis model reveal that they can effectively modulate the level of assistance for the wearer. The new technologies obtained from this work can be potentially implemented in emerging assistive applications, such as rehabilitation, defense, and industry.

Block chain based Malware Detection using Machine Learning Algorithms for IoT enabled E-Health Applications

World is pacing in ultra-high speed with huge population along with drastic changes in the technology day-byday. This results in new era of applications, tools, and devices to make human life simpler. But the advancements in technology and human life leads to number of problems like lack of security, privacy, health and many more. In recent time the most dangerous problem that has been haunting the whole human race in various continents is COVID-19 virus. There are number of assistive technologies that has been created and deployed around the world to cope up and sustain in this pandemic situation. Now we are going to discuss one of the mobile assistive applications which helps to reduce the spreading of this contiguous virus in our country which is named as AAROGYASETU by Indian government. This is a pan India application to monitor, prevent as well as analyse the spreading of covid virus around the country. The assignment follows with the pros and cons of the application along with accessibility audit of the mobile app and finally proposed solution for the problems identified by the analysis.

Problematic Privacy Policies of Voice Assistant Applications

This work conducts a comprehensive empirical analysis on the privacy policies of 64,720 Amazon Alexa skills and 16,002 Google Assistant actions. We show that a substantial number of problematic privacy policies exist in the Amazon Alexa and Google Assistant platforms.

Maritime navigational assistance by visual augmentation

AbstractSeveral types of equipment have been developed over the years to assist ship operators with their tasks. Nowadays, navigational equipment typically provides an enormous volume of information. Thus, there is a corresponding need for efficiency in how such information is presented to ship operators. Augmented reality (AR) systems are being investigated for such efficient presentation of typical navigational information. The present work is particularly interested in an AR architecture commonly referred as monitor augmented reality (MAR).In this context, the development of MAR systems is briefly summarised. The projection of three-dimensional elements into a camera scene is presented. Potential visual assets are proposed and exemplified with videos from a ship manoeuvring simulator and a real experiment. Enhanced scenes combining pertinent virtual elements are shown exemplifying potential assistance applications. The authors mean to contribute to the popularisation of MAR systems in maritime environments. Further research is suggested to define optimal combinations of visual elements for alternative maritime navigation scenarios. Note that there are still many challenges for the deployment of MAR tools in typical maritime operations.

A Hybrid Speech Enhancement Algorithm for Voice Assistance Application.

In recent years, speech recognition technology has become a more common notion. Speech quality and intelligibility are critical for the convenience and accuracy of information transmission in speech recognition. The speech processing systems used to converse or store speech are usually designed for an environment without any background noise. However, in a real-world atmosphere, background intervention in the form of background noise and channel noise drastically reduces the performance of speech recognition systems, resulting in imprecise information transfer and exhausting the listener. When communication systems’ input or output signals are affected by noise, speech enhancement techniques try to improve their performance. To ensure the correctness of the text produced from speech, it is necessary to reduce the external noises involved in the speech audio. Reducing the external noise in audio is difficult as the speech can be of single, continuous or spontaneous words. In automatic speech recognition, there are various typical speech enhancement algorithms available that have gained considerable attention. However, these enhancement algorithms work well in simple and continuous audio signals only. Thus, in this study, a hybridized speech recognition algorithm to enhance the speech recognition accuracy is proposed. Non-linear spectral subtraction, a well-known speech enhancement algorithm, is optimized with the Hidden Markov Model and tested with 6660 medical speech transcription audio files and 1440 Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) audio files. The performance of the proposed model is compared with those of various typical speech enhancement algorithms, such as iterative signal enhancement algorithm, subspace-based speech enhancement, and non-linear spectral subtraction. The proposed cascaded hybrid algorithm was found to achieve a minimum word error rate of 9.5% and 7.6% for medical speech and RAVDESS speech, respectively. The cascading of the speech enhancement and speech-to-text conversion architectures results in higher accuracy for enhanced speech recognition. The evaluation results confirm the incorporation of the proposed method with real-time automatic speech recognition medical applications where the complexity of terms involved is high.

Probing heteroatoms co-doped graphene quantum dots for energy transfer and 2-photon assisted applications

Carbon based quantum dots find various applications such as imaging, diagnosis and therapeutics due to low toxicity, antibacterial activity, excellent water solubility, and good biocompatibility. However, the UV-blue emission of carbon based quantum dots is unsuited for applications in biomedicine and imaging as the short wavelength lights harm the living cells due to phototoxicity. In the present work, we have synthesised the long wavelength emitting heteroatoms (nitrogen (N) and sulfur (S)) co-doped graphene quantum dots (NS-GQD) via hydrothermal route. These NS-GQD have excitation-wavelength independent photoluminescence with high quantum yield in protonated and neutral forms. The non-covalent interaction of these NS-GQD when studied with a dye rhodamine B (RB) shows evidence of Förster resonance energy transfer (FRET). It has been shown here that the NS-GQD-RB composite exhibits prominent two-photon absorption at near infrared wavelength with a capability of deep-tissue imaging upto 175 µm.

Sharing Practices for Datasets Related to Accessibility and Aging.

Datasets sourced from people with disabilities and older adults play an important role in innovation, benchmarking, and mitigating bias for both assistive and inclusive AI-infused applications. However, they are scarce. We conduct a systematic review of 137 accessibility datasets manually located across different disciplines over the last 35 years. Our analysis highlights how researchers navigate tensions between benefits and risks in data collection and sharing. We uncover patterns in data collection purpose, terminology, sample size, data types, and data sharing practices across communities of focus. We conclude by critically reflecting on challenges and opportunities related to locating and sharing accessibility datasets calling for technical, legal, and institutional privacy frameworks that are more attuned to concerns from these communities.

PENGEMBANGAN MEDIA PEMBELAJARAN DENGAN APLIKASI WORKED EXAMPLE BERBANTUAN ANDROID UNTUK MENINGKATAKAN PRESTASI BELAJAR (Studi Pada Mata Pelajaran Fisika Kelas X Di SMA Negeri 1 Pagar Alam)

The aims of this research is to describe the learning media with the android assisted worked example application which is feasible to improve learning achievement and Describing the effectiveness of learning media with the android assisted worked example application in improving learning achievement. In this research, the development design (Research and Development) was used. The research subjects were students of class X MIPA at SMA Negeri 1 Pagar Alam. The data collection techniques used in the research were assessment sheets and student test results. This research data analysis with mean and t-test. The validation instrument for media and material experts in the research was a Likert scale. The conclusion of this research is the learning media with the android assisted worked example application developed are feasible to improve learning achievement and the development of learning media with the android assisted worked example application effective in improving learning achievement based on the results of limited trials and broad scale tests for class X MIPA SMA Negeri 1 Pagar Alam on Work and Energy Physics subject matter

Action recognition for educational proposals applying concepts of Social Assistive Robotics

Action recognition has been gaining interest in research due to its great number of applications. So, the main contribution of this manuscript is a human–robot interaction framework, relying on dimension reduction of the system’s inputs in order to require a smaller dataset for training of an Artificial Neural Network. Our motivation is the development of a Social Assistive Robotics application. In summary, we choose nine standard actions to guide a robot, and two neutral ones to represent stand-by or resting cases. The dataset is created by people with different body shape, for robustness purposes, using only 5 to 10 samples of each class per person. Offline and online tests validate the method’s accuracy and confusion matrices clarify the results. A TicTacToe game using a ground robot exemplify a real world application, where each action represents a desired spot in the game. The results confirm a high accuracy, above 96.7%, in all the tests. Based on this, we can conclude our preprocessing strategy and classifier are capable of identifying the action patterns, even for a tiny dataset; thus, it is recommended for educational proposals due to its simplicity.

Identifying Phonological Barriers in Korean-Accented English Using Artificial Intelligence Apps

Foreseeing the future of English education, this study has incorporated artificial intelligence voice assistant mobile phone applications (AI apps) into English Language Teaching. The study explored: (1) to what extent the current AI apps can dictate Korean-accented English (KoE), and (2) the phonological elements that are commonly misrecognized by the current AI apps. To accomplish these goals, 108 Korean undergraduates, whose English proficiency levels were on average B2 (upper-intermediate level) according to the Common European Framework of Reference for languages (CEFR), participated in the study. Each participant read twenty-five sentences aloud, while AI apps produced transcripts of their dictation. Then the percentage of correctly recognized target words was calculated to determine speech intelligibility. The findings show that KoE attained an accuracy rate of 56.8 percent on average, ranging from 50.1 to 62.7 percent. The KoE barriers associated with the phonological features of less intelligible tokens were also investigated. By bringing attention to the features in comparison to the Lingua Franca Core targets (Jenkins, 2000), the findings suggest the necessity of further research into the feasibility of implementing AI apps within teaching practices, and expanding the boundaries of English as a lingua franca research to KoE, which has not been sufficiently studied.

Descubriendo Soluciones Juntos: Cultural Adaptation of Problem-Solving Training for Latinx Care Partners

<h3>Research Objectives</h3> To culturally adapt and pilot test the Spanish-language version of Problem-Solving Training (PST) - Descubriendo Soluciones Juntos (DSJ) - for Spanish-speaking Latinx care partners of adults with neurological conditions. PST is an evidence-based metacognitive strategy training approach that teaches a method for evaluating problems, generating/selecting solutions, creating/implementing realistic goals and action plans, and evaluating plan effectiveness. <h3>Design</h3> Single-group, pre-post-test pilot study with iterative adaptation of DSJ based on the protocol outlined by Marsiglia & Booth (2015), which provides justification and a roadmap for cultural adaptation of behavioral interventions. <h3>Setting</h3> Community. <h3>Participants</h3> A convenience sample of 10 Spanish-speaking, Latinx adult care partners of individuals with neurological conditions recruited from our Acquired Brain Injury Research Registry, TBI Model Systems study, and Spanish-language flyers available to care partners accompanying care recipients to UT Southwestern PM&R outpatient clinics. <h3>Interventions</h3> Six previously validated structured PST sessions (30-60 minutes each, ∼1-2x/week) translated to Spanish and delivered via telephone/videoconference by rehabilitation neuropsychologists. Sessions consist of assisted application of the ABCDEF steps of DSJ (Assess, Brainstorm, Consider and Choose, Develop and Do, Evaluate & Flex) to develop a detailed action plan to solve specific problems and achieve the goal. <h3>Main Outcome Measures</h3> 1) Demographics (age, gender, race, ethnicity, education); 2) Care relationship information (nature, duration, living status); 3) Acculturation; and 4) satisfaction with DSJ via the Client Satisfaction Questionnaire (CSQ). All measures are collected via REDCap™ electronic survey at baseline and 1-month post intervention. <h3>Results</h3> We describe the iterative adaptation of "Descubriendo Soluciones Juntos," a culturally adapted Spanish-language version of PST for broader implementation to Spanish-speaking individuals. Qualitative experiences of participants and descriptive measures indicating pre-to-post intervention change in measures of caregiver burden and well-being are presented as a case series. <h3>Conclusions</h3> Developing a Spanish-language, culturally adapted version of PST for Latinx care partners is a critical step towards reducing health disparities and establishing guidelines for an evidence-based, self-management-promoting intervention for care partners of adults with neurological conditions. <h3>Author(s) Disclosures</h3> This work was funded by a grant from Communities Foundation of Texas.

Contactless tracking of humans using non-contact triboelectric sensing technology: Enabling new assistive applications for the elderly and the visually impaired

Continuous monitoring of human motion and its interaction with the surrounding environment is crucial for healthcare applications such as early diagnosis of neurological disorders, or rehabilitation of vulnerable patients with walking difficulties in hospital and age-care settings. Additionally, it is also necessary to provide assistive care to visually impaired individuals who are prone to falls. Commonly used wearable sensors require on-body components, which may cause discomfort. More importantly, the forgetfulness of people in hospital or aged-care settings or their resistance in using the on-body units, undermine the effectiveness of a wearable-based monitoring strategy. The ideal monitoring system should not interfere with the individual’s privacy and daily routines and work continuously, without requiring any specific on-body devices. Here, we present a contactless sensing platform that can monitor and distinguish complex movements of human subjects, without requiring them to use wearable devices. The approach uses flexible Non-Contact Triboelectric Sensors (NCTS) (7 ×3.6 ×0.5 cm3) produced by attaching a negatively pre-charged PDMS film onto an aluminum (Al) foil. By exploiting the sensitivity to external electrostatic charges, near-field remote monitoring of human movements at distances as far as 1.5 m, is successfully achieved. Furthermore, The NCTS system differentiates activities such as walking, running, and jumping; and estimates walking speed, relative position, and direction of two people walking together in a contactless manner, which is not well examined in the literature of Non-contact Triboelectric Sensors, by analyzing the time domain and frequency spectrum of the output signal. Besides, the sensor is integrated with a portable accident prevention system designed to avoid visually impaired people’s collisions with obstacles. Falling detection tests provide promising results for their application in elderly safety. Finally, indoor navigation/position monitoring and direction recognition are achieved and demonstrated using multiple NCTS, for future potential applications in elder activity tracking and people counting in hospitals.

Virtual voice assistant applications improved expressive verbal abilities and social interactions in children with autism spectrum disorder: a Single-Subject experimental study

The use of interactive technologies has been demonstrated to enhance verbal and non-verbal communication, as well as the social interaction tendencies of children with Autism Spectrum Disorder (ASD). We examined effects of using Virtual Voice Assistant (VVAs) in children with ASD with respect to two outcomes: speech skills and social interaction skills. A single-case study included three children with ASD (4–11 years old) that utilized VVAs for three months. Pre- and post-intervention questionnaires and semi-structured interviews were used to measure the communication and social interaction skills of the participating children. Participant One, Two and Three showed improvement in the number of correct words produced the VVA intervention. All participants showed increases in social interactions in the intervention phase. Overall, the results showed that the VVAs had positive effects on the speech and social interaction skills of autistic children. The findings demonstrate that children with ASD may benefit from VVAs to improve their communication skills.

Ensemble Learning Based Brain–Computer Interface System for Ground Vehicle Control

This article establishes a novel electroencephalograph (EEG)-based brain-computer interface (BCI) system for ground vehicle control with potential application of mobility assistance to the disabled. To enable an intuitive motor imagery (MI) paradigm of “left,” “right,” “push,” and “pull,” a driving simulator based EEG data recording and automatic labeling platform is built for dataset making. In the preprocessing stage, a wavelet and canonical correlation analysis (CCA) combined method is used for artifact removal and improving signal-to-noise ratio. An ensemble learning based training and testing framework is proposed for MI EEG data classification. The average classification accuracy of proposed framework is about 91.75%. This approach essentially takes advantage of the common spatial pattern (CSP) with ability of extracting the feature of event-related potentials and the convolutional neural networks (CNNs) with powerful capacity of feature learning and classification. To convert the classification results of EEG data segments into motion control signals of ground vehicle, shared control strategy is used to realize the control command of “left-steering,” “right-steering,” “acceleration,” and “stop” considering collision avoidance with obstacles detected by a single-line LIDAR. The online experimental results on a model vehicle platform validate the significant performance of the established BCI system and reveal the application potential of BCI on the vehicle control and automation.

A Case Study on Information and Communication Technology in Empowering the Visually Challenged Women in Inclusive Eduction

Brigham Young posits that ‘If you educate a man, you educate an individual, but you educate a woman, you educate a nation. Even now, educating a woman is challenging in cultural practices dominated by values and beliefs in the social system. Women in developing countries face significant barriers in accessing education, whereas gender inequality in communities, society, and the workplace hinders economic and social development. Education as a means of empowerment of women can bring changes socially, economically, and politically. An American author, Helen Keller, says that ‘Education cannot be created with ease, only through the experience of trial and sufferings it can be strengthened, inspired and achieved. Women with visual impairment are still receiving less schooling, denied social acceptance, and lack of access to information also became an impediment added to their woes. Perhaps, ICT has changed visually impaired women’s lives by giving them access to mainstream and trying to be independent financially and socially.&#x0D; Furthermore, ICT has allowed visually impaired women in accessing braille materials, operating assistive devices and applications that build confidence, recognition, self-expression, and acquire knowledge. This study explores how inclusive education creates an opportunity for visually impaired women in overcoming societal stigma by achieving personal, social, emotional, and academic goals. ICT in education plays an essential role in developing women and facilitates empowerment, calibrated to a new order of society’s structure and functions.

Study of electrooculography signal acquisition sites for assistive device applications

Study of electrooculography signal acquisition sites for assistive device applications

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applications.

Fast speech adversarial example generation for keyword spotting system with conditional GAN

Deep network-based keyword spotting (KWS) has embraced great success in many speech assistant applications. However, such network-based KWS systems were demonstrated vulnerable to adversarial attacks. In this work, we propose to utilize a conditional generative adversarial network (CGAN) to efficiently craft targeted speech adversarial examples. Specifically, we first transform the attacking target label into a vector, which is treated as the condition input of CGAN. The generator in CGAN is tasked to generate perturbation that could make the adversarial example misclassified as the pre-specified target keyword, while simultaneously deceiving the discriminator to misclassify the adversarial example as genuine. The discriminator aims to differentiate the crafted adversarial examples from the legitimate samples. Secondly, the target network-based KWS classifier(s) are ensembled and integrated into the proposed CGAN framework to enforce the generator to construct model-independent perturbation. The classification error loss of the target KWS is back-propagated through gradients for guiding the weight update of the generator. Finally, with properly devised network architecture and training procedure, we obtain a well-trained generator that generates the adversarial perturbation for a given speech clip and target label. Experimental results show that the crafted adversarial examples could effectively attack the state-of-the-art KWS system with quite a high attack success rate, while attaining acceptable perception quality.

How to Effectively Decrease Patient Co-Payments of High-Cost Drugs Through Innovation: Lessons From the Karmanos Specialty Pharmacy.

High-cost drugs impose a financial burden on patients with cancer. Karmanos Specialty Pharmacy (KSP) developed a process to automate financial assistance (FA) applications to decrease patient drug cost. We evaluate the outcomes of this program on cost to patients and payers. This is an observational, retrospective study of the KSP claims data set from January to December 2019, accessed by 13 statewide cancer centers within Michigan. Drug cost of patients, payers, FA (funds to lower patient drug cost), and types of FA were obtained. A subset analysis was performed to determine drug delivery times. In 2019, 869 prescriptions and 1,722 prescription fills were provided to 463 patients through KSP. The total cost of drug claims was approximately $10 million US dollars (USD) among Medicare patients (58%), approximately $3.4 million USD for privately insured patients (20%), and approximately $3.7 million USD for Medicaid patients (22%). Twenty-seven percent of patients (22% of all prescription fills) required additional FA with initial total co-payment claims of $335,216 USD. $280,988 USD of FA was obtained, which substantially lowered total patient costs by 81%. $250,818 USD of FA obtained was from foundation grants (327 fills), and $21,441 USD from manufacturer co-pay cards (47 fills). An additional $12,260 USD (12 fills) from a Karmanos Patient Assistance Fund was used. There was high dependence on foundation grant assistance among Medicare patients (33% of claims). In a subset analysis, the median time from prescription written to delivery to the patient was < 7 days (0-56 days). Twenty-seven percent of patients (22% of prescriptions fills) in 2019 required additional FA for high-cost drugs. KSP substantially reduced patient cost by implementing an efficient process using additional pharmacy assistants to obtain FA.