Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 6 appraisals
Science advances
An AI-driven, wearable, conformal ring system for real-time and user-independent sign language interpretation.
Sign language translation systems have long aimed to bridge the communication between signers and nonsigners. However, preliminary systems rely on glove-type wearables or wired sensor arrays, which constrain hand movement, reduce comfort, and require nonpersonalized sensor positions that limit adaptability across users. Here, we introduce a wirelessly connected, ring-type sign language translator (WRSLT) designed to overcome these limitations by enabling full finger mobility through independent sensor rings and multilink communication. The system supports static and dynamic gesture detection using selected fingers via quantitative relevance analysis and achieves robust user-independent performance without per-user calibration. WRSLT demonstrated high recognition accuracy on large-scale datasets comprising 100 American Sign Language and 100 International Sign Language words, achieving 88.3 and 88.5% accuracy, respectively, under unseen-user conditions (i.e., test users not included in model training). Furthermore, a custom sequential word detection framework enables sentence-level translation from continuous signing input without requiring separate training on entire sentence structures.
5 May 2026
Read appraisal →PloS one
Comparison of deep learning-based three-dimensional human pose estimation methods with motion capture for gesture research
Humans often make gestures while speaking. These gestures have been extensively researched in psychology and cognitive science, revealing their functions and roles in communication. Despite being produced in three-dimensional (3D) space, gestures and gesture space have primarily been measured and analyzed using two-dimensional planes. Optical motion capture (MoCap) is commonly used for 3D gesture measurement. However, MoCap is expensive, requires a large space, and the attachment of retroreflective markers can interfere with the natural generation of gestures by the speaker. Recent advances in deep learning-based human pose estimation (HPE) offer promising alternatives by enabling 3D keypoint estimation from standard video cameras. This study investigates the accuracy of four HPEs-two monocular and two stereo with triangulation methods-in estimating upper-body keypoints commonly used in natural scenarios for gesture research. Ten participants were recorded performing gesture-rich speech by both a MoCap system and three video cameras. We compared 13 keypoints, including the wrists, elbows, shoulders, fingers, and face, with MoCap data, using Euclidean distance as an error metric. Statistical analyses revealed that stereo methods significantly outperformed monocular methods across all keypoints. The most accurate method achieved an average error of 49.4 mm with respect to MoCap, suggesting sufficient accuracy for practical gesture analysis. Additionally, we visualized 3D gesture spaces and found a 75.4% overlap between HPE and MoCap at a voxel size of 50 mm, indicating high spatial agreement. Our findings demonstrate that stereo HPE methods are viable, cost-effective alternatives to MoCap in gesture-related applications. Our ultimate objective is to create a toolbox that is accurate and easy to use for measuring human gestures in 3D space by exploiting the recent advances in HPE as an alternative to MoCap. Such a toolbox will be suitable for non-experts in machine learning, and these results lay the foundation for it.
27 Apr 2026
Read appraisal →Journal of speech, language, and hearing research : JSLHR
Rhotic Generalization Is More Rapid in Biofeedback Than Motor-Based Treatment for Residual Speech Sound Disorder: Secondary Outcomes of a Randomized Controlled Trial
PURPOSE: This study examined the effects of visual biofeedback approaches and nonbiofeedback motor-based treatment on generalization outcomes following speech therapy for children with residual speech sound disorders (RSSDs). METHOD: A total of 108 children aged 9-15 years with RSSD affecting American English /ɹ/ were randomly assigned to receive 19 motor-based speech treatment sessions, with or without visual biofeedback (divided into ultrasound or visual-acoustic biofeedback). The treatment included practice designed to implement several motor learning principles, with task difficulty systematically adjusted based on the child's performance. Children's /ɹ/ accuracy on untreated words before and after treatment was rated as correct or incorrect by lay listeners who were blinded to participant characteristics, treatment conditions, and time points. RESULTS: The mixed-effects regression model revealed a statistically significant interaction between treatment type and time point. Specifically, both the biofeedback and nonbiofeedback motor-based treatment groups made progress over time, but the amount of generalization to untreated words was significantly greater in the biofeedback condition than in the motor-based treatment. In a subanalysis comparing biofeedback types, greater generalization was observed following ultrasound biofeedback than visual-acoustic biofeedback, although this effect was strongest at one treatment site. DISCUSSION: This randomized controlled trial found that adding biofeedback to motor-based treatment can increase the rate of accurate production of the American English /ɹ/ in untreated words.
12 Apr 2026
Read appraisal →Journal of speech, language, and hearing research : JSLHR
Information Load Predicts Stuttering Events
PURPOSE: One potential contributor to the variability of stuttering events is information load, quantified by assessing word predictability based on preceding context. This study investigated whether words with greater information load are more likely to be stuttered using word surprisal as an index while controlling for relevant linguistic variables. We also examined whether this relationship varied with stuttering severity and impact. METHOD: We analyzed spontaneous speech samples from 35 adults who stutter, drawn from the FluencyBank corpus. Word surprisal values were computed using the Generative Pretrained Transformer 2 large language model and analyses controlled for six linguistic predictors of stuttering. We also tested whether clinical measures of stuttering severity and overall impact predicted individuals' sensitivity to surprisal. RESULTS: Higher word surprisal significantly increased the likelihood of stuttering, even after controlling for linguistic covariates. However, we found no evidence that stuttering severity or overall impact moderated this relationship. CONCLUSIONS: These findings suggest that words with greater information load are more likely to elicit stuttering during spontaneous speech. This pattern may reflect increased communicative demands associated with high-surprisal words. The absence of a moderating effect of stuttering severity or impact may indicate that individual differences in coping with communicative pressure, rather than clinical severity alone, shape stuttering variability.
11 Apr 2026
Read appraisal →Journal of speech, language, and hearing research : JSLHR
Rhotic Acquisition Is More Rapid in Biofeedback Than Motor-Based Treatment for Residual Speech Sound Disorder: Primary Outcome of a Randomized Controlled Trial
PURPOSE: Residual speech sound disorder (RSSD) is a high-prevalence condition that can limit children's academic and social participation, with negative consequences for overall well-being. Previous studies have described visual biofeedback as a promising option for RSSD, but results have been inconclusive due to study design limitations and small sample sizes. METHOD: In a preregistered randomized controlled trial, 108 children aged 9-15 years with RSSD affecting American English /ɹ/ were randomly assigned to receive treatment incorporating visual biofeedback (subdivided into ultrasound and visual-acoustic types) or a comparison condition of motor-based treatment consistent with current best practices in speech therapy. An acoustic measure (the distance between the second and third formants, which is smaller in perceptually accurate /ɹ/) was used to quantify progress in the first three sessions of treatment in participants' assigned condition. The focus on the early stages of treatment was grounded in principles of motor learning: As a type of knowledge of performance feedback, biofeedback is expected to have its greatest impact during initial acquisition of a new speech-motor plan. RESULTS: A linear mixed-effects regression model revealed a statistically significant interaction between treatment type and session: While both groups made progress over time, the rate of progress was significantly faster in the biofeedback condition compared to motor-based treatment. No statistically significant difference was observed between the ultrasound and visual-acoustic types of biofeedback. DISCUSSION: This large-scale randomized controlled trial supports previous small-scale research in finding that biofeedback can help children with RSSD acquire American English /ɹ/. A companion study will compare participants' generalization learning after the end of all treatment. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.29971501.
11 Apr 2026
Read appraisal →Journal of medical Internet research
Innovations in Deaf Health Care Communication: Systematic Review of Sign Language Recognition Systems
BACKGROUND: Deaf individuals often face communication challenges when interacting with those who can hear. Within health care settings, these challenges may pose risks to their safety, potentially resulting in misdiagnoses, treatment errors, and decreased quality of care. OBJECTIVE: This study aims to systematically review the evidence on communication systems reported in the literature that use human-computer interaction techniques to support communication between deaf individuals who use sign language and hearing health professionals in health care settings. The review focuses on systems that are either currently in use or proposed for use in health care and that have been tested using human participants or videos of human users. METHODS: A comprehensive search was performed via MEDLINE, Web of Science, ACM, IEEE Xplore, Scopus, and Google Scholar in March 2025. The inclusion criteria comprised studies developing a sign language recognition system within a health care context and testing with human users. Eligible studies underwent screening by 2 independent investigators (LRV and LMMSR or LFRdO and GTdSS), with any disagreements resolved by a senior researcher (MSM). RESULTS: The search retrieved 21,778 publications, and screening of reference lists identified 2 additional studies, resulting in a total of 23 studies meeting the eligibility criteria. Most systems (15/23, 65.2%) were image-based, while 34.8% (8/23) relied on sensors (glove-based or depth-sensing). Applications varied across health care settings, including general hospital care (10/23, 43.5%), emergencies (8/23, 34.8%), and primary care (4/23, 17.4%). All systems were in the development and testing stage, with no data on security and psychological impacts. Accuracy ranged from 25% to 100% for image-based and 72% to 99.7% for sensor-based systems. Bidirectionality and facial expression recognition, crucial for effective communication, were largely overlooked. CONCLUSIONS: Image-based systems were more common than sensor-based ones, though both showed wide variability in accuracy in recognizing and interpreting signs. Most systems failed to address critical aspects such as bidirectional communication and the recognition of facial expressions, essential for effective communication. None fully addresses the requirements for integration into health care settings. These findings highlight the need for further research on implementation, usability, and impact on the quality of care for deaf patients.
10 Apr 2026
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