The use of non-speech oral-motor exercises among Indian speech-language pathologists to treat speech disorders: An online survey

Objective: Previous surveys in the United States of America (USA), the United Kingdom (UK), and Canada have indicated that most of the speech-language pathologists (SLPs) tend to use non-speech oral-motor exercises (NSOMEs) on a regular basis to treat speech disorders.At present, there is considerable debate regarding the clinical effectiveness of NSOMEs. Thecurrent study aimed to investigate the pattern and extent of usage of NSOMEs among Indian SLPs. Method: An online survey intended to elicit information regarding the use of NSOMEswas sent to 505 members of the Indian Speech and Hearing Association. The questionnaire consisted of three sections. The first section solicited demographic information, the second and third sections solicited information from participants who did and did not prefer to use NSOMEs, respectively. Descriptive statistics were employed to analyse the responses that were clinically relevant. Results: A total of 127 participants responded to the survey. Ninety-one percent of the participants who responded to the survey indicated that they used NSOMEs. Conclusion: The results suggested that the percentage of SLPs preferring to use NSOMEsis similar to the findings of surveys conducted in the USA, the UK, and Canada. The Indian SLPs continue to use NSOMEs based on a multitude of beliefs. It is important for SLPs toincorporate the principles of evidence-based practice while using NSOMEs to provide high quality clinical care

Assessment of Driver's Stress using Multimodal Biosignals and Regularized Deep Kernel Learning

In this work, we classify the stress state of car drivers using multimodal physiological signals and regularized deep kernel learning. Using a driving simulator in a controlled environment, we acquire electrocardiography (ECG), electrodermal activity (EDA), photoplethysmography (PPG), and respiration rate (RESP) from N = 10 healthy drivers in experiments of 25min duration with different stress states (5min resting, 10min driving, 10min driving + answering cognitive questions). We manually remove unusable segments and approximately 4h of data remain. Multimodal time and frequency features are extracted and employed to regularized deep kernel machine learning based on a fusion framework. Task-specific representations of different physiological signals are combined using intermediate fusion. Subsequently, the fused multimodal features are fed a support vector machine (SVM) and a random forest (RF) for stress classification. The experimental results show that the proposed approach can discriminate between stress states. The combination of PPG and ECG using RF as classifier yields the highest F1-score of 0.97 in the test set. PPG only and RF yield a maximum F1-score of 0.90. Furthermore, subject-specific cross-validation improves performance. ECG and PPG signals are reliable in classifying the stress state of a car driver. In summary, the proposed framework could be extended to real-time stress state assessment in driving conditions