scholarly journals Social Robotics in Therapy of Apraxia of Speech

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
José Carlos Castillo ◽  
Diego Álvarez-Fernández ◽  
Fernando Alonso-Martín ◽  
Sara Marques-Villarroya ◽  
Miguel A. Salichs
Keyword(s):  
Multimodal Interaction ◽  
Social Robot ◽  
Machine Learning Techniques ◽  
Apraxia Of Speech ◽  
Motor Speech ◽  
Motor Speech Disorder ◽  
Speech Rehabilitation ◽  
Learning Techniques ◽  
Current Therapies ◽  
The Right

Apraxia of speech is a motor speech disorder in which messages from the brain to the mouth are disrupted, resulting in an inability for moving lips or tongue to the right place to pronounce sounds correctly. Current therapies for this condition involve a therapist that in one-on-one sessions conducts the exercises. Our aim is to work in the line of robotic therapies in which a robot is able to perform partially or autonomously a therapy session, endowing a social robot with the ability of assisting therapists in apraxia of speech rehabilitation exercises. Therefore, we integrate computer vision and machine learning techniques to detect the mouth pose of the user and, on top of that, our social robot performs autonomously the different steps of the therapy using multimodal interaction.

scholarly journals Learning Curves: A Novel Approach for Robustness Improvement of Load Forecasting

2021 ◽  
Vol 5 (1) ◽  
pp. 38
Author(s):  
Chiara Giola ◽  
Piero Danti ◽  
Sandro Magnani
Keyword(s):  
Load Forecasting ◽  
Learning Curves ◽  
Machine Learning Techniques ◽  
Essential Information ◽  
Set Size ◽  
Novel Approach ◽  
Learning Techniques ◽  
Energy Plant ◽  
Definition Of ◽  
The Right

In the age of AI, companies strive to extract benefits from data. In the first steps of data analysis, an arduous dilemma scientists have to cope with is the definition of the ’right’ quantity of data needed for a certain task. In particular, when dealing with energy management, one of the most thriving application of AI is the consumption’s optimization of energy plant generators. When designing a strategy to improve the generators’ schedule, a piece of essential information is the future energy load requested by the plant. This topic, in the literature it is referred to as load forecasting, has lately gained great popularity; in this paper authors underline the problem of estimating the correct size of data to train prediction algorithms and propose a suitable methodology. The main characters of this methodology are the Learning Curves, a powerful tool to track algorithms performance whilst data training-set size varies. At first, a brief review of the state of the art and a shallow analysis of eligible machine learning techniques are offered. Furthermore, the hypothesis and constraints of the work are explained, presenting the dataset and the goal of the analysis. Finally, the methodology is elucidated and the results are discussed.

scholarly journals Childhood Apraxia of Speech. Developmental Speech Patterns. A Wide Retrospective Study

2020 ◽  
Vol 11 (3sup1) ◽  
pp. 54-68
Author(s):  
Elad Vashdi ◽  
◽  
Amit Avramov ◽  
Špela Falatov ◽  
Huang Yi-Chen ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Sound Production ◽  
Speech Disorder ◽  
Apraxia Of Speech ◽  
Childhood Apraxia Of Speech ◽  
Motor Speech ◽  
Entry Level ◽  
Motor Speech Disorder ◽  
Speech Patterns

Patterns of a phenomenon define the entity. If one understands the patterns of the maze, he can find his way there. Patterns of colors on a dress will hold its characters and soul. Understanding the expressive patterns of a developmental syndrome enables treating it with success. It is true for treating Childhood Apraxia of speech (CAS) as well. CAS as motor-speech disorder involves difficulties in sounds production for speech purposes. The difficulties can be demonstrated in patterns that would be specific to CAS. These patterns can distinguish one phenomenon from another. A retrospective research was conducted based on 277 entry level evaluations of children diagnosed with CAS or suspected of CAS who visited a private clinic between 2006 and 2013. The analysis included speech variables alongside background and environmental variables. This article is dealing with speech patterns of children with motor speech disorder. Among the patterns examined are vowels ladder, single syllable ladder, Blowing and SSP (single sound production), Oral motor and SSP, Consonant group ladder and Consonants Exploratory factor analysis. The findings demonstrated the relationship and order of vowels, consonants and single syllables among Hebrew speaking children diagnosed with motor speech disorder. The Consonants Exploratory factor analysis gave validity to the existence of unique consonant groups. Further discussion regarding every result and its implication is included. Understanding the unique patterns of consonants and vowels strength among children with CAS can help clinicians in the decision-making process and goals targeting.

scholarly journals Big Data Use and Challenges: Insights from Two Internet-Mediated Surveys

Computers ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 73 ◽  
Author(s):  
Rossi ◽  
Rubattino ◽  
Viscusi
Keyword(s):  
Big Data ◽  
New Ventures ◽  
Machine Learning Techniques ◽  
Personal Safety ◽  
Privacy And Security ◽  
Public And Private ◽  
Private Organizations ◽  
Learning Techniques ◽  
The Right ◽  
Low Rate

Big data and analytics have received great attention from practitioners and academics, nowadays representing a key resource for the renewed interest in artificial intelligence, especially for machine learning techniques. In this article we explore the use of big data and analytics by different types of organizations, from various countries and industries, including the ones with a limited size and capabilities compared to corporations or new ventures. In particular, we are interested in organizations where the exploitation of big data and analytics may have social value in terms of, e.g., public and personal safety. Hence, this article discusses the results of two multi-industry and multi-country surveys carried out on a sample of public and private organizations. The results show a low rate of utilization of the data collected due to, among other issues, privacy and security, as well as the lack of staff trained in data analysis. Also, the two surveys show a challenge to reach an appropriate level of effectiveness in the use of big data and analytics, due to the shortage of the right tools and, again, capabilities, often related to a low rate of digital transformation.

scholarly journals Communication Limitations in Patients With Progressive Apraxia of Speech and Aphasia

2020 ◽  
Vol 29 (4) ◽  
pp. 1976-1986
Author(s):  
Rene L. Utianski ◽  
Heather M. Clark ◽  
Joseph R. Duffy ◽  
Hugo Botha ◽  
Jennifer L. Whitwell ◽  
...  
Keyword(s):  
Functional Communication ◽  
Apraxia Of Speech ◽  
Speech And Language ◽  
Motor Speech ◽  
Entire Cohort ◽  
Severity Rating ◽  
Speech Language Pathologist ◽  
Motor Speech Disorder ◽  
Language Evaluations ◽  
Language Measures

Purpose Individuals with primary progressive apraxia of speech (AOS) have AOS in which disruptions in articulation and prosody predominate the speech pattern. Many develop aphasia and/or dysarthria later in the disease course. The aim of this study was to describe the communication limitations in these patients, as measured by (a) the patient via the Communicative Participation Item Bank (CPIB) and (b) the speech-language pathologist via the American Speech-Language-Hearing Association's (ASHA) Functional Communication Measures (FCMs) and an adapted motor speech disorder (MSD) severity rating. Method Speech and language evaluations were completed for 24 patients with progressive AOS ( n = 7 with isolated AOS; n = 17 with a combination of AOS and aphasia). Descriptive comparisons were utilized to evaluate differences in communication measures among patients with various combinations of MSDs and aphasia. Differences associated with phonetic predominant or prosodic predominant AOS were also examined. Across the entire cohort, correlations were calculated between the participation ratings and other clinical assessment measures. Results The CPIB reflected greater limitations for those with aphasia and AOS compared to isolated AOS, but was not notably different when dysarthria occurred with AOS ( n = 9/24). Across the cohort, there were statistically significant correlations between the CPIB and ASHA FCM–Motor Speech and Language Expression ratings and the MSD severity rating. The CPIB did not correlate with the ASHA FCM–Language Comprehension or other speech-language measures. Conclusions Patients with neurodegenerative AOS experience reduced participation in communication that is further exacerbated by co-occurring language deficits. The study suggests measures of severity cannot be assumed to correlate with measures of participation restrictions and offers a foundation for further research examining the day-to-day sequela of progressive speech and language disorders. Supplemental Material https://doi.org/10.23641/asha.12743252

scholarly journals Deep Regressor: Cross Subject Academic Performance Prediction System for University Level Students

2019 ◽  
Vol 8 (11S) ◽  
pp. 1265-1267
Keyword(s):  
Machine Learning ◽  
Academic Performance ◽  
Evaluation Process ◽  
Education Institution ◽  
Drop Out ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Current Program ◽  
The Right ◽  
The University

Predicting the academic performance of students has been an important research topic in the Educational field. The main aim of a higher education institution is to provide quality education for students. One way to accomplish a higher level of quality of education is by predicting student’s academic performance and there by taking earlyre- medial actions to improve the same. This paper presents a system which utilizes machine learning techniques to classify and predict the academic performance of the students at the right time before the drop out occurs. The system first accepts the performance parameters of the basic level courses which the student had already passed as these parameters also influence the further study. To pre- dict the performance of the current program, the system continuously accepts the academic performance parame- ters after each academic evaluation process. The system employs machine learning techniques to study the aca- demic performance of the students after each evaluation process. The system also learns the basic rules followed by the University for assessing the students. Based on the present performance of the students, the system classifies the students into different levels and identify the students at high risk. Earlier prediction can help the students to adopt suitable measures in advance to improve the per for- man ce. The systems can also identify the factor saffecting the performance of the same students which helps them to take remedial measures in advance.

scholarly journals Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

Machines ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 38 ◽  
Author(s):  
Fabrizio Balducci ◽  
Donato Impedovo ◽  
Giuseppe Pirlo
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Agricultural Field ◽  
Heterogeneous Information ◽  
Learning Techniques ◽  
Farm Model ◽  
Machine Learning Applications ◽  
Crop Harvest ◽  
The Right ◽  
The Internet Of Things

This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it.

scholarly journals Teaching robots social autonomy from in situ human guidance

2019 ◽  
Vol 4 (35) ◽  
pp. eaat1186 ◽  
Author(s):  
Emmanuel Senft ◽  
Séverin Lemaignan ◽  
Paul E. Baxter ◽  
Madeleine Bartlett ◽  
Tony Belpaeme
Keyword(s):  
Autonomous Systems ◽  
Human Robot Interaction ◽  
Machine Learning Techniques ◽  
Generic Properties ◽  
Domain Specific ◽  
Learning Techniques ◽  
The One ◽  
The Right ◽  
Social Autonomy

Striking the right balance between robot autonomy and human control is a core challenge in social robotics, in both technical and ethical terms. On the one hand, extended robot autonomy offers the potential for increased human productivity and for the off-loading of physical and cognitive tasks. On the other hand, making the most of human technical and social expertise, as well as maintaining accountability, is highly desirable. This is particularly relevant in domains such as medical therapy and education, where social robots hold substantial promise, but where there is a high cost to poorly performing autonomous systems, compounded by ethical concerns. We present a field study in which we evaluate SPARC (supervised progressively autonomous robot competencies), an innovative approach addressing this challenge whereby a robot progressively learns appropriate autonomous behavior from in situ human demonstrations and guidance. Using online machine learning techniques, we demonstrate that the robot could effectively acquire legible and congruent social policies in a high-dimensional child-tutoring situation needing only a limited number of demonstrations while preserving human supervision whenever desirable. By exploiting human expertise, our technique enables rapid learning of autonomous social and domain-specific policies in complex and nondeterministic environments. Last, we underline the generic properties of SPARC and discuss how this paradigm is relevant to a broad range of difficult human-robot interaction scenarios.

Prediction and Recommendation System for Diabetes Using Machine Learning Models

2022 ◽  
pp. 316-327
Author(s):  
Nareshkumar Mustary ◽  
Phani Kumar Singamsetty
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Medical Center ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Diabetes Prediction ◽  
Artery Disease ◽  
The Right ◽  
Deep Learning Model ◽  
Machine Learning Models

Diabetes is one of the most deadly diseases on the planet. It is also a cause of a variety of illnesses, such as coronary artery disease, blindness, and urinary organ disease. In this situation, the patient must visit a medical center to obtain their results following consultation. Finding the right combination of characteristics and machine learning techniques for classification is also very critical. However, with the advancement of machine learning techniques, we now have the potential to find a solution to the current problem. The healthcare recommendation system (HRS) may be designed to predict health by evaluating patient lifestyle, physical health, mental health aspects using machine learning. For example, training the model using people's age and diabetes helps to predict new patients without a specific diagnostic for diabetes. The proposed deep learning model with convolutional neural network (D-CNN) achieves an overall accuracy of 96.25%. D-CNN is found to be more successful for diabetes prediction than other machine learning (ML) approaches in the experimental analysis.

Data Analytics and Modeling in IoT-Fog Environment for Resource Constrained IoT-Applications - A Review

2021 ◽  
Vol 14 ◽  
Author(s):  
Omar Farooq ◽  
Parminder Singh
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Data Classification ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Resource Constrained ◽  
Learning Techniques ◽  
Iot Devices ◽  
The Right ◽  
Continuous Use

Introduction: The emergence of the concepts like Big Data, Data Science, Machine Learning (ML), and the Internet of Things (IoT) has added the potential of research in today's world. The continuous use of IoT devices, sensors, etc. that collect data continuously puts tremendous pressure on the existing IoT network. Materials and Methods: This resource-constrained IoT environment is flooded with data acquired from millions of IoT nodes deployed at the device level. The limited resources of the IoT Network have driven the researchers towards data Management. This paper focuses on data classification at the device level, edge/fog level, and cloud level using machine learning techniques. Results: The data coming from different devices is vast and is of variety. Therefore, it becomes essential to choose the right approach for classification and analysis. It will help optimize the data at the device edge/fog level to better the network's performance in the future. Conclusion: This paper presents data classification, machine learning approaches, and a proposed mathematical model for the IoT environment.

Investigation on Identifying Road Anomalies using In-Vehicle Sensors for Cooperative Applications and Road Asset Management

2020 ◽  
Vol 2674 (7) ◽  
pp. 585-595
Author(s):  
Moksheeth Padarthy ◽  
Mohammed Sami ◽  
Emiliano Heyns
Keyword(s):  
Asset Management ◽  
Real Life ◽  
Pavement Management ◽  
Machine Learning Techniques ◽  
Lateral Acceleration ◽  
The Road ◽  
Pavement Management Systems ◽  
Learning Techniques ◽  
The Right ◽  
Vehicle Sensors

One of the main challenges for road authorities is to maintain the quality of the road infrastructure. Road anomalies can have a significant impact on traffic flow, the condition of vehicles, and the comfort of occupants of vehicles. Strategies such as pavement management systems use pavement evaluation vehicles that are equipped with state-of-the-art devices to assist road authorities in identifying and repairing these anomalies. The quantity of data available is limited, however, by the limited availability and, therefore, coverage of these vehicles. To address this problem, several investigations have been conducted on the use of smartphones or equipping vehicles with additional sensors to identify the presence of road anomalies. This paper aims to add to this arsenal by using sensors already available in production vehicles to identify road anomalies. If production vehicles could be used to identify road anomalies, then road authorities would be equipped with an additional fleet of mobile sensors (vehicles traveling on a particular road) to receive initial insights into the presence of anomalies. This information could then be used to assist road authorities to deploy their staff and equipment more precisely at these locations, such that appropriate equipment reaches the right place at the right time. In this paper, an algorithm that uses lateral acceleration and individual wheel speed signals, which are commonly available vehicular variables, was developed to detect potholes using machine learning techniques. The results of the algorithm were validated with real life test scenarios.

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