Heart Rate as a Predictor of Challenging Behaviours among Children with Autism from Wearable Sensors in Social Robot Interactions

Alban, Ahmad Qadeib; Alhaddad, Ahmad Yaser; Al‐Ali, Abdulaziz; So, Wing-Chee; Connor, Olcay; Ayesh, Malek; Qidwai, Uvais; Cabibihan, John-John

doi:10.3390/robotics12020055

Cited by 8 publications

(3 citation statements)

References 51 publications

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“…Activity recognition using self-annotated data or sensory readings (e.g., accelerometer) from wearables is useful in healthcare applications [41][42][43]. Being aware of the current physical activity of each participant is key to interpreting changes in blood glucose levels and, especially, the development of hypoglycemia [44,45].…”

Section: Daily Activity Loggingmentioning

confidence: 99%

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions

Alhaddad

Aly

Gad

et al. 2023

Sensors

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Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes.

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Section: Daily Activity Loggingmentioning

confidence: 99%

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions

Alhaddad

Aly

Gad

et al. 2023

Sensors

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“…Por su parte,Alban et al (2023) trabajaron con 10 niños autistas distintas situaciones con estímulos, tanto inanimados, como animados, para predecir los comportamientos mediante redes neuronales convolucionales. Para ello, se utilizaron como variables predictoras los movimientos de las muñecas, la temperatura corporal, la frecuencia cardiaca, el pulso y la actividad electrodérmica.…”

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Análisis mediante inteligencia artificial de las emociones del alumnado autista en la interacción social con el robot NAO

Lorenzo Lledó,

Lorenzo-Lledó,

Rodríguez-Quevedo

2024

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Currently, technology is the most widely used tool in the development of daily life activities. The number of fields of knowledge that benefit from its versatility and application in the development of their activities is increasing. In the educational environment, it allows the generation of activities adapted to the needs of students. In recent years, robotics and artificial intelligence are the most widespread. The characteristics of these tools favour their application with students with autism spectrum disorder. Therefore, the objective of the research is the application of robotics to promote communication and social interaction in students with autism by analysing the emotions they show throughout the different activities. For this purpose, a pilot study was implemented with the NAO robot and four autistic children who developed imitation, game and social interaction activities. An automatic system based on convolutional neural networks was used to detect mood states in the interaction process. The results show that sadness, happiness and anger are the emotions most likely to occur in the participants. Therefore, it is concluded that the robot and the artificial intelligence system are a fundamental element to help express emotions in social interaction. Actualmente, la tecnología es la herramienta más utilizada en el desarrollo de las actividades de la vida diaria. Cada vez es mayor, el número de campos de conocimiento que se benefician de su versatilidad y la aplicación en el desarrollo de sus actividades. En el entorno educativo, permite generar actividades adaptadas a las necesidades del alumnado. En los últimos años, la robótica y la inteligencia artificial son las que mayor difusión están teniendo. Las características de estas herramientas favorecen su aplicación con el alumnado con Trastorno del Espectro Autista. Por tanto, el objetivo de la investigación es la aplicación de la robótica para favorecer la comunicación e interacción social en el alumnado con autismo analizando las emociones que manifiestan a lo largo de las distintas actividades. Para ello, se implementó un estudio piloto con el robot NAO y cuatro niños autistas que desarrollaron actividades de imitación, juego e interacción social. Durante su realización se utilizó un sistema automático basado en redes neuronales convolucionales para detectar los estados de ánimo en el proceso de interacción. Los resultados muestran que tristeza, felicidad y enfado son las emociones que tiene una mayor probabilidad de producirse en los participantes. Por tanto, se concluye que el robot y el sistema de inteligencia artificial son un elemento fundamental para ayudar a expresar sus emociones en las interacciones sociales.

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“…Biofeedback is a technique which is often recommended for those struggling with anxiety, anger, or stress. Some authors have explored various machine learning methods and types of data collected through wearable sensors worn by children diagnosed with ASD [93,94]. Other authors have designed a prototype to reinforce the mental skills of children with ASD through neurofeedback using EEG data and a small humanoid robot to stimulate attention towards a joint activity [95].…”

mentioning

confidence: 99%

Exploring the Potential of Social Robots for Speech and Language Therapy: A Review and Analysis of Interactive Scenarios

et al. 2023

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The use of innovative technology in the field of Speech and Language Therapy (SLT) has gained significant attention nowadays. Despite being a promising research area, Socially Assistive Robots (SARs) have not been thoroughly studied and used in SLT. This paper makes two main contributions: firstly, providing a comprehensive review of existing research on the use of SARs to enhance communication skills in children and adolescents. Secondly, organizing the information into tables that categorize the interactive play scenarios described in the surveyed papers. The inclusion criteria for play scenarios in the tables are based only on their effectiveness for SLT proven by experimental findings. The data, systematically presented in a table format, allow readers to easily find relevant information based on various factors, such as disorder type, age, treatment technique, robot type, etc. The study concludes that the despite limited research on the use of social robots for children and adolescents with communication disorders (CD), promising outcomes have been reported. The authors discuss the methodological, technical, and ethical limitations related to the use of SARs for SLT in clinical or home environments, as well as the huge potential of conversational Artificial Intelligence (AI) as a secondary assistive technology to facilitate speech and language interventions.

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Heart Rate as a Predictor of Challenging Behaviours among Children with Autism from Wearable Sensors in Social Robot Interactions

Cited by 8 publications

References 51 publications

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions

Análisis mediante inteligencia artificial de las emociones del alumnado autista en la interacción social con el robot NAO

Exploring the Potential of Social Robots for Speech and Language Therapy: A Review and Analysis of Interactive Scenarios

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