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

Children with autism face challenges in various skills (e.g., communication and social) and they exhibit challenging behaviours. These challenging behaviours represent a challenge to their families, therapists, and caregivers, especially during therapy sessions. In this study, we have investigated several machine learning techniques and data modalities acquired using wearable sensors from children with autism during their interactions with social robots and toys in their potential to detect challenging behaviours. Each child wore a wearable device that collected data. Video annotations of the sessions were used to identify the occurrence of challenging behaviours. Extracted time features (i.e., mean, standard deviation, min, and max) in conjunction with four machine learning techniques were considered to detect challenging behaviors. The heart rate variability (HRV) changes have also been investigated in this study. The XGBoost algorithm has achieved the best performance (i.e., an accuracy of 99%). Additionally, physiological features outperformed the kinetic ones, with the heart rate being the main contributing feature in the prediction performance. One HRV parameter (i.e., RMSSD) was found to correlate with the occurrence of challenging behaviours. This work highlights the importance of developing the tools and methods to detect challenging behaviors among children with autism during aided sessions with social robots

AI FOR MELTDOWN DETECTION IN AUTISM USING WEARABLE SENSORS.

Autism spectrum disorder is a neurodevelopmental disorder that is associated with many symptoms, such as impairments in social skills, communication, and abnormal behaviors. Children on the spectrum exhibit atypical, restricted, repetitive, and challenging behaviours. The occurrence of such behaviours poses challenges to caregivers and therapists during therapy sessions. In this study, we investigate the feasibility of integrating wearable sensors and machine learning techniques to detect the occurrence of challenging behaviours among children with autism in real-time. Children wore a wearable device, which collected physiological data in five sessions. The video recordings of the sessions were analyzed to identify the instances of challenging behaviours. Four machine learning techniques were used to leverage various features extracted from the wearable sensors to automatically detect challenging behaviors. The best prediction performance was observed when the XGBoost algorithm was used with all gathered features (i.e., accuracy of 99%). Physiological features were found to be more effective than kinetic ones for the prediction task. Among various physiological features, the heart rate was the main contributing feature in the detection of challenging behaviours. Furthermore, experiments revealed that changes in the HRV parameter (i.e., RMSSD) correlated to the instances of challenging behaviours. The findings of this work motivate research towards methods of early detection of challenging behaviours which enable timely intervention by caregivers and parents

Private Function Evaluation Using Intel's SGX

Private Function Evaluation (PFE) is the problem of evalu- ating one party's private data using a private function owned by another party. Several techniques were presented to tackle PFE by running universal circuits in secure multi-party computation and more recently by hiding the circuit's topology and the gate's functionalities. These solutions however, are not efficient enough for practical use, hence there remains a need for more efficient techniques. This work looks at utilizing the Intel Software Guard Extensions platform (SGX) to provide a more practical solution for PFE while the privacy of the data and the function are both kept protected. Our solution carefully avoids the pitfalls of side channel attacks on SGX. We present solutions for two different scenarios: the first is with the function's owner having SGX enabled and the other is with a third party (other than data owner and function owner) having SGX. Our results show a clear expected advantage in term of time consumption for the first case over the second. Investigating the slowdown in the second case lead to the garbling time which constitutes more than 60% of the consumed time. Both solutions clearly outperform FairplayPF in our tests

Detection of challenging behaviours of children with autism using wearable sensors during interactions with social robots

Autism spectrum disorder is a neurodevelopmental disorder that is characterized by patterns of behaviours and difficulties with social communication and interaction. Children on the spectrum exhibit atypical, restricted, repetitive, and challenging behaviours. In this study, we investigate the feasibility of integrating wearable sensors and machine learning techniques to detect the occurrence of challenging behaviours in real-time. A session of a child with autism interacting with different stimuli groups that included social robots was annotated with observed challenging behaviors. The child wore a wearable device that captured different motion and physiological signals. Different features and machine learning configurations were investigated to identify the most effective combination. Our results showed that physiological signals in addition to typical kinetic measures led to more accurate predictions. The best features and learning model combination achieved an accuracy of 97%. The findings of this work motivate research toward methods of early detection of challenging behaviours, which may enable the timely intervention by caregivers and possibly by social robots

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

Children with autism face challenges in various skills (e.g., communication and social) and they exhibit challenging behaviours. These challenging behaviours represent a challenge to their families, therapists, and caregivers, especially during therapy sessions. In this study, we have investigated several machine learning techniques and data modalities acquired using wearable sensors from children with autism during their interactions with social robots and toys in their potential to detect challenging behaviours. Each child wore a wearable device that collected data. Video annotations of the sessions were used to identify the occurrence of challenging behaviours. Extracted time features (i.e., mean, standard deviation, min, and max) in conjunction with four machine learning techniques were considered to detect challenging behaviors. The heart rate variability (HRV) changes have also been investigated in this study. The XGBoost algorithm has achieved the best performance (i.e., an accuracy of 99%). Additionally, physiological features outperformed the kinetic ones, with the heart rate being the main contributing feature in the prediction performance. One HRV parameter (i.e., RMSSD) was found to correlate with the occurrence of challenging behaviours. This work highlights the importance of developing the tools and methods to detect challenging behaviors among children with autism during aided sessions with social robots