Initial response analysis of Robot-Based Intervention Program (RBIP) for children with autism using humanoid robot NAO / Luthffi Idzhar Ismail

In recent decade, robotics has become very significant in assisting the children with autism in the areas like social interaction, emotion recognition, interactive plays, joint attention and special education. Autism is a brain developmental disorder that affects an individual’s social interaction, communication impairments and restricted stereotyped behaviour. Currently, research on the robotics for autism children shows suggestive finding in helping them to improve their quality of lifestyle and adapting themselves to their surroundings. Motivated by these emerging factors, the main objective of this research is to design and propose an interactive robot-based intervention modules using humanoid robot and analyze the initial response of autism children when they are expose to the module in an experimental program called Robot-based Intervention Program (RBIP). The methodology and outcome of this research are outlined in several stages that will be described in details in the Chapter 3. The initial response analysis is being done using modified Behaviour Score Sheet with reference to the Gilliam Autism Rating Scale (GARS) second edition. Based on the experiments that has been conducted in the RBIP and normal classroom, the results shows that 83.3% of the participated children response positively to the interaction in RBIP while another 16.7% response very good in normal classroom interaction for their stereotyped behaviour subscale and communications subscale. On the other hand, only 50% of the participated children with autism response positively in the social interaction subscale in the RBIP while 41.7% response optimistically to the normal classroom interaction while the remaining of 8.3% is not being able to be evaluated since the participants did not cooperate during the interaction in both RBIP and normal classroom experimental setting. Overall, most of the children are positively respond in RBIP which indicate that the robotic intervention program is an effective intervention program for them in improving their impairment in irregular repetitive stereotyped behaviour, communication skills and social interaction skills. Lower score in the three-subscale evaluation of Behaviour Score Sheet during RBIP is indicated that they exhibit less autism characteristic during RBIP compared to the normal classroom interaction

Response pattern of child-robot interaction among children with cognitive impairment

Children with cognitive impairments reported having an engaging initial response in child-robot interaction. However, the interaction modules for child-robot interaction vary and depend on the aim of the studies in the literature. In this study, we designed child-robot interaction modules in order to help children with cognitive impairment in improving their social interaction skills. Hence, we would like to report the overall pattern of their response for each module in 3 sessions of child-robot interaction. Their response pattern for each module is important in future behaviour analysis, especially with regards to their attention skills analysis and their eye contact engagement analysis during child-robot interaction

Face detection technique of Humanoid Robot NAO for application in robotic assistive therapy

This paper proposed a face detection method for tracking the faces of children with Autism Spectrum Disorder in a robotic assistive therapy. The face detection is a novel approach in robotic assistive therapy involving autistic children since it is believe that those children will positively react with high-end devices, gadget and cutting edge devices. The intention of tracking the autistic children's faces is to measure the concentration level of the children in social interaction and communication since everyone knows that those children are suffering from communication disabilities and deficits due to brain developmental disorder. Humanoid Robot Nao with 573.2mm height equipped with 2 internal cameras is utilized for this research. The face detection tools in choregraphe and telepathe based on Graphical User Interface (GUI) module is used in this study. The non-verbal interaction between humanoid robot and autistic children is recorded by using 2 internal cameras from the robot's head. The interaction is going to take about 30 minutes and supervised by occupational therapist and certified psychologist. The autistic children will be introduced to the Humanoid Robot Nao and their reaction will be recorded simultaneously while the robot is trying to track their faces

Humanoid robot NAO: review of control and motion exploration

Humanoids; a most intriguing subject to behold by both the engineers and the world at large. With the introduction of humanoid robot NAO by Aldebaran-Robotics in 2008, a performant biped robot is now available and affordable for research laboratories and the mass market. In this paper, an exploration of current trends in control methods of biped walks, behavior interface tools for motion control for NAO and imminent findings in both research areas are discussed. Future directions are for researchers to devise a unique controller with low power consumption without compromising the robot's speed and robustness

Flight performance analysis of a radio controlled airplane Luthffi Idzhar Ismail

Radio Controlled Airplane which is also known as RC Airplane has become very common and familiar to the community and public. Basically, a Radio Controlled Airplane is a model airplane that is controlled remotely by using a handheld transmitter and a receiver in the airplane. The receiver controls the corresponding servos that move the control surfaces based on the position and configuration of the joysticks on the transmitter which would result the airplane to move and fly. The aim of this research is to obtain the performance characteristics of the RC Airplane model Trainer 40 with the 3047 MWS glow engines. The analysis will be done theoretically as well as experimentally by flying the airplane. Computational Fluid Dynamics simulation is performed to obtain the important parameters such as lift coefficient, drag coefficient and moment coefficient for the airfoil as well as for the whole wing itself. This research focus on the analysis of the range performance, endurance performance, as well as the maximum velocity performance. The theoretical result for the flight maximum velocity is 21.2m/s while the flight range and endurance is 36429.45m and 2040.82s respectively. The actual flight performance obtained from the flight test is slightly lower compared to the theoretical result. The percentage difference of the result for the flight maximum velocity, range and endurance is 12.26%, 8.86% and 12.54% respectively

Real-time detection of ripe oil palm fresh fruit bunch based on YOLOv4

Fresh Fruit Bunch (FFB) is the main ingredient in palm oil production. Harvesting FFB from oil palm trees at its peak ripeness stage is crucial to maximise the oil extraction rate (OER) and quality. In current harvesting practices, misclassification of FFB ripeness can occur due to human error, resulting in OER loss. Therefore, a vision-based ripe FFB detection system is proposed as the first step in a robotic FFB harvesting system. In this work, live camera input is fed into a Convolutional Neural Network (CNN) model known as YOLOv4 to detect the presence of ripe FFBs on the oil palm trees in real-time. Once a ripe FFB is detected on the tree, a signal is transmitted via ROS to the robotic harvesting mechanism. To train the YOLOv4 model, a large number of ripe FFB images were collected using an Intel Realsense Camera D435 with a resolution of 1920× 1080. During data acquisition, a subject matter expert assisted in classifying the FFBs in terms of ripe or unripe. During the testing phase, the result of the mean Average Precision (mAP) and recall are 87.9 % and 82 % as the detection fulfilled the Intersect over Union (IoU) with more than 0.5 after 2000 iterations and the system operated at the real-time speed of roughly 21 Frame Per Second (FPS)

Spatio-Temporal Analysis of Leptospirosis Hotspot Areas and Its Association With Hydroclimatic Factors in Selangor, Malaysia: Protocol for an Ecological Cross-sectional Study

BackgroundLeptospirosis is considered a neglected zoonotic disease in temperate regions but an endemic disease in countries with tropical climates such as South America, Southern Asia, and Southeast Asia. There has been an increase in leptospirosis incidence in Malaysia from 1.45 to 25.94 cases per 100,000 population between 2005 and 2014. With increasing incidence in Selangor, Malaysia, and frequent climate change dynamics, a study on the disease hotspot areas and their association with the hydroclimatic factors would further enhance disease surveillance and public health interventions. ObjectiveThis study aims to examine the association between the spatio-temporal distribution of leptospirosis hotspot areas from 2011 to 2019 with the hydroclimatic factors in Selangor using the geographical information system and remote sensing techniques to develop a leptospirosis hotspot predictive model. MethodsThis will be an ecological cross-sectional study with geographical information system and remote sensing mapping and analysis concerning leptospirosis using secondary data. Leptospirosis cases in Selangor from January 2011 to December 2019 shall be obtained from the Selangor State Health Department. Laboratory-confirmed cases with data on the possible source of infection would be identified and georeferenced according to their longitude and latitudes. Topographic data consisting of subdistrict boundaries and the distribution of rivers in Selangor will be obtained from the Department of Survey and Mapping. The ArcGIS Pro software will be used to evaluate the clustering of the cases and mapped using the Getis-Ord Gi* tool. The satellite images for rainfall and land surface temperature will be acquired from the Giovanni National Aeronautics and Space Administration EarthData website and processed to obtain the average monthly values in millimeters and degrees Celsius. Meanwhile, the average monthly river hydrometric levels will be obtained from the Department of Drainage and Irrigation. Data are then inputted as thematic layers and in the ArcGIS software for further analysis. The artificial neural network analysis in artificial intelligence Phyton software will then be used to obtain the leptospirosis hotspot predictive model. ResultsThis research was funded as of November 2022. Data collection, processing, and analysis commenced in December 2022, and the results of the study are expected to be published by the end of 2024. The leptospirosis distribution and clusters may be significantly associated with the hydroclimatic factors of rainfall, land surface temperature, and the river hydrometric level. ConclusionsThis study will explore the associations of leptospirosis hotspot areas with the hydroclimatic factors in Selangor and subsequently the development of a leptospirosis predictive model. The constructed predictive model could potentially be used to design and enhance public health initiatives for disease prevention. International Registered Report Identifier (IRRID)PRR1-10.2196/4371