Machine Learning and Virtual Reality on Body Movements¿ Behaviors to Classify Children with Autism Spectrum Disorder

[EN] Autism spectrum disorder (ASD) is mostly diagnosed according to behavioral symptoms in sensory, social, and motor domains. Improper motor functioning, during diagnosis, involves the qualitative evaluation of stereotyped and repetitive behaviors, while quantitative methods that classify body movements' frequencies of children with ASD are less addressed. Recent advances in neuroscience, technology, and data analysis techniques are improving the quantitative and ecological validity methods to measure specific functioning in ASD children. On one side, cutting-edge technologies, such as cameras, sensors, and virtual reality can accurately detect and classify behavioral biomarkers, as body movements in real-life simulations. On the other, machine-learning techniques are showing the potential for identifying and classifying patients' subgroups. Starting from these premises, three real-simulated imitation tasks have been implemented in a virtual reality system whose aim is to investigate if machine-learning methods on movement features and frequency could be useful in discriminating ASD children from children with typical neurodevelopment. In this experiment, 24 children with ASD and 25 children with typical neurodevelopment participated in a multimodal virtual reality experience, and changes in their body movements were tracked by a depth sensor camera during the presentation of visual, auditive, and olfactive stimuli. The main results showed that ASD children presented larger body movements than TD children, and that head, trunk, and feet represent the maximum classification with an accuracy of 82.98%. Regarding stimuli, visual condition showed the highest accuracy (89.36%), followed by the visual-auditive stimuli (74.47%), and visual-auditive-olfactory stimuli (70.21%). Finally, the head showed the most consistent performance along with the stimuli, from 80.85% in visual to 89.36% in visual-auditive-olfactory condition. The findings showed the feasibility of applying machine learning and virtual reality to identify body movements' biomarkers that could contribute to improving ASD diagnosis.This work was supported by the Spanish Ministry of Economy, Industry, and Competitiveness funded project "Immersive virtual environment for the evaluation and training of children with autism spectrum disorder: T Room" (IDI-20170912) and by the Generalitat Valenciana funded project REBRAND (PROMETEO/2019/105). Furthermore, this work was co-founded by the European Union through the Operational Program of the European Regional development Fund (ERDF) of the Valencian Community 2014-2020 (IDIFEDER/2018/029).Alcañiz Raya, ML.; Marín-Morales, J.; Minissi, ME.; Teruel Garcia, G.; Abad, L.; Chicchi-Giglioli, IA. (2020). 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Activity recognition based on thermopile imaging array sensors

With aging population, the importance of caring for elderly people is getting more and more attention. In this paper, a low resolution thermopile array sensor is used to develop an activity recognition system for elderly people. The sensor is composed of a 32x32 thermopile array with the corresponding 33° × 33° field of view. The outputs of the sensor are sequential images in which each pixel contains a temperature value. According to the thermopile images, the activity recognition system first determines whether the target is within the tracking area; if the target is within the tracking area, the location of the target will be detected and three kinds of activities will be identified. Keywords- Activity Recognition, Raspberry Pi, Thermopile, Imaging Processing

Familiarizing children with atificial intelligence

Abstract. Studies regarding the digital literacy of children can be found easily. Such as teaching children about coding, involvement of children in the design and development of technology, learning of CT, and abstraction. On the other hand, the availability of literature regarding the combination of children and AI is still not enough. Especially, there is a lack of research regarding AI literacy of children which is the research problem. The gap was found while searching for material regarding AI and children through ACM Digital Library and IEEE Xplore which motivated to conduct this research. Thus, the research was conducted with the aim of familiarizing children with the AI. Moreover, the qualitative research method was used for this study. The reason to choose this method was the lack of literature in this field. Another reason was to obtain evidence-based on observations in the real environment. Data was collected in the form of observations, texts (activity worksheets), pictures, video, and audio. The teacher was interviewed at the end of the last session to get feedback about children’s learning. Also, the study was conducted at an international school in Oulu, Finland. Sessions were conducted on 19 Nov and 26 Nov 2019. Each session was of approximately 45 minutes. Children belonging to the age-group of 11–12 years were included. To introduce AI to the children existing material with modification was used. During the sessions, children had some hands-on activities such as an online ML activity. Some activity worksheets were also distributed among them. Children were asked about AI before and after this concept was explained to them. Findings of the study suggested that some children’s opinion about AI was changed after they were being engaged in learning activities. In the beginning, upon asking them about AI a few children answered as coding or robot whereas repeating the same question at the end some students mentioned “thinking by itself”. In contrast, some students still mentioned robot or computer. Observations also suggest that children seemed to learn more easily through hands-on activities and by listening to stories. Based on the results of this study, it seems that more sessions with careful planning are needed to get better results in the future. One limitation is, the results of this study cannot be applied to a large group of children. Another limitation of this study is the unknown background of participants

Robust Signal Processing Techniques for Wearable Inertial Measurement Unit (IMU) Sensors

Activity and gesture recognition using wearable motion sensors, also known as inertial measurement units (IMUs), provides important context for many ubiquitous sensing applications including healthcare monitoring, human computer interface and context-aware smart homes and offices. Such systems are gaining popularity due to their minimal cost and ability to provide sensing functionality at any time and place. However, several factors can affect the system performance such as sensor location and orientation displacement, activity and gesture inconsistency, movement speed variation and lack of tiny motion information. This research is focused on developing signal processing solutions to ensure the system robustness with respect to these factors. Firstly, for existing systems which have already been designed to work with certain sensor orientation/location, this research proposes opportunistic calibration algorithms leveraging camera information from the environment to ensure the system performs correctly despite location or orientation displacement of the sensors. The calibration algorithms do not require extra effort from the users and the calibration is done seamlessly when the users present in front of an environmental camera and perform arbitrary movements. Secondly, an orientation independent and speed independent approach is proposed and studied by exploring a novel orientation independent feature set and by intelligently selecting only the relevant and consistent portions of various activities and gestures. Thirdly, in order to address the challenge that the IMU is not able capture tiny motion which is important to some applications, a sensor fusion framework is proposed to fuse the complementary sensor modality in order to enhance the system performance and robustness. For example, American Sign Language has a large vocabulary of signs and a recognition system solely based on IMU sensors would not perform very well. In order to demonstrate the feasibility of sensor fusion techniques, a robust real-time American Sign Language recognition approach is developed using wrist worn IMU and surface electromyography (EMG) sensors

Robust Signal Processing Techniques for Wearable Inertial Measurement Unit (IMU) Sensors

Activity and gesture recognition using wearable motion sensors, also known as inertial measurement units (IMUs), provides important context for many ubiquitous sensing applications including healthcare monitoring, human computer interface and context-aware smart homes and offices. Such systems are gaining popularity due to their minimal cost and ability to provide sensing functionality at any time and place. However, several factors can affect the system performance such as sensor location and orientation displacement, activity and gesture inconsistency, movement speed variation and lack of tiny motion information. This research is focused on developing signal processing solutions to ensure the system robustness with respect to these factors. Firstly, for existing systems which have already been designed to work with certain sensor orientation/location, this research proposes opportunistic calibration algorithms leveraging camera information from the environment to ensure the system performs correctly despite location or orientation displacement of the sensors. The calibration algorithms do not require extra effort from the users and the calibration is done seamlessly when the users present in front of an environmental camera and perform arbitrary movements. Secondly, an orientation independent and speed independent approach is proposed and studied by exploring a novel orientation independent feature set and by intelligently selecting only the relevant and consistent portions of various activities and gestures. Thirdly, in order to address the challenge that the IMU is not able capture tiny motion which is important to some applications, a sensor fusion framework is proposed to fuse the complementary sensor modality in order to enhance the system performance and robustness. For example, American Sign Language has a large vocabulary of signs and a recognition system solely based on IMU sensors would not perform very well. In order to demonstrate the feasibility of sensor fusion techniques, a robust real-time American Sign Language recognition approach is developed using wrist worn IMU and surface electromyography (EMG) sensors