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Autism Spectrum Disorder (ASD) is characterized by social interaction difficulties and communication difficulties. Moreover, children with ASD often suffer from other co-morbidities, such as anxiety and depression. Finding appropriate treatment can be difficult as symptoms of ASD and co-morbidities often overlap. Due to these challenges, parents of children with ASD often suffer from higher levels of stress. This research aims to investigate the feasibility of empowering children with ASD and their parents through the use of a serious game to reduce stress and anxiety and a supporting parent application. The New Horizon game and the SpaceControl application were developed together with therapists and according to guidelines for e-health patient empowerment. The game incorporates two mini-games with relaxation techniques. The performance of the game was analyzed and usability studies with three families were conducted. Parents and children were asked to fill in the Spence's Children Anxiety Scale (SCAS) and Spence Children Anxiety Scale-Parents (SCAS-P) anxiety scale. The game shows potential for stress and anxiety reduction in children with ASD.Sensors 2020, 20, 966 2 of 41 language. ASD is a heterogeneous condition: the profile of each child or adult with ASD is unique [4]. An estimated 1 in 132 individuals suffers from ASD [5]. As a result of these challenges, related to social functioning and communication, people with ASD often suffer from other conditions, such as mood swings and anxiety. It is estimated that up to 72% of children with ASD suffer from co-morbidities [6], however, co-morbidity rates remain largely unknown for ASD, as co-morbid conditions can be difficult to diagnose [7][8][9][10]. Depression and anxiety are two of the most common ASD co-morbidities with rates around 40 to 50% [11].Due to communication difficulties, children with ASD are often unable to express themselves when stressed or frustrated. Combined with unique symptoms in every child and overlap in symptoms of co-morbidities, it is challenging to find the appropriate response. As a result, temper tantrums are common, which can be an important cause of parental stress [1]. Determining the correct diagnosis or finding appropriate treatment is difficult due to the challenges in communication and uniqueness in symptoms [7].Empowering both the children and their parents by raising awareness of the child's anxiety and stress can result in better management of the child's temper tantrums, which can result in a decrease in stress and anxiety. The term "patient empowerment" is used in many different settings and domains, but no general definition exists. The consensus is that patient empowerment is used to describe situations where patients and users are encouraged to take control of their health management [12]. In other words, patient empowerment is enabling patients to actively understand their health [13], but also the support to restore a sense of hope, respect, self-efficacy and the drive to face seemingly insurmountable challenges [14...

BackgroundHeadache disorders are an important health burden, having a large health-economic impact worldwide. Current treatment & follow-up processes are often archaic, creating opportunities for computer-aided and decision support systems to increase their efficiency. Existing systems are mostly completely data-driven, and the underlying models are a black-box, deteriorating interpretability and transparency, which are key factors in order to be deployed in a clinical setting.MethodsIn this paper, a decision support system is proposed, composed of three components: (i) a cross-platform mobile application to capture the required data from patients to formulate a diagnosis, (ii) an automated diagnosis support module that generates an interpretable decision tree, based on data semantically annotated with expert knowledge, in order to support physicians in formulating the correct diagnosis and (iii) a web application such that the physician can efficiently interpret captured data and learned insights by means of visualizations.ResultsWe show that decision tree induction techniques achieve competitive accuracy rates, compared to other black- and white-box techniques, on a publicly available dataset, referred to as migbase. Migbase contains aggregated information of headache attacks from 849 patients. Each sample is labeled with one of three possible primary headache disorders. We demonstrate that we are able to reduce the classification error, statistically significant (ρ≤0.05), with more than 10% by balancing the dataset using prior expert knowledge. Furthermore, we achieve high accuracy rates by using features extracted using the Weisfeiler-Lehman kernel, which is completely unsupervised. This makes it an ideal approach to solve a potential cold start problem.ConclusionDecision trees are the perfect candidate for the automated diagnosis support module. They achieve predictive performances competitive to other techniques on the migbase dataset and are, foremost, completely interpretable. Moreover, the incorporation of prior knowledge increases both predictive performance as well as transparency of the resulting predictive model on the studied dataset.

For elderly people fall incidents are life-changing events that lead to degradation or even loss of autonomy. Current fall detection systems are not integrated and often associated with undetected falls and/or false alarms.In this paper, a social-and context-aware multi-sensor platform is presented, which integrates information gathered by a plethora of fall detection systems and sensors at the home of the elderly, by using a cloud-based solution, making use of an ontology. Within the ontology, both static and dynamic information is captured to model the situation of a specific patient and his/her (in)formal caregivers. This integrated contextual information allows to automatically and continuously assess the fall risk of the elderly, to more accurately detect falls and identify false alarms and to automatically notify the appropriate caregiver, e.g., based on location or their current task.The main advantage of the proposed platform is that multiple fall detection systems and sensors can be integrated, as they can be easily plugged in, this can be done based on the specific needs of the patient. systems and sensors leads to a more reliable system, with better accuracy. The proof of concept was tested with the use of the visualizer, which enables a better way to analyze the data flow within the back-end and with the use of the portable testbed, which is equipped with several different sensors.