Nuno Gonçalves

Automatic detection of stereotyped hand flapping movements: Two different approaches

Stereotypical motor movements are one of the most common and least understood behaviors occurring in individuals with Autism Spectrum Disorder (ASD). The traditional methods for recording the number of occurrences and duration of stereotypies are insufficient and time consuming. Thus the objective of this study is to automatically detect stereotypical motor movements in real time considering two different approaches. The first approach uses the Microsoft sensor Kinect and gesture recognition algorithms. The second approach uses a trademark device of Texas Instruments with built-in accelerometers and statistical methods to recognize stereotyped movements. The two proposed systems were tested in children with Autism Spectrum Disorders (ASD) and the results are compared. This study provides a valuable tool to monitor stereotypes in order to understand and to cope with this problematic. In the end, it facilitates the identification of relevant behavioral patterns when studying interaction skills in children with ASD.

Detection of stereotyped hand flapping movements in Autistic children using the Kinect sensor: A case study

This paper presents a case study on the use of the Microsoft Kinect sensor and the gesture recognition algorithm, Dynamic Time Warping (DTW to automatically detect stereotyped motor behaviours in children with Autism Spectrum Disorder (ASD). The proposed system was initially tested in a laboratory environment, and after the encouraging results obtained, the system was tested during the sessions with children with ASD in two schools with Special Needs Units. Video analysis was used to validate the results obtained using the Kinect application. This study provides a valuable tool to monitor stereotypes in order to understand and to cope with this problematic. In the end, it facilitates the identification of relevant behavioural patterns when studying interaction skills in children with ASD.

Preliminary study on determining stereotypical motor movements

Stereotypical motor movements are one of the most common and least understood behaviors occurring in individuals with Autism Spectrum Disorder (ASD). To overcome problems with traditional methods for measuring stereotypical motor movements in persons with ASD, the Kinect sensor from Microsoft and gesture recognition algorithms were used to automatically detect hand flapping. This study provides a valuable tool to monitor stereotypes in order to understand and to cope with this problem. At the end it facilitates to identify behavioral patterns especially relevant when studying interaction skills in children with ASD.

Stereotyped movement recognition in children with ASD

Abstract Autism Spectrum Disorders (ASD) manifest in different behaviors, being one of them body rocking, mouthing, and complex hand and finger movements [1] . The traditional methods for recording the number of occurrences and duration of stereotypes are inadequate and time consuming. Therefore, it was used a commercial system with accelerometers sensors that records the movement of wrist. The collected data is sent through a wireless network to the computer, where an application was developed to distinguish the movement made by the children as a stereotyped gesture, hand flapping movement, from a normal gesture. The system was previously tested in laboratory environment and during the intervention sessions. Statistical methods were used to characterize the signal acquired from a previously expressed stereotype recorded from a child with ASD. The parameters that were analyzed are: Root Mean Square (RMS), Standard Variation, Peaks and Valleys. At the end, the proposed methodology facilitates to identify behavioral patterns special relevant when studying interaction skills in children with ASD.

Yarn hairiness determination using image processing techniques

This paper presents a method to automatically determine yarn hairiness using Image Processing (IP) techniques. After image acquisition, the samples are analyzed and processed by a custom-made application developed in Lab VIEW from National Instruments with the IMAQ Vision toolkit. The results show that a reliable quantification of the yarn hairiness index can be obtained. This methodology can be used as an efficient alternative to the traditional commercial hairiness testers suppressing their constraints. Step-by-step algorithms used to isolate the yarn core, to highlight the fibres and to measure the hairiness index are described.

Robótica-Autismo project: Technology for autistic children

This article presents the Robótica-Autismo research project which started in 2009 from a partnership with APPACDM (Portuguese Association of Parents and Friends of Mentally Retarded Citizens). The main objective of the study is to promote the interaction and communication with children with Autism Spectrum Disorder (ASD) by using robots. Different approaches have been followed by the researchers enrolled in this project. Some studies are focused on using Lego robots to teach social and academic skills to children with ASD; other studies are dedicated to the detection of stereotyped movements in children with ASD using two different methods: the Kinect sensor and accelerometers. Recent work is related to the use of a humanoid robot that allows facial expressions to facilitate the emotion recognition and teaching facial expressions.

Studies on the yarn mass parameters determination using Image Processing techniques

This paper presents a study on the development of a methodology to automatically determine yarn mass parameters using Image Processing (IP) techniques. The sample images were analyzed and processed through a custom made application developed in LabVIEW from National Instruments using IMAQ Vision Toolkit. The results show that a reliable diameter, mass and yarn faults determination can be obtained using IP techniques. Step-by-step algorithms used to determine yarn mass parameters are described. The presented approach allows improving products quality as well as the possibility of increasing the efficiency of the textile industry.

Screening a Case Base for Stroke Disease Detection

Stroke stands for one of the most frequent causes of death, without distinguishing age or genders. Despite representing an expressive mortality figure, the disease also causes long-term disabilities with a huge recovery time, which goes in parallel with costs. However, stroke and health diseases may also be prevented considering illness evidence. Therefore, the present work will start with the development of a decision support system to assess stroke risk, centered on a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with a Case Based Reasoning (CBR) approach to computing. Indeed, and in order to target practically the CBR cycle, a normalization and an optimization phases were introduced, and clustering methods were used, then reducing the search space and enhancing the cases retrieval one. On the other hand, and aiming at an improvement of the CBR theoretical basis, the predicates` attributes were normalized to the interval 0…1, and the extensions of the predicates that match the universe of discourse were rewritten, and set not only in terms of an evaluation of its Quality-of-Information (QoI), but also in terms of an assessment of a Degree-of-Confidence (DoC), a measure of oneʼs confidence that they fit into a given interval, taking into account their domains, i.e., each predicate attribute will be given in terms of a pair (QoI, DoC), a simple and elegant way to represent data or knowledge of the type incomplete, self-contradictory, or even unknown.

Illegal access to information systems and the Directive 2013/40/EU

Directive 2013/40/EU of the European Parliament and of the Council of 12 August 2013, on attacks against information systems, came into force on 3 September 2013, replacing Council Framework Decision 2005/222/JHA. It maintains existing offences and criminalizes new activities such as illegal interception and the usage of certain tools for committing offences. The offence that is the focus of this article – illegal access to information systems – is set out in Article 3. It represents a change from the wording of Art 2 (2) of the Framework Decision 2005/222/JHA in that under Art 3 of the Directive the incrimination of illegal access to information systems now depends upon whether such access infringes a security measure. This paper provides an overview of the impetus for the introduction of cybercrime laws and analysis of the key provisions of the Directive before exploring whether the wording of Art 3 is a sensible legislative approach.

Stereotype movement recognition in children with ASD

Abstract The Autism Spectrum Disorders (ASD) covers different events, being one of them body rocking, mouthing, and complex hand and finger movements [1] . The traditional methods for recording the number of occurrences and duration of stereotypes are inadequate and time consuming. Therefore, it was used a commercial system with accelerometers sensors that records the movement of wrist and sends the collected data through a wireless network to the computer. Statistical methods were used to characterize the signal acquired from a previously expressed stereotypy. The parameters that were analyzed are: RMS, Standard Variation, Peaks and Valleys. At the end, the proposed methodology facilitates to identify behavioral patterns special relevant when studying interaction skills in children with ASD.