Automatic visual detection of human behavior: a review from 2000 to 2014

Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.

Ontology-based representation and generation of workflows for micro-task human-machine computation

Doctoral Program in Computer Science

Silica nanoparticles as dexamethasone delivery systems able to induce the osteogenic differentiation of human bone marrow-derived mesenchymal stem cells

Bioactive glasses, especially silica-based materials, are reported to pres- ent osteoconductive and osteoinductive properties, fundamental char- acteristics in bone regeneration [1,2]. Additionally, dexamethasone (Dex) is one of the bioactive agents able to induce the osteogenic differ- entiation of mesenchymal stem cells by increasing the alkaline phos- phatase activity, and the expression levels of Osteocalcin and Bone Sialoprotein [3]. Herein, we synthesised silica (SiO2) nanoparticles (that present inherent bioactivity and ability to act as a sustained drug delivery system), and coated their surface using poly-L-lysine (PLL) and hyaluronic acid (HA) using the layer-by-layer processing technique. Further on, we studied the influence of these new SiO2-polyelectrolyte coated nanoparticles as Dex sustained delivery systems. The SiO2 nanoparticles were loaded with Dex (SiO2-Dex) and coated with PLL and HA (SiO2-Dex-PLL-HA). Their Dex release profile was evaluated and a more sustained release was obtained with the SiO2-Dex-PLL-HA. All the particles were cultured with human bone marrow-derived mes- enchymal stem cells (hBMSCs) under osteogenic differentiation culture conditions. hBMSCs adhered, proliferated and differentiated towards the osteogenic lineage in the presence of SiO2 (DLS 174nm), SiO2-Dex (DLS 175nm) and SiO2-Dex-PLL-HA (DLS 679nm). The presence of these materials induced the overexpression of osteogenic transcripts, namely of Osteocalcin, Bone Sialoprotein and Runx2. Scanning Elec- tron Microscopy/Electron Dispersive Spectroscopy analysis demon- strated that hBMSCs synthesised calcium phosphates when cultured with SiO2-Dex and SiO2-Dex-PLL-HA nanoparticles. These results indi- cate the potential use of these SiO2-polyelectrolytes coated nanoparti- cles as dexamethasone delivery systems capable of promoting osteogenic differentiation of hBMSCs.

The human eye as human-machine interface

Eye tracking as an interface to operate a computer is under research for a while and new systems are still being developed nowadays that provide some encouragement to those bound to illnesses that incapacitates them to use any other form of interaction with a computer. Although using computer vision processing and a camera, these systems are usually based on head mount technology being considered a contact type system. This paper describes the implementation of a human-computer interface based on a fully non-contact eye tracking vision system in order to allow people with tetraplegia to interface with a computer. As an assistive technology, a graphical user interface with special features was developed including a virtual keyboard to allow user communication, fast access to pre-stored phrases and multimedia and even internet browsing. This system was developed with the focus on low cost, user friendly functionality and user independency and autonomy.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

Generic system for human-computer gesture interaction: applications on sign language recognition and robotic soccer refereeing

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for human-computer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of visionbased interaction systems could be the same for all applications and thus facilitate the implementation. For hand posture recognition, a SVM (Support Vector Machine) model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM (Hidden Markov Model) model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications. To validate the proposed framework two applications were implemented. The first one is a real-time system able to interpret the Portuguese Sign Language. The second one is an online system able to help a robotic soccer game referee judge a game in real time.

Generic system for human-computer gesture interaction

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for humancomputer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.

Biomimetic strategies to engineer mineralised human tissues

In the last few years, many reports have been describing promising biocompatible and biodegradable materials that can mimic in a certain extent the multidimensional hierarchical structure of bone, while are also capable of releasing bioactive agents or drugs in a controlled manner. Despite these great advances, new developments in the design and fabrication technologies are required to address the need to engineer suitable biomimetic materials in order tune cells functions, i.e. enhance cell-biomaterial interactions, and promote cell adhesion, proliferation, and differentiation ability. Scaffolds, hydrogels, fibres and composite materials are the most commonly used as biomimetics for bone tissue engineering. Dynamic systems such as bioreactors have also been attracting great deal of attention as it allows developing a wide range of novel in vitro strategies for the homogeneous coating of scaffolds and prosthesis with ceramics, and production of biomimetic constructs, prior its implantation in the body. Herein, it is overviewed the biomimetic strategies for bone tissue engineering, recent developments and future trends. Conventional and more recent processing methodologies are also described.

Development of procedures for the design, optimization and manufacturing of customized orthopaedic and trauma implants: Geometrical/anatomical modelling from 3D medical imaging

Tese de Doutoramento (Programa Doutoral em Engenharia Biomédica)

Development and characterization of lipid-based nanosystems for controlled release of bioactive compounds

Tese de Doutoramento em Engenharia Química e Biológica.

The role of the mammalian DNA end-processing enzyme polynucleotide kinase 3'-phosphatase in spinocerebellar ataxia Type 3 pathogenesis

DNA strand-breaks (SBs) with non-ligatable ends are generated by ionizing radiation, oxidative stress, various chemotherapeutic agents, and also as base excision repair (BER) intermediates. Several neurological diseases have already been identified as being due to a deficiency in DNA end-processing activities. Two common dirty ends, 3'-P and 5'-OH, are processed by mammalian polynucleotide kinase 3'-phosphatase (PNKP), a bifunctional enzyme with 3'-phosphatase and 5'-kinase activities. We have made the unexpected observation that PNKP stably associates with Ataxin-3 (ATXN3), a polyglutamine repeat-containing protein mutated in spinocerebellar ataxia type 3 (SCA3), also known as Machado-Joseph Disease (MJD). This disease is one of the most common dominantly inherited ataxias worldwide; the defect in SCA3 is due to CAG repeat expansion (from the normal 14-41 to 55-82 repeats) in the ATXN3 coding region. However, how the expanded form gains its toxic function is still not clearly understood. Here we report that purified wild-type (WT) ATXN3 stimulates, and by contrast the mutant form specifically inhibits, PNKP's 3' phosphatase activity in vitro. ATXN3-deficient cells also show decreased PNKP activity. Furthermore, transgenic mice conditionally expressing the pathological form of human ATXN3 also showed decreased 3'-phosphatase activity of PNKP, mostly in the deep cerebellar nuclei, one of the most affected regions in MJD patients' brain. Finally, long amplicon quantitative PCR analysis of human MJD patients' brain samples showed a significant accumulation of DNA strand breaks. Our results thus indicate that the accumulation of DNA strand breaks due to functional deficiency of PNKP is etiologically linked to the pathogenesis of SCA3/MJD.

Mother's anxiety and depression during the third pregnancy trimester and neonate's mother versus stranger's face/voice visual preference

Background: Neonates show visual preference for their mother's face/voice and shift their attention from their mother to a stranger's face/voice after habituation. Aim: To assess neonate's mother versus stranger's face/voice visual preference, namely mother's anxiety and depression during the third pregnancy trimester and neonate's: 1) visual preference for the mother versus the stranger's face/voice (pretest visual preference), 2) habituation to the mother's face/voice and 3) visual preference for the stranger versus the mother's face/voice (posttest visual preference). Method: Mothers (N=100) filled out the Edinburgh Postnatal Depression Scale (EPDS) and the State Anxiety Inventory (STAI) both at the third pregnancy trimester and childbirth, and the “preference and habituation to the mother's face/voice versus stranger” paradigm was administered to their newborn 1 to 5 days after childbirth. Results: Neonates of anxious/depressed mothers during the third pregnancy trimester contrarily to neonates of non-anxious/non-depressed mothers did not look 1) longer at their mother's than at the stranger's face/voice at the pretest visual preference (showing no visual preference for the mother), nor 2) longer at the stranger's face/voice in the posttest than in the pretest visual preference (not improving their attention to the stranger's after habituation). Conclusion: Infants exposed to mother's anxiety/depression at the third gestational trimester exhibit less perceptual/social competencies at birth.