A probabilistic approach to learn activities of daily living of a mobility aid device user

© 2014 IEEE. The problem of inferring human behaviour is naturally complex: people interact with the environment and each other in many different ways, and dealing with the often incomplete and uncertain sensed data by which the actions are perceived only compounds the difficulty of the problem. In this paper, we propose a framework whereby these elaborate behaviours can be naturally simplified by decomposing them into smaller activities, whose temporal dependencies can be more efficiently represented via probabilistic hierarchical learning models. In this regard, patterns of a number of activities typically carried out by users of an ambulatory aid device have been identified with the aid of a Hierarchical Hidden Markov Model (HHMM) framework. By decomposing the complex behaviours into multiple layers of abstraction the approach is shown capable of modelling and learning these tightly coupled human-machine interactions. The inference accuracy of the proposed model is proven to compare favourably against more traditional discriminative models, as well as other compatible generative strategies to provide a complete picture that highlights the benefits of the proposed approach, and opens the door to more intelligent assistance with a robotic mobility aid

Using movement kinematics to understand the motor side of Autism Spectrum Disorder

openComprensione del sintomo motorio dell'autismo attraverso la cinematica del movimentoBeside core deficits in social interaction and communication, atypical motor patterns have been often reported in people with Autism Spectrum Disorder (ASD). It has been recently speculated that a part of these sensorimotor abnormalities could be better explained considering prospective motor control (i.e., the ability to plan actions toward future events or consider future task demands), which has been hypothesized to be crucial for higher mind functions (e.g., understand intentions of other people) (Trevarthen and Delafield-Butt 2013). The aim of the current dissertation was to tackle the motor ‘side’ in ASD exploring whether and how prospective motor control might be atypical in children with a diagnosis of autism, given that actions are directed into the future and their control is based on knowledge of what is going to happen next (von Hofsten and Rosander 2012). To do this, an integrative approach based on neuropsychological assessment, behavioural paradigms and machine learning modelling of the kinematics recorded with motion capture techniques was applied in typically developing children and children with ASD without accompanying intellectual impairment.openXXXI CICLO - ARCHITETTURA E DESIGN - Design navale e nauticoBECCHIO, CRISTINA (IIT)Podda, Jessic

The Neurogenic Hypothesis of RSI

The upsurge in cases of a syndrome known as repetition strain injury (RSI) or occupational overuse syndrome COOS) in the 1980s highlighted lack of precision in the medical diagnosis of work-related neck and upper limb disorders. 80,81 Exemplifying the prevailing ignorance, Ferguson 37 stated that 'the majority of cases of repetition strain injury are not localised syndromes, but of a more diffuse disorder, apparently of muscles ... and ... little is known of its aetiology, pathogenesis and pathology ... nor, if when established, why it appears to persist despite prolonged rest of the patient.

On Leveraging Statistical and Relational Information for the Representation and Recognition of Complex Human Activities

Machine activity recognition aims to automatically predict human activities from a series of sensor signals. It is a key aspect to several emerging applications, especially in the pervasive computing field. However, this problem faces several challenges due to the complex, relational and ambiguous nature of human activities. These challenges still defy the majority of traditional pattern recognition approaches, whether they are knowledge-based or data-driven. Concretely, the current approaches to activity recognition in sensor environments fall short to represent, reason or learn under uncertainty, complex relational structure, rich temporal context and abundant common-sense knowledge. Motivated by these shortcomings, our work focuses on the combination of both data-driven and knowledge-based paradigms in order to address this problem. In particular, we propose two logic-based statistical relational activity recognition frameworks which we describe in two different parts. The first part presents a Markov logic-based framework addressing the recognition of complex human activities under realistic settings. Markov logic is a highly flexible statistical relational formalism combining the power of first-order logic with Markov networks by attaching real-valued weights to formulas in first-order logic. Thus, it unites both symbolic and probabilistic reasoning and allows to model the complex relational structure as well as the inherent uncertainty underlying human activities and sensor data. We focus on addressing the challenge of recognizing interleaved and concurrent activities while preserving the intuitiveness and flexibility of the modelling task. Using three different models we evaluate and prove the viability of using Markov logic networks for that problem statement. We also demonstrate the crucial impact of domain knowledge on the recognition outcome. Implementing an exhaustive model including heterogeneous information sources comes, however, at considerable knowledge engineering efforts. Hence, employing a standard, widely used formalism can alleviate that by enhancing the portability, the re-usability and the extension of the model. In the second part of this document, we apply a hybrid approach that goes one step further than Markov logic network towards a formal, yet intuitive conceptualization of the domain of discourse. Concretely, we propose an activity recognition framework based on log-linear description logic, a probabilistic variant of description logics. Log-linear description logic leverages the principles of Markov logic while allowing for a formal conceptualization of the domain of discourse, backed up with powerful reasoning and consistency check tools. Based on principles from the activity theory, we focus on addressing the challenge of representing and recognizing human activities at three levels of granularity: operations, actions and activities. Complying with real-life scenarios, we assess and discuss the viability of the proposed framework. In particular, we show the positive impact of augmenting the proposed multi-level activity ontology with weights compared to using its conventional weight-free variant

Towards gestural understanding for intelligent robots

Fritsch JN. Towards gestural understanding for intelligent robots. Bielefeld: Universität Bielefeld; 2012.A strong driving force of scientific progress in the technical sciences is the quest for systems that assist humans in their daily life and make their life easier and more enjoyable. Nowadays smartphones are probably the most typical instances of such systems. Another class of systems that is getting increasing attention are intelligent robots. Instead of offering a smartphone touch screen to select actions, these systems are intended to offer a more natural human-machine interface to their users. Out of the large range of actions performed by humans, gestures performed with the hands play a very important role especially when humans interact with their direct surrounding like, e.g., pointing to an object or manipulating it. Consequently, a robot has to understand such gestures to offer an intuitive interface. Gestural understanding is, therefore, a key capability on the way to intelligent robots. This book deals with vision-based approaches for gestural understanding. Over the past two decades, this has been an intensive field of research which has resulted in a variety of algorithms to analyze human hand motions. Following a categorization of different gesture types and a review of other sensing techniques, the design of vision systems that achieve hand gesture understanding for intelligent robots is analyzed. For each of the individual algorithmic steps – hand detection, hand tracking, and trajectory-based gesture recognition – a separate Chapter introduces common techniques and algorithms and provides example methods. The resulting recognition algorithms are considering gestures in isolation and are often not sufficient for interacting with a robot who can only understand such gestures when incorporating the context like, e.g., what object was pointed at or manipulated. Going beyond a purely trajectory-based gesture recognition by incorporating context is an important prerequisite to achieve gesture understanding and is addressed explicitly in a separate Chapter of this book. Two types of context, user-provided context and situational context, are reviewed and existing approaches to incorporate context for gestural understanding are reviewed. Example approaches for both context types provide a deeper algorithmic insight into this field of research. An overview of recent robots capable of gesture recognition and understanding summarizes the currently realized human-robot interaction quality. The approaches for gesture understanding covered in this book are manually designed while humans learn to recognize gestures automatically during growing up. Promising research targeted at analyzing developmental learning in children in order to mimic this capability in technical systems is highlighted in the last Chapter completing this book as this research direction may be highly influential for creating future gesture understanding systems

Learning from Teacher's Eye Movement: Expertise, Subject Matter and Video Modeling

How teachers' eye movements can be used to understand and improve education is the central focus of the present paper. Three empirical studies were carried out to understand the nature of teachers' eye movements in natural settings and how they might be used to promote learning. The studies explored 1) the relationship between teacher expertise and eye movement in the course of teaching, 2) how individual differences and the demands of different subjects affect teachers' eye movement during literacy and mathematics instruction, 3) whether including an expert's eye movement and hand information in instructional videos can promote learning. Each study looked at the nature and use of teacher eye movements from a different angle but collectively converge on contributions to answering the question: what can we learn from teachers' eye movements? The paper also contains an independent methodology chapter dedicated to reviewing and comparing methods of representing eye movements in order to determine a suitable statistical procedure for representing the richness of current and similar eye tracking data. Results show that there are considerable differences between expert and novice teachers' eye movement in a real teaching situation, replicating similar patterns revealed by past studies on expertise and gaze behavior in athletics and other fields. This paper also identified the mix of person-specific and subject-specific eye movement patterns that occur when the same teacher teaches different topics to the same children. The final study reports evidence that eye movement can be useful in teaching; by showing increased learning when learners saw an expert model's eye movement in a video modeling example. The implications of these studies regarding teacher education and instruction are discussed.PHDEducation & PsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145853/1/yizhenh_1.pd

Choice responding in infants and preschoolers : the effects of child control over stimulus presentation

Studies of operant conditioning with infants have suggested that control over environmental events is reinforcing. Interpretations of the pleasure derived from controlling stimuli have been largely based upon observation and anecdotal reports of increased attention and positive affect (e.g., smiling, cooing) under conditions of infant-controlled stimulation and observations of negative affect (e.g., fussing, crying) when control is taken away. The purpose of the present study was to empirically validate whether infants and young children do, in fact, prefer contingent over noncontingent stimulation. To accomplish this, children aged 12 to 51 months were provided with a series of opportunities to choose between contingent and noncontingent visual stimuli. The stimuli consisted of a series of slides of colorful cartoon and storybook characters projected onto plexiglass panels. Choice between the two schedules was used as a measure of preference. Rates of responding (i.e., panel pressing) to the tvo schedules following each choice were also analyzed

Visual literacy for the 1990\u27s

Visual aesthetics -- Whole-brain thinking -- Pictorial imaging -- Art and science, mathematics, social studies, and language arts

Vortex of the Web. Potentials of the online environment

This volume compiles international contributions that explore the potential risks and chances coming along with the wide-scale migration of society into digital space. Suggesting a shift of paradigm from Spiral of Silence to Nexus of Noise, the opening chapter provides an overview on systematic approaches and mechanisms of manipulation – ranging from populist political players to Cambridge Analytica. After a discussion of the the juxtaposition effects of social media use on social environments, the efficient instrumentalization of Twitter by Turkish politicans in the course of the US-decision to recognize Jerusalem as Israel’s capital is being analyzed. Following a case study of Instagram, Black Lives Matter and racism is a research about the impact of online pornography on the academic performance of university students. Another chapter is pointing out the potential of online tools for the successful relaunch of shadow brands. The closing section of the book deals with the role of social media on the opinion formation about the Euromaidan movement during the Ukrainian revolution and offers a comparative study touching on Russian and Western depictions of political documentaries in the 2000s