HUMAN ACTIVITY RECOGNITION IN SMART-HOME ENVIRONMENTS FOR HEALTH-CARE APPLICATIONS

With a growing population of elderly people, the number of subjects at risk of cognitive disorders is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care costs and supporting the medical diagnosis. Clinicians are interested in monitoring several behavioral aspects for a wide variety of applications: early diagnosis, emergency monitoring, assessment of cognitive disorders, etcetera. Among the several behavioral aspects of interest, anomalous behaviors while performing activities of daily living (ADLs) are of great importance. Indeed, these anomalies can be indicators of serious cognitive diseases like Mild Cognitive Impairment. The recognition of such abnormal behaviors relies on robust and accurate ADLs recognition systems. Moreover, in order to enable unobtrusive and privacy-aware monitoring, environmental sensors in charge of unobtrusively capturing the interaction of the subject with the home infrastructure should be preferred. This thesis presents several contributions on this topic. The major ones are two novel hybrid ADLs recognition algorithms. The former is supervised while the latter is unsupervised. Preliminary results, which still need to be confirmed, show that the recognition rate of the unsupervised method is comparable to the one obtained by the supervised one, with the great advantage of not requiring the acquisition of an annotated dataset. Beyond ADLs recognition, other contributions on smart sensing and anomaly recognition are presented. Regarding unobtrusive sensing, we propose a machine learning technique to detect fine-grained manipulations performed by the inhabitant on household objects instrumented with tiny accelerometer sensors. Finally, a novel rule-based framework for the recognition of fine-grained abnormal behaviors is presented. Experimental results on several datasets show the effectiveness of all the proposed techniques

Diagnosing faults in autonomous robot plan execution

A major requirement for an autonomous robot is the capability to diagnose faults during plan execution in an uncertain environment. Many diagnostic researches concentrate only on hardware failures within an autonomous robot. Taking a different approach, the implementation of a Telerobot Diagnostic System that addresses, in addition to the hardware failures, failures caused by unexpected event changes in the environment or failures due to plan errors, is described. One feature of the system is the utilization of task-plan knowledge and context information to deduce fault symptoms. This forward deduction provides valuable information on past activities and the current expectations of a robotic event, both of which can guide the plan-execution inference process. The inference process adopts a model-based technique to recreate the plan-execution process and to confirm fault-source hypotheses. This technique allows the system to diagnose multiple faults due to either unexpected plan failures or hardware errors. This research initiates a major effort to investigate relationships between hardware faults and plan errors, relationships which were not addressed in the past. The results of this research will provide a clear understanding of how to generate a better task planner for an autonomous robot and how to recover the robot from faults in a critical environment

Detecting market manipulation in stock market data

Anomaly Detection is an extensively researched problem that has diverse applications in many domains. Anomaly detection is the process of finding data points or patterns that do not conform to expected behavior within a dataset. Solutions to this problem have used techniques from disciplines such as statistics, machine learning, data mining, spectral theory and information theory. In the case of stock market data, the input is a non-linear complex time series that render statistical methods ineffective. The aim of this thesis, is to detect anomalies within the Standard and Poor and Qatar Stock Exchange using the behavior of similar time series. Many works on stock market manipulation focus on supervised learning techniques, which require labeled datasets. The labeling process requires substantial efforts. Anomalous behavior is also dynamic in nature. For those reasons, the development of an unsupervised market manipulation detection technique would be very interesting. The Contextual Anomaly Detector (CAD) is an unsupervised method that finds anomalies by looking at similarly behaving time series and uses them to predict expected values. When the predicted value is different from the actual value in the time series by a certain threshold, it is considered an anomaly. This thesis will look at the Contextual Anomaly Detector (CAD) and implement a different preprocessing step to improve recall and precision

Are language production problems apparent in adults who no longer meet diagnostic criteria for attention-deficit/hyperactivity disorder?

In this study, we examined sentence production in a sample of adults (N = 21) who had had attention-deficit/hyperactivity disorder (ADHD) as children, but as adults no longer met DSM-IV diagnostic criteria (APA, 2000). This “remitted” group was assessed on a sentence production task. On each trial, participants saw two objects and a verb. Their task was to construct a sentence using the objects as arguments of the verb. Results showed more ungrammatical and disfluent utterances with one particular type of verb (i.e., participle). In a second set of analyses, we compared the remitted group to both control participants and a “persistent” group, who had ADHD as children and as adults. Results showed that remitters were more likely to produce ungrammatical utterances and to make repair disfluencies compared to controls, and they patterned more similarly to ADHD participants. Conclusions focus on language output in remitted ADHD, and the role of executive functions in language production

Method and apparatus for monitoring dynamic cardiovascular function using n-dimensional representatives of critical functions

A method, system, apparatus and device for the monitoring, diagnosis and evaluation of the state of a dynamic pulmonary system is disclosed. This method and system provides the processing means for receiving sensed and/or simulated data, converting such data into a displayable object format and displaying such objects in a manner such that the interrelationships between the respective variables can be correlated and identified by a user. This invention provides for the rapid cognitive grasp of the overall state of a pulmonary critical function with respect to a dynamic system

Visual Processing Across Space and Time in Children with Autism Spectrum Disorder

Individuals with autism spectrum disorder (ASD) demonstrate enhanced perceptual abilities relative to typically developing (TD) peers, as evidenced by better detection and identification of visual targets. This enhanced ability to discriminate features has been replicated across spatial and temporal displays. Research also suggests that visual perceptual abilities are correlated with the severity of core autism symptoms in this population, with the exception of atypical sensory behaviors, including sensory seeking and aversion, in which the relationship has been understudied and remains poorly understood. The current study introduces a novel visual search task to assess identification accuracy of feature-based visual targets that concurrently manipulates the temporal and spatial presentation of targets and distractors among children with and without ASD. In the task, target and distractor stimuli were simultaneously presented over visual space on a computer screen, with the peripheral distance of target stimuli from the center of the screen manipulated across trials (close, medium, and far), and the presentation rate manipulated across blocks (39, 117, and 195ms). Results revealed a perceptual advantage in children with ASD when targets were presented close to the center of the display at a presentation rate of 195ms, but not at other rate/distance combinations. Several significant correlations were found between perceptual accuracy and core ASD traits, including atypical visual sensory behaviors. Conclusions are limited by the smaller than expected sample size (due to COVID-19 and abrupt discontinuation of data collection), and data collection will resume when possible to clarify findings. Nonetheless, results provide important insights into the nature of perceptual processing, both in individuals with ASD and TD individuals, in the context of simultaneous spatial and temporal constraints. Clinical implications, limitations, and future directions are discussed

The effect of firm characteristics and good corporate governance characteristics to earning management behaviors

Purpose: This research is carried out to investigate the influence of firm characteristics and good governance characteristics to earnings management behavior. Furthermore, the research is expanded to determine the predictive discretionary accruals models in Indonesia. The author utilizing firm listed in Indonesia Stock Exchange during 2014 – 2018 as research object. Design/methodology/approach: The research samples is selected by utilizing the purposive sampling method. In addition, the data analyze is conducted through E-Views version 10. Three discretionary accruals models is used to define earnings management behavior. The research assumed firm characteristics factors such as financial performance, firm size, leverage, and share issuance activity and good governance characteristics such as board of directors’ size and auditor’s size. Findings: The research discovers that firm characteristics can accentuate the earnings management behavior significantly. In other hand, in good corporate governance characteristics only big four auditor is significant. The research also find that discretionary accruals model of Jones, Dechow, and Kothari are predictive in Indonesia. Practical implications: The discoveries of this research provide understanding for investors that enforcement on both governance and monitoring mechanism are essential approach to reduce earnings management behavior. Originality/value: The research investigated three models of discretionary accruals’ capability in predicting earnings management behavior, and found out all discretionary accruals model are still relevant to be use in predictive to define earnings management behavior in Indonesia.peer-reviewe

Resolving Animal Distress and Pain: Principles and Examples of Good Practice in Various Fields of Research

Pain and distress are central topics in legislation, regulations, and standards regarding the use of animals in research. However, in practice, pain has received greatly increased attention in recent years, while attention to distress has lagged far behind, especially for distress that is not induced by pain. A contributing factor is that there is less information readily available on distress, including practical information on its recognition, assessment and alleviation. This chapter attempts to help fill that void by reversing the usual pattern and giving greater attention to distress than to pain. In addition, we also bypass the pain versus distress dichotomy by adopting a holistic treatment of adverse effects, i.e., not parsing distress and pain, by providing guidance on how to assess deviations from normality through tools such as score sheets. Our aim is to provide practical information to IACUCs, scientists, technicians and animal care personnel. We organize the chapter according to specific research areas and case studies. However, the principles and approaches are readily generalized to other research areas. CONTENTS Effect of surgical technical skill on pain and distress in animals - Alicia Karas, DVM Carbon dioxide euthanasia: example of aversion techniques - Matthew C. Leach, PhD The Refinement of Infectious Disease Research - Karl A. Andrutis DVM, MS, DACLAM Polyclonal antibody production - Kathleen Conlee, BS, MPA Animal models of human psychopathology: anxiety - John P. Gluck PhD Refinement In Toxicology Testing: A Workshop to Promote Current Advances and Disseminate Best Practices - Andrew N. Rowan, Martin L. Stephens, and Kathleen M. Conle