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Julianna Mcintyre - One of the best experts on this subject based on the ideXlab platform.

  • team based functional Behavior Assessment as a proactive public school process a descriptive analysis of current barriers
    Journal of Behavioral Education, 2005
    Co-Authors: Terrance M. Scott, Carl J. Liaupsin, Michael C Nelson, Julianna Mcintyre
    Abstract:

    Although functional Behavior Assessment (FBA) has been widely recognized as a promising practice for providing proactive interventions with students exhibiting challenging Behaviors in typical schools, questions persist as to how FBA should best be trained and used in such public settings. Debate has balanced the issue of what is practical for public school personnel and whether FBA can ever reach that level of practicality while maintaining a level of integrity necessary to be a valid technology for Behavior intervention. This paper presents a descriptive analysis of the perceptions and practices of 13 school-based FBA teams that included one or more members who received a 1-day workshop on FBA. Teams were asked to respond to a brief questionnaire regarding their perceptions of the process, what information they found useful, and how that information was used. Results indicate several problem issues and barriers that must be addressed before team-based FBA is widely advocated and practiced in public school settings. Sample team responses and discussion of future directions are included.

  • An Examination of Functional Behavior Assessment in Public School Settings: Collaborative Teams, Experts, and Methodology:
    Behavioral Disorders, 2004
    Co-Authors: Terrance M. Scott, Carl J. Liaupsin, C. Michael Nelson, Julianna Mcintyre, Maureen A. Conroy
    Abstract:

    Recent literature regarding functional Behavior Assessment (FBA) in general education environments has been critical of the paucity of research in such settings, given the complex and often time-co...

Neville M. Blampied - One of the best experts on this subject based on the ideXlab platform.

Terrance M. Scott - One of the best experts on this subject based on the ideXlab platform.

Geoffrey T Fong - One of the best experts on this subject based on the ideXlab platform.

  • defensive verbal Behavior Assessment
    Personality and Social Psychology Bulletin, 2002
    Co-Authors: Lisa Feldman Barrett, Nathan L Williams, Geoffrey T Fong
    Abstract:

    The present investigation introduces a new way to measure the existence of self-protective cognitive strategies: defensive verbal Behavior Assessment (DVBA). In Study 1, the authors introduce the coding procedure for DVBA and demonstrate evidence for its interrater reliability and construct validity. In Study 2, the authors demonstrate that defensive verbal Behavior is influenced both by characteristics of the person and by the situational context. Together, the two studies illustrate that (a) reliable and valid Behavioral Assessment of defensive processes is possible in nonclinical samples without the need for lengthy Assessment times or specialized clinical knowledge and (b) qualities of the person and the situation must be considered to provide a full account of self-protective Behavior.

Gigliola Vaglini - One of the best experts on this subject based on the ideXlab platform.

  • Sleep Behavior Assessment via smartwatch and stigmergic receptive fields
    Personal and Ubiquitous Computing, 2018
    Co-Authors: Antonio L. Alfeo, Filippo Palumbo, Davide La Rosa, Paolo Barsocchi, Mario G C A Cimino, Gigliola Vaglini
    Abstract:

    Sleep Behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep Behavior analysis. Other approaches are based on actigraphy and sleep diary. Although being a good source of information for sleep quality Assessment, sleep diaries can be affected by cognitive bias related to subject’s sleep perception, while actigraphy overestimates sleep periods and night-time disturbance compared to sleep diaries. Machine learning techniques can improve the objectivity and reliability of the observations. However, since signal morphology vary widely between people, conventional machine learning is complex to set up. In this regard, we present an adaptive, reliable, and innovative computational approach to provide per-night Assessment of sleep Behavior to the end-user. We exploit heartbeat rate and wrist acceleration data, gathered via smartwatch, in order to identify subject’s sleep Behavioral pattern. More specifically, heartbeat rate and wrist motion samples are processed via computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Stigmergy associates each sample to a digital pheromone deposit (mark) defined in a mono-dimensional space and characterized by evaporation over time. As a consequence, samples close in terms of time and intensity are aggregated into functional structures called trails. The stigmergic trails allow to compute the similarity between time series on different temporal scales, to support classification or clustering processes. The overall computing schema includes a parametric optimization for adapting the structural parameters to individual sleep dynamics. The outcome is a similarity between sleep nights of the same subject, to generate clusters of nights with different quality levels. Experimental results are shown for three real-world subjects. The resulting similarity is also compared with the dynamic time warping, a popular similarity measure for time series.

  • Sleep Behavior Assessment via smartwatch and stigmergic receptive fields
    Personal and Ubiquitous Computing, 2018
    Co-Authors: Antonio L. Alfeo, Filippo Palumbo, Davide La Rosa, Paolo Barsocchi, Mario G C A Cimino, Gigliola Vaglini
    Abstract:

    Sleep Behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep Behavior analysis. Other approaches are based on actigraphy and sleep diary. Although being a good source of information for sleep quality Assessment, sleep diaries can be affected by cognitive bias related to subject’s sleep perception, while actigraphy overestimates sleep periods and night-time disturbance compared to sleep diaries. Machine learning techniques can improve the objectivity and reliability of the observations. However, since signal morphology vary widely between people, conventional machine learning is complex to set up. In this regard, we present an adaptive, reliable, and innovative computational approach to provide per-night Assessment of sleep Behavior to the end-user. We exploit heartbeat rate and wrist acceleration data, gathered via smartwatch, in order to identify subject’s sleep Behavioral pattern. More specifically, heartbeat rate and wrist motion samples are processed via computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Stigmergy associates each sample to a digital pheromone deposit (mark) defined in a mono-dimensional space and characterized by evaporation over time. As a consequence, samples close in terms of time and intensity are aggregated into functional structures called trails. The stigmergic trails allow to compute the similarity between time series on different temporal scales, to support classification or clustering processes. The overall computing schema includes a parametric optimization for adapting the structural parameters to individual sleep dynamics. The outcome is a similarity between sleep nights of the same subject, to generate clusters of nights with different quality levels. Experimental results are shown for three real-world subjects. The resulting similarity is also compared with the dynamic time warping, a popular similarity measure for time series.