A Depth Video-based Human Detection and Activity Recognition using Multi-features and Embedded Hidden Markov Models for Health Care Monitoring Systems

Increase in number of elderly people who are living independently needs especial care in the form of healthcare monitoring systems. Recent advancements in depth video technologies have made human activity recognition (HAR) realizable for elderly healthcare applications. In this paper, a depth video-based novel method for HAR is presented using robust multi-features and embedded Hidden Markov Models (HMMs) to recognize daily life activities of elderly people living alone in indoor environment such as smart homes. In the proposed HAR framework, initially, depth maps are analyzed by temporal motion identification method to segment human silhouettes from noisy background and compute depth silhouette area for each activity to track human movements in a scene. Several representative features, including invariant, multi-view differentiation and spatiotemporal body joints features were fused together to explore gradient orientation change, intensity differentiation, temporal variation and local motion of specific body parts. Then, these features are processed by the dynamics of their respective class and learned, modeled, trained and recognized with specific embedded HMM having active feature values. Furthermore, we construct a new online human activity dataset by a depth sensor to evaluate the proposed features. Our experiments on three depth datasets demonstrated that the proposed multi-features are efficient and robust over the state of the art features for human action and activity recognition

Human-Tool-Interaction-Based Action Recognition Framework for Automatic Construction Operation Monitoring

Monitoring activities on a construction jobsite is one of the most important tasks that a construction management team performs every day. Construction management teams monitor activities to ensure that a construction project progresses as scheduled and that the construction crew works properly in a safe working environment. However, site monitoring is often time-consuming. Various automated or semi-automated tracking approaches such as radio frequency identification, Global Positioning System, ultrawide band, barcode, and laser scanning have been introduced to better monitor activities on the construction site. However, deploying and maintaining such techniques require a high level of involvement by very specific well-trained professionals and could be costly. As an alternative way to monitor sites, object recognition and tracking have the advantage of requiring low human involvement and intervention. However, it is still a challenge to recognize construction crew activities with existing methods, which have a high false recognition rate. This research proposes a new approach for recognizing construction personnel activity from still images or video frames. The new approach mimics the human thinking process with the assumption that a construction worker performs a certain activity with a specific body pose using a specific tool. The new approach consists of two recognition tasks, construction worker pose recognition and tool recognition. The two recognition tasks are connected in sequence with an interactive spatial relationship. The proposed method was developed into a computer application using Matlab. It was compared against a benchmark method that only uses construction worker body pose for activity recognition. The benchmark method was also developed into a computer application with Matlab. The proposed method and the benchmark method were tested with the same sample set containing 500 images of over 10 different construction activities. The experimental results show that the proposed framework achieved a higher reliability (precision value), a lower sensitivity (recall value), and an overall better performance (F₁ score) than the benchmark method

Understanding Equitable Assessment: How Preservice Teachers Make Meaning of DisAbility

Disproportionality of historically marginalized populations in special education continues to be a critical concern. The identification of students with disabilities is reliant on valid and reliable assessment that is free of bias. The extent to which this is possible given measurement constraints and an increasingly diverse student population is unclear. How teachers are trained to design, select, administer, score, and interpret assessment data related to the identification of students with disabilities is vastly under-researched considering the significant implications of assessment practices. In this study, six special education preservice teachers engaged in an assessment methods course during their second semester of an initial certification program. This study focuses on shifts in preservice teacher understanding and the associated learning experiences in the course. Findings from this study have the potential to inform general and special education teacher preparation coursework

Developing a framework to identify and manage the impacts of human factors on the main activities of an agile supply chain

Supply chains are increasingly challenged to satisfy changeable market requirements whilst shortening lead times; leading them to embrace agile philosophy in the pursuit of greater flexibility and responsiveness. Although these agile supply chains have focused predominantly on automation, they still rely greatly on their employees to make strategic decisions at every stage of the supply chain. Therefore, it is of great importance to recognise the impacts of these human factors along the supply chain and how to best minimise them. The purpose of this dissertation was to develop a framework to identify the human factors affecting the main activities of an agile supply chain. To do so, a thorough and extensive literature review was undertaken, divided into three parts that studied first, the agile supply chains, then human resource management practices and lastly, the human factors affecting agility in these supply chains. From the analysis of this comprehensive literature review, the primary findings were the identification of the human factors that form the agile culture. These were said to be the foundation of the process of becoming agile. These so called ‘agile culture human factors’ were recognised to affect all the activities along the agile supply chain; furthermore, specific human factors affecting each single activity were also identified. As a result of this, the framework was constructed, graphically depicting these findings. Additionally, a list of suggestions was provided for better managing the human factors impacting only the planning stage; the activity most affected by human factors as illustrated in the framework. The results obtained here are of great relevance for organisations in their transition towards achieving agility. The framework developed is a useful tool that guides managers in the identification and control of human impacts when attaining agility. Moreover, as the framework identifies the factors impacting each activity of the supply chain individually, it can also prove useful to assist line managers, especially in the planning stage. However, further studies should be conducted to test the framework in real life scenarios in order to verify its validity. Keywords: human factors, agile supply chains, agile culture, human resource practices

A Framework to Manage the Complex Organisation of Collaborating: Its Application to Autonomous Systems

In this paper we present an analysis of the complexities of large group collaboration and its application to develop detailed requirements for collaboration schema for Autonomous Systems (AS). These requirements flow from our development of a framework for collaboration that provides a basis for designing, supporting and managing complex collaborative systems that can be applied and tested in various real world settings. We present the concepts of "collaborative flow" and "working as one" as descriptive expressions of what good collaborative teamwork can be in such scenarios. The paper considers the application of the framework within different scenarios and discuses the utility of the framework in modelling and supporting collaboration in complex organisational structures

A Framework for Assessing the Rationality of Judgments in Carcinogenicity Hazard Identification

Arguing that guidelines for identifying carcinogens now lack a philosophically rigorous framework, the authors present an alternative that draws clear attention to the process of reasoning towards judgments of carcinogenicity

Interpretable Machine Learning for Privacy-Preserving Pervasive Systems

Our everyday interactions with pervasive systems generate traces that capture various aspects of human behavior and enable machine learning algorithms to extract latent information about users. In this paper, we propose a machine learning interpretability framework that enables users to understand how these generated traces violate their privacy