Special Issue on Wearable Computing and Machine Learning for Applications in Sports, Health, and Medical Engineering

Note: In lieu of an abstract, this is an excerpt from the first page. Recent advancement in digital technologies is driving a remarkable transformation in sports, health, and medical engineering, aiming to achieve the accurate quantification of performance, well-being, and disease condition, and the optimization of sports, clinical, and therapeutic training and treatment programs. Traditionally, understanding and monitoring of functional performance and capacity has been performed in gait laboratories based on optoelectronic motion capture systems. However, gait laboratories in practical settings are often not readily available because the systems are costly and require trained experts to operate. Most importantly, when assessments are restricted to laboratory settings, they provide a narrow snapshot of function and do not capture functionality in natural free-living settings, thus representing a severely under-sampled view of an individual’s condition. The use of mobile and wearable technologies has been explored in many sports, health, and medical research studies examining individuals in “in-the-wild” settings. Among the most important drivers of this transformation are (1) wearable sensors and (2) signal processing and machine learning algorithms. Wearable sensors are capable of collecting physical and/or physiological data continuously and seamlessly outside of laboratory settings. Signal processing and machine learning algorithms allow data-driven approaches for analyzing considerable amounts of multidimensional sensory data and for extracting important information relevant to the mentioned application areas (e.g., validating the efficacy of sports training, health benefits, and chronic disease progression). These technologies together would support how sports and clinical professionals understand and interpret individuals’ performance more objectively, and enable proactive, evidence-based, and personalized management systems

Learning Leaders: a multi-method evaluation, final report

This report investigates findings arising from a variety of forms of feedback on Cumbria Partnership Foundation Trust’s “Learning Leaders” Programme (henceforth LLP) running from 2012-2013

The role and effectiveness of e-learning: key issues in an industrial context

This paper identifies the current role and effectiveness of e-learning and its key issues in an industrial context. The first objective is to identify the role of e-learning, particularly in staff training and executive education, where e-learning (online, computer-based or videoconferencing learning) has made significant impacts and contributions to several organisations such as the Royal Bank of Scotland, Cisco and Cap Gemini Earnst Young. With e-learning, staff training and executive education provides more benefits and better efficiency than traditional means. The second objective of this research is to understand the effectiveness of e-learning. This can be classified into two key issues: (1) methods of e-learning implementations; and (2) factors influencing effective and ineffective e-learning implementations. One learning point from (1) is that centralized e-learning implementations may prevail for big organizations. How-ever, more organizations adopt decentralized e-learning implementations due to various reasons, which will be discussed in this paper. From the research results, a proposed way is to retain the decentralized way. The second learning point is about interactive learning (IL), the combination of both e-learning and face-to-face learning. IL has been making contributions to several organizations, including the increase in motivation, learning interests and also efficiency. The popular issues about IL are (a) how to minimize the disadvantages of IL and (b) the degree of interactivity for maximizing learning efficiency. One learning point from (2) is to analyze the factors influencing effective and ineffective implementations, which reflect the different focuses between industrialists and academics. In terms of effective e-learning implementations, factors identified by both groups can map to particular cases in industry. In contrast, factors causing ineffective implementations rely more on primary source data. In order to find out these factors and analyze the rationale behind, case studies and interviews were used as research methodology that matched the objective of the research

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption e ciency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence di ers from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-

Joint Learning Update

Over the past three years, the Joint Learning Network for Universal Health Coverage (JLN) has become a well-established practitioner-to-practitioner network of countries at the forefront of the global movement toward universal health coverage (UHC) . Now that the JLN has been active for a few years, many involved in the JLN felt that it was an appropriate time to assess what has been achieved to date and develop a roadmap for the future.In December 2012, the Rockefeller Foundation engaged Pact, an independent NGO that specializes in community engagement and networks, to conduct an independent strategic review of the JLN's value proposition, mechanisms for engaging members, and decision-making structures. Pact sampled each stakeholder group in the JLN by administering an online member survey and conducting a series of in-person interviews to gather perspectives from across the community and gain a deeper understanding of how each group contributes to the JLN's goals.One hundred and four JLN members -- a 45 percent response rate -- responded to the member survey and 27 stakeholders participated in semi-structured interviews. The results provided useful information about JLN members and how they are applying knowledge acquired through network activities in their own context

The efficacy of using data mining techniques in predicting academic performance of architecture students.

In recent years, there has been a tremendous increase in the number of applicants seeking placement in the undergraduate architecture programme. It is important to identify new intakes who possess the capability to succeed during the selection phase of admission at universities. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during selection process. The present study investigates the efficacy of using data mining techniques to predict academic performance of architecture student based on information contained in prior academic achievement. The input variables, i.e. prior academic achievement, were extracted from students' academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data was divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models. The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement are good predictors of academic performance of architecture students. Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students. The developed SVM model can be used a decision-making tool for selecting new intakes into the architecture program at Nigerian universities

SAP-Related Education - Status-Quo and Experience

Integrating Enterprise Systems solutions in the curriculum of not only universities but all types of institutes of higher learning has been a major challenge for nearly ten years. Enterprise Systems education is surprisingly well documented in a number of papers on Information Systems education. However, most publications in this area report on the individual experiences of an institution or an academic. This paper focuses on the most popular Enterprise System - SAP - and summarizes the outcomes of a global survey on the status quo of SAP-related education. Based on feedback of 305 lecturers and more than 700 students, it reports on the main factors of Enterprise Systems education including, critical success factors, alternative hosting models, and students’ perceptions. The results show among others an overall increasing interest in advanced SAP solutions and international collaboration, and a high satisfaction with the concept of using Application Hosting Centers

Business Process Management Education in Academia: Status, challenges, and Recommendations

In response to the growing proliferation of Business Process Management (BPM) in industry and the demand this creates for BPM expertise, universities across the globe are at various stages of incorporating knowledge and skills in their teaching offerings. However, there are still only a handful of institutions that offer specialized education in BPM in a systematic and in-depth manner. This article is based on a global educators’ panel discussion held at the 2009 European Conference on Information Systems in Verona, Italy. The article presents the BPM programs of five universities from Australia, Europe, Africa, and North America, describing the BPM content covered, program and course structures, and challenges and lessons learned. The article also provides a comparative content analysis of BPM education programs illustrating a heterogeneous view of BPM. The examples presented demonstrate how different courses and programs can be developed to meet the educational goals of a university department, program, or school. This article contributes insights on how best to continuously sustain and reshape BPM education to ensure it remains dynamic, responsive, and sustainable in light of the evolving and ever-changing marketplace demands for BPM expertise

Wireless power strip socket

Today, the demand on the electricity consumption is increasing throughout the society. Unfortunately, it goes without saying that in spite of the heavy consumption of the electricity, the number of damages and fatal accidents also caused by electricity if consumers neglected the safety precaution related to it. In fact, based on the statistics of the electrical accident released by the Energy Commission (ST), 13 cases of electric accidents occurred nationwide with seven involving the deceased in 2015 [1]. Researchers have taken the opportunities to invent many kinds of products or mechanism to minimize the losses created by the malfunctioning of the electrical devices. Having thought of the situation, a portable device using smartphones has been designed to encounter this matter