Preterm birth is associated with increased risks of neurological and motor impairments such as cerebral palsy. The risks are highest in those born at the lowest gestations. Early identification of those most at risk is challenging meaning that a critical window of opportunity to improve outcomes through therapy-based interventions may be missed. Clinically, the assessment of spontaneous general movements is an important tool, which can be used for the prediction of movement impairments in high risk infants. Movement recognition aims to capture and analyze relevant limb movements through computerized approaches focusing on continuous, objective, and quantitative assessment. Different methods of recording and analyzing infant movements have recently been explored in high risk infants. These range from camera-based solutions to body-worn miniaturized movement sensors used to record continuous time-series data that represent the dynamics of limb movements. Various machine learning methods have been developed and applied to the analysis of the recorded movement data. This analysis has focused on the detection and classification of atypical spontaneous general movements. This article aims to identify recent translational studies using movement recognition technology as a method of assessing movement in high risk infants. The application of this technology within pediatric practice represents a growing area of inter-disciplinary collaboration, which may lead to a greater understanding of the development of the nervous system in infants at high risk of motor impairment.

The LoRaWAN based Low Power Wide Area networks aim to provide long-range connectivity to a large number of devices by exploiting limited radio resources. The Adaptive Data Rate (ADR) mechanism controls the assignment of these resources to individual end-devices by a runtime adaptation of their communication parameters when the quality of links inevitably changes over time. This paper provides a detailed performance analysis of the ADR technique presented in the recently released LoRaWAN Specifications (v1.1). We show that the ADR technique lacks the agility to adapt to the changing link conditions, requiring a number of hours to days to converge to a reliable and energy-efficient communication state. As a vital step towards improving this situation, we then change different control knobs or parameters in the ADR technique to observe their effects on the convergence time.

No abstract

Abstract-Stress, anxiety and depression in the workplace are detrimental to human health and productivity with significant financial implications. Recent research in this area has focused on the use of sensor technologies, including smartphones and wearables embedded with physiological and movement sensors. In this work, we explore the possibility of using such devices for mood recognition, focusing on work environments. We propose a novel mood recognition framework that is able to identify five intensity levels for eight different types of moods every two hours. We further present a smartphone app ('HealthyOffice'), designed to facilitate self-reporting in a structured manner and provide our model with the ground truth. We evaluate our system in a small-scale user study where wearable sensing data is collected in an office environment. Our experiments exhibit promising results allowing us to reliably recognize various classes of perceived moods.

Purpose Despite more than two decades of experience regarding the adoption and implementation of enterprise resource planning (ERP) systems in organizations, ERPs success is questionable. Though ERPs success stories are published in past research studies, the failure rate of ERP systems is relatively high. The purpose of this study was to find issues and challenges and assess the degree of criticality of these issues/challenges faced by organizations during ERP implementation. Design/methodology/approach For doing systematic review/research synthesis systematic literature review (SLR) was carried out considering research studies published within the time period, i.e. 1999-2018. Three major steps such as planning, conducting and reporting were followed to proceed further in this study. This study attempted to accomplish a critical review of 53 studies out of 103 studies identified, which were published in reputable journals to synthesize the existing literature in the ERP domain. The studies selected have almost addressed different challenges/issues faced by small and large organizations during ERP implementation. Findings Research synthesis/SLR led to the identification of 31 issues/challenges, which may be termed as most critical based on their occurrence/frequency in past studies included. The topmost ten issues/challenges amongst 31 identified include top management approach, change management, training and development, effective communication, system integration, business process reengineering, consultants/vendors selection, project management, project team formation, team empowerment/skilled people and data conversing/migration. However, other issues/challenges identified such as security risks/data security, cloud awareness, functionality limitations, service level agreements and subscription expenses are more related to cloud ERPs. Originality/value The current study is unique in its kind, focusing on the issues and challenges faced by organization during implementing ERP projects. Moreover, this study contributes to understanding and further analyzing management capabilities for developing remedial measures while planning the implementation of an enterprise system in their organizations prior to the occurrence of different issues and challenges ahead. The study also led to understanding and explaining socio-technical issues and their severity.

Saving energy in residential and commercial buildings is of great interest due to diminishing resources. Heating ventilation and air conditioning systems, and electric lighting are responsible for a significant share of energy usage, which makes it desirable to optimise their operations while maintaining user comfort. Such optimisation requires accurate occupancy estimations. In contrast to current, often invasive or unreliable methods we present an approach for accurate occupancy estimation using a wireless sensor network (WSN) that only collects non-sensitive data and a novel, hierarchical analysis method. We integrate potentially uncertain contextual information to produce occupancy estimates at different levels of granularity and provide confidence measures for effective building management. We evaluate our framework in real-world deployments and demonstrate its effectiveness and accuracy for occupancy monitoring in both low-and high-traffic area scenarios. Furthermore, we show how the system is used for analysing historical data and identify effective room misuse and thus a potential for energy saving.

No abstract

No abstract