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A review of assistive spatial orientation and navigation technologies for the visually impaired
Get PDFDynamic feet distance: A new functional assessment during treadmill locomotion in normal and thoracic spinal cord injured rats
Get PDFOf all the detrimental effects of spinal cord injury (SCI), one of the most devastating is the disruption of the ability to perform functional movement. Very little is known on the recovery of hindlimb joint kinematics after clinically-relevant contusive thoracic lesion in experimental animal models. A new functional assessment instrument, the dynamic feet distance (DFD) was used to describe the distance between the two feet throughout the gait cycle in normal and affected rodents. The purpose of this investigation was the evaluation and characterization of the DFD during treadmill locomotion in normal and T9 contusion injured rats, using three-dimensional (3D) instrumented gait analysis. Despite that normal and injured rats showed a similar pattern in the fifth metatarsal head joints distance excursion, we found a significantly wider distance between the feet during the entire gait cycle following spinal injury. This is the first study to quantify the distance between the two feet, throughout the gait cycle, and the biomechanical adjustments made between limbs in laboratory rodents after nervous system injury
Adaptive learning for dynamic environments: A comparative approach
Get PDFNowadays most learning problems demand adaptive solutions. Current challenges include temporal data streams, drift and non-stationary scenarios, often with text data, whether in social networks or in business systems. Various efforts have been pursued in machine learning settings to learn in such environments, specially because of their non-trivial nature, since changes occur between the distribution data used to define the model and the current environment. In this work we present the Drift Adaptive Retain Knowledge (DARK) framework to tackle adaptive learning in dynamic environments based on recent and retained knowledge. DARK handles an ensemble of multiple Support Vector Machine (SVM) models that are dynamically weighted and have distinct training window sizes. A comparative study with benchmark solutions in the field, namely the Learn + +.NSE algorithm, is also presented. Experimental results revealed that DARK outperforms Learn + +.NSE with two different base classifiers, an SVM and a Classification and Regression Tree (CART)
Hospital centre performance dimensions and internal stakeholder valuation: a case study
Get PDFPurpose - Hospital centres (HCs) are the result of a horizontal integration of two or more hospital units. The benefits of this integration have been presented in the literature. The purpose of this paper is to define the hospital performance dimensions most valued by HC internal stakeholders, and to evaluate if the importance given to each dimension is different when comparing professional groups. Design/methodology/approach - An in-depth HC case study using a quantitative survey based on the Parsons' social system action theory to achieve this goal was conducted which embraces the four major models of organizational performance. In the final version of the survey, 37 items were retained for analysis. An exploratory factor analysis was conducted for a final sample of 365 participants, through principal component analysis, with oblique rotation and the Kaiser criterion. Findings - Four factors were retained: "Human resources development and Internal Processes", "Attractiveness/Openness", "Public service mission" and "Interpersonal relationships". The means factor scores only reveal statistical differences between the attractiveness/openness factor and the remaining three factors. A shared view was found in this study among the three groups of internal stakeholders: physicians, caregivers and administrative staff. Originality/value - The results of this study suggest that the HC performance concept should be expanded and performance measurement frameworks with a greater scope should be used. Interpersonal relationships, the human resources development and the public service are considered important dimensions for the performance measurement of the HC. Additionally, a consensual view regarding the most valued performance dimension could contribute to a beneficial and healthy working environment and improvements in HC performance
A framework for designing backroom areas in grocery stores
Get PDFPurpose - The design of retail backroom storage areas has great impact on in-store operations, customer service level and on store life-cycle costs. Moreover, backroom storage in modern retail grocery stores is critical to several functions, such as acting as a buffer against strong demand lifts yielded by an ever-increasing promotional activity, stocking seasonal peak demand and accommodating e-commerce activities. The purpose of this paper is to propose a framework to design retail backroom storage area. Furthermore, the authors aim to draw attention to the lack of literature on this topic, while clarifying the relationship between this promising research stream and the considerable body of research regarding the design and operations of conventional warehouses, as well as retail in-store operations. Design/methodology/approach - The key literature on backrooms, grocery retail, in-store operations, warehouse design and operations was reviewed. This allowed an understanding of the gap in the literature regarding the design of backrooms. Moreover, a case study methodological approach was conducted in a Portuguese retailer to extend the literature review. Findings - Despite having functions similar to conventional warehouses, backroom storage facilities have particularities that deserve a distinct analysis. Thus, the authors stress these differences and demonstrate how they influence the development of a novel backroom design framework. Originality/value - This paper fills a gap by proposing a framework to design backroom areas. Furthermore, this research may help practitioners to better design backroom areas, since this process currently lacks a formal and standardized procedure
Beat-ID: Towards a computationally low-cost single heartbeat biometric identity check system based on electrocardiogram wave morphology
Get PDFIn recent years, safer and more reliable biometric methods have been developed. Apart from the need for enhanced security, the media and entertainment sectors have also been applying biometrics in the emerging market of user-adaptable objects/systems to make these systems more user-friendly. However, the complexity of some state-of-the-art biometric systems (e.g., iris recognition) or their high false rejection rate (e.g., fingerprint recognition) is neither compatible with the simple hardware architecture required by reduced-size devices nor the new trend of implementing smart objects within the dynamic market of the Internet of Things (IoT). It was recently shown that an individual can be recognized by extracting features from their electrocardiogram (ECG). However, most current ECG-based biometric algorithms are computationally demanding and/or rely on relatively large (several seconds) ECG samples, which are incompatible with the aforementioned application fields. Here, we present a computationally low-cost method (patent pending), including simple mathematical operations, for identifying a person using only three ECG morphology-based characteristics from a single heartbeat. The algorithm was trained/tested using ECG signals of different duration from the Physionet database on more than 60 different training/test datasets. The proposed method achieved maximal averaged accuracy of 97.450% in distinguishing each subject from a ten-subject set and false acceptance and rejection rates (FAR and FRR) of 5.710 +/- 1.900% and 3.440 +/- 1.980%, respectively, placing Beat-ID in a very competitive position in terms of the FRR/FAR among state-of-the-art methods. Furthermore, the proposed method can identify a person using an average of 1.020 heartbeats. It therefore has FRR/FAR behavior similar to obtaining a fingerprint, yet it is simpler and requires less expensive hardware. This method targets low-computational/energy-cost scenarios, such as tiny wearable devices (e.g., a smart object that automatically adapts its configuration to the user). A hardware proof-of concept implementation is presented as an annex to this paper
Optimizing Daily Operation of Battery Energy Storage Systems Under Real-Time Pricing Schemes
No full textModernization of electricity networks is currently being carried out using the concept of the smart grid; hence, the active participation of end-user consumers and distributed generators will be allowed in order to increase system efficiency and renewable power accommodation. In this context, this paper proposes a comprehensive methodology to optimally control lead-acid batteries operating under dynamic pricing schemes in both independent and aggregated ways, taking into account the effects of the charge controller operation, the variable efficiency of the power converter, and the maximum capacity of the electricity network. A genetic algorithm is used to solve the optimization problem in which the daily net cost is minimized. The effectiveness and computational efficiency of the proposed methodology is illustrated using real data from the Spanish electricity market during 2014 and 2015 in order to evaluate the effects of forecasting error of energy prices, observing an important reduction in the estimated benefit as a result of both factors: 1) forecasting error and 2) power system limitations
New Multi-Stage and Stochastic Mathematical Model for Maximizing RES Hosting Capacity-Part II: Numerical Results
No full textA new multistage and stochastic mathematical model of an integrated distribution system planning problem is described in Part I. The efficiency and validity of this model are tested by carrying out a case study on a standard IEEE 41-bus radial distribution system. The numerical results show that the simultaneous integration of energy storage systems (ESSs) and reactive power sources largely enables a substantially increased penetration of variable generation (wind and solar) in the system, and consequently, reduces overall system costs and network losses. For the system, a combined wind and solar PV power of up to nearly three times the base-case peak load is installed over a three-year planning horizon. In addition, the proposed planning approach also considerably defers network expansion and/or reinforcement needs. Generally, it is clearly demonstrated in an innovative way that the joint planning of distributed generation, reactive power sources, and ESSs, brings significant improvements to the system such as reduction of losses, electricity cost, and emissions as a result of increased renewable energy sources (RESs) penetration. Besides, the proposed modeling framework considerably improves the voltage profile in the system, which is crucial for a normal operation of the system as a whole. Finally, the novel planning model proposed can be considered as a major leap forward toward developing controllable grids, which support large-scale integration of RESs
Enriching Mental Health Mobile Assessment and Intervention with Situation Awareness
Get PDFCurrent mobile devices allow the execution of sophisticated applications with the capacity for identifying the user situation, which can be helpful in treatments of mental disorders. In this paper, we present SituMan, a solution that provides situation awareness to MoodBuster, an ecological momentary assessment and intervention mobile application used to request self-assessments from patients in depression treatments. SituMan has a fuzzy inference engine to identify patient situations using context data gathered from the sensors embedded in mobile devices. Situations are specified jointly by the patient and mental health professional, and they can represent the patient's daily routine (e.g., "studying", "at work", "working out"). MoodBuster requests mental status self-assessments from patients at adequate moments using situation awareness. In addition, SituMan saves and displays patient situations in a summary, delivering them for consultation by mental health professionals. A first experimental evaluation was performed to assess the user satisfaction with the approaches to define and identify situations. This experiment showed that SituMan was well evaluated in both criteria. A second experiment was performed to assess the accuracy of the fuzzy engine to infer situations. Results from the second experiment showed that the fuzzy inference engine has a good accuracy to identify situations