Unveiling E-bike potential for commuting trips from GPS traces

Common goals of sustainable mobility approaches are to reduce the need for travel, to facilitate modal shifts, to decrease trip distances and to improve energy efficiency in the transportation systems. Among these issues, modal shift plays an important role for the adoption of vehicles with fewer or zero emissions. Nowadays, the electric bike (e-bike) is becoming a valid alternative to cars in urban areas. However, to promote modal shift, a better understanding of the mobility behaviour of e-bike users is required. In this paper, we investigate the mobility habits of e-bikers using GPS data collected in Belgium from 2014 to 2015. By analysing more than 10,000 trips, we provide insights about e-bike trip features such as: distance, duration and speed. In addition, we offer a deep look into which routes are preferred by bike owners in terms of their physical characteristics and how weather influences e-bike usage. Results show that trips with higher travel distances are performed during working days and are correlated with higher average speeds. Usage patterns extracted from our data set also indicate that e-bikes are preferred for commuting (home-work) and business (work related) trips rather than for recreational trips

Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People

This paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context. The contribution also aims to analyse the usage of the models for the transfer of knowledge when different sensors with different settings contribute to the noise levels. Finally, based on the authors’ experience, a framework proposal for creating valuable and aggregated knowledge is depicted.This research was partially funded by Fundación Tecnalia Research & Innovation, and J.O.-M. also wants to recognise the support obtained from the EU RFCS program through project number 793505 ‘4.0 Lean system integrating workers and processes (WISEST)’ and from the grant PRX18/00036 given by the Spanish Secretaría de Estado de Universidades, Investigación, Desarrollo e Innovación del Ministerio de Ciencia, Innovación y Universidades

NILM techniques for intelligent home energy management and ambient assisted living: a review

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio

A hybrid method for the analysis of learner behaviour in active learning environments

Software-mediated learning requires adjustments in the teaching and learning process. In particular active learning facilitated through interactive learning software differs from traditional instructor-oriented, classroom-based teaching. We present behaviour analysis techniques for Web-mediated learning. Motivation, acceptance of the learning approach and technology, learning organisation and actual tool usage are aspects of behaviour that require different analysis techniques to be used. A hybrid method based on a combination of survey methods and Web usage mining techniques can provide accurate and comprehensive analysis results. These techniques allow us to evaluate active learning approaches implemented in form of Web tutorials

Data mining technology for the evaluation of learning content interaction

Interactivity is central for the success of learning. In e-learning and other educational multimedia environments, the evaluation of interaction and behaviour is particularly crucial. Data mining – a non-intrusive, objective analysis technology – shall be proposed as the central evaluation technology for the analysis of the usage of computer-based educational environments and in particular of the interaction with educational content. Basic mining techniques are reviewed and their application in a Web-based third-level course environment is illustrated. Analytic models capturing interaction aspects from the application domain (learning) and the software infrastructure (interactive multimedia) are required for the meaningful interpretation of mining results

Categorisation of activities of daily living of lower limb amputees during short-term use of a portable kinetic recording system: a preliminary study

The purpose of this preliminary study was to determine the relevance of the categorisation of the load regime data to assess the functional output and usage of the prosthesis of lower limb amputees. The objectives were (A) to introduce a categorisation of load regime, (B) to present some descriptors of each activity and (C) to report the results for a case. The load applied on the osseointegrated fixation of one transfemoral amputee was recorded using a portable kinetic system for five hours. The periods of directional locomotion, localised locomotion and stationary loading occurred 44%, 34% and 22% of recording time and each accounted for 51%, 38% and 12% of the duration of the periods of activity, respectively. The absolute maximum force during directional locomotion, localised locomotion and stationary loading was 19%, 15% and 8% of the BW on the antero-posterior axis, 20%, 19% and 12% on the medio-lateral axis as well as 121%, 106% and 99% on the long axis. A total of 2,783 gait cycles were recorded. Approximately 10% more gait cycles and 50% more of the total impulse than conventional analyses were identified. The proposed categorisation and apparatus have the potential to complement conventional instruments, particularly for difficult cases