Detecting User’s Behavior Shift with Sensorized Shoes and Stigmergic Perceptrons

As populations become increasingly aged, health monitoring has gained increasing importance. Recent advances in engineering of sensing, processing and artificial learning, make the development of non-invasive systems able to observe changes over time possible. In this context, the Ki-Foot project aims at developing a sensorized shoe and a machine learning architecture based on computational stigmergy to detect small variations in subjects gait and to learn and detect users behaviour shift. This paper outlines the challenges in the field and summarizes the proposed approach. The machine learning architecture has been developed and publicly released after early experimentation, in order to foster its application on real environments

A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage

A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in the urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e hotspots), analyze their activity over time, and unfold anomalies. Moreover, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin

A Model for Using Physiological Conditions for Proactive Tourist Recommendations

Mobile proactive tourist recommender systems can support tourists by recommending the best choice depending on different contexts related to herself and the environment. In this paper, we propose to utilize wearable sensors to gather health information about a tourist and use them for recommending tourist activities. We discuss a range of wearable devices, sensors to infer physiological conditions of the users, and exemplify the feasibility using a popular self-quantification mobile app. Our main contribution then comprises a data model to derive relations between the parameters measured by the wearable sensors, such as heart rate, body temperature, blood pressure, and use them to infer the physiological condition of a user. This model can then be used to derive classes of tourist activities that determine which items should be recommended

Stock price forecasting over adaptive timescale using supervised learning and receptive fields

Pattern recognition in financial time series is not a trivial task, due to level of noise, volatile context, lack of formal definitions and high number of pattern variants. A current research trend involves machine learning techniques and online computing. However, medium-term trading is still based on human centric heuristics, and the integration with machine learning support remains relatively unexplored. The purpose of this study is to investigate potential and perspectives of a novel architectural topology providing modularity, scalability and personalization capabilities. The proposed architecture is based on the concept of Receptive Fields (RF), i.e., sub-modules focusing on specific patterns, that can be connected to further levels of processing to analyze the price dynamics on different granularities and different abstraction levels. Both Multilayer Perceptrons (MLP) and Support Vector Machines (SVM) have been experimented as a RF. Early experiments have been carried out over the FTSEMIB index

Recognizing motor imagery tasks from EEG oscillations through a novel ensemble-based neural network architecture

Brain-Computer Interfaces (BCI) provide effective tools aimed at recognizing different brain activities, translate them into actions, and enable humans to directly communicate through them. In this context, the need for strong recognition performances results in increasingly sophisticated machine learning (ML) techniques, which may result in poor performance in a real application (e.g., limiting a real-time implementation). Here, we propose an ensemble approach to effectively balance between ML performance and computational costs in a BCI framework. The proposed model builds a classifier by combining different ML models (base-models) that are specialized to different classification sub-problems. More specifically, we employ this strategy with an ensemble-based architecture consisting of multi-layer perceptrons, and test its performance on a publicly available electroencephalography-based BCI dataset with four-class motor imagery tasks. Compared to previously proposed models tested on the same dataset, the proposed approach provides greater average classification performances and lower inter-subject variability

Machine learning with ensemble stacking model for automated sleep staging using dual-channel EEG signal

Abstract Sleep staging is an important part of diagnosing the different types of sleep-related disorders because any discrepancies in the sleep scoring process may cause serious health problems such as misinterpretations of sleep patterns, medication errors, and improper diagnosis. The best way of analyzing sleep staging is visual interpretations of the polysomnography (PSG) signals recordings from the patients, which is a quite tedious task, requires more domain experts, and time-consuming process. This proposed study aims to develop a new automated sleep staging system using the brain EEG signals. Based on a new automated sleep staging system based on an ensemble learning stacking model that integrates Random Forest (RF) and eXtreme Gradient Boosting (XGBoosting). Additionally, this proposed methodology considers the subjects' age, which helps analyze the S1 sleep stage properly. In this study, both linear (time and frequency) and non-linear features are extracted from the pre-processed signals. The most relevant features are selected using the ReliefF weight algorithm. Finally, the selected features are classified through the proposed two-layer stacking model. The proposed methodology performance is evaluated using the two most popular datasets, such as the Sleep-EDF dataset (S-EDF) and Sleep Expanded-EDF database (SE-EDF) under the Rechtschaffen & Kales (R&K) sleep scoring rules. The performance of the proposed method is also compared with the existing published sleep staging methods. The comparison results signify that the proposed sleep staging system has an excellent improvement in classification accuracy for the six-two sleep states classification. In the S-EDF dataset, the overall accuracy and Cohen's kappa coefficient score obtained by the proposed model is (91.10%, 0.87) and (90.68%, 0.86) with inclusion and exclusion of age feature using the Fpz-Cz channel, respectively. Similarly, the Pz-Oz channel's performance is (90.56%, 0.86) with age feature and (90.11%, 0.86) without age feature. The performed results with the SE-EDF dataset using Fpz-Cz channel is (81.32%, 0.77) and (81.06%, 0.76), using Pz-Oz channel with the inclusion and exclusion of the age feature, respectively. Similarly the model achieved an overall accuracy of 96.67% (CT-6), 96.60% (CT-5), 96.28% (CT-4),96.30% (CT-3) and 97.30% (CT-2) for with 16 selected features using S-EDF database. Similarly the model reported an overall accuracy of 85.85%, 84.98%, 85.51%, 85.37% and 87.40% for CT-6 to CT-2 with 18 selected features using SE-EDF database

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

How do Smart watches influence the market of luxury watches with particular regard of the buying-reasons.

Ya no es necesario mirar el reloj de pulsera para saber la hora exacta. Los teléfonos inteligentes, el reloj del ordenador o el reloj del automóvil nos informan también sobre la hora. La gente paga miles de euros por un reloj de lujo, aunque podría comprar relojes mucho más económicos, que además cumplen la función de indicar la hora exacta. Hace unos años, los relojes inteligentes entraron en el mercado y ahora la gente puede obtener además datos como la distancia que recorren a pie por día o controlar su sueño... La pregunta es: ¿cómo reaccionarán los clientes de relojes de lujo? La intención de esta tesis, entre otros objetivos, es establecer los diferentes tipos de razones por las que los consumidores tienden a comprar relojes de lujo. Las diferentes razones pueden ser explicadas con la ayuda de modelos y variables psicológicas que facilitan la comprensión de los motivos del comportamiento de compra. En el estudio se trata de explicar la importancia y el significado de identidad de la marca con referencia a la compra de relojes inteligentes y relojes de lujo, iden-tificando los beneficios y características de los relojes inteligentes, que se entienden como productos sustitutivos de los relojes de lujo. Además, se pretende explicar las razones de compra de los relojes inteligentes en comparación con los motivos para comprar relojes de lujo, y averiguar si el cliente típico de relojes de lujo tiene los mismos motivos y razones de compra que el cliente de relojes inteligentes. A través del análisis de la teoría de la actitud y la teoría de la congruencia, con referencia al comportamiento de compra y su influencia en la elección de marca, se establece un modelo de ecuación estructural que responde a los objetivos mencionados. La intención es obtener una comprensión profunda del efecto psico-lógico de las marcas para poder explicar la toma de decisiones de compra de este tipo de productos. Para ello, se han realizado estudios empíricos basados en cues-tionarios anónimos sobre las marcas Apple Watch y Rolex. Se comprueba que la influencia de la intención de elección de marca es mayor en Apple, en comparación con los clientes de Rolex. La norma subjetiva tiene la mayor relevancia con referencia a la intención de elección de marca en Rolex. Además, la congruencia real no es positivamente relevante con respecto a la in-tención de elegir relojes de la marca Rolex; de hecho, la congruencia real del cliente de Rolex es insignificante en comparación con la congruencia ideal. Con referencia a Apple Watch, la congruencia ideal juega el papel más im-portante para la intención de la elección de la marca. La personalidad de la marca del Apple Watch está más cerca del ideal de la persona de prueba, en comparación con la persona de prueba de Rolex. Según este estudio, la congruencia funcional no tiene relevancia positiva con referencia a la intención de elección de marca de Apple Watch. Los criterios rele-vantes para la congruencia funcional para la muestra que se aplican en este estudio son: cómo de bien está fabricado el producto, si es un producto duradero, cómo de alta es la calidad del material de fabricación y cómo se percibe el diseño del producto. Estos criterios,Administración y Dirección de Empresa

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject