Baran: an interaction-centred user monitoring framework

User Quality of Experience (QoE) is a subjective entity and difficult to measure. One important aspect of it, User Experience (UX), corresponds to the sensory and emotional state of a user. For a user interacting through a User Interface (UI), precise information on how they are using the UI can contribute to understanding their UX, and thereby understanding their QoE. As well as a user’s use of the UI such as clicking, scrolling, touching, or selecting, other real-time digital information about the user such as from smart phone sensors (e.g. accelerometer, light level) and physiological sensors (e.g. heart rate, ECG, EEG) could contribute to understanding UX. Baran is a framework that is designed to capture, record, manage and analyse the User Digital Imprint (UDI) which, is the data structure containing all user context information. Baran simplifies the process of collecting experimental information in Human and Computer Interaction (HCI) studies, by recording comprehensive real-time data for any UI experiment, and making the data available as a standard UDI data structure. This paper presents an overview of the Baran framework, and provides an example of its use to record user interaction and perform some basic analysis of the interaction.

Comparación de la medida de actividad física mediante cuestionario Ipaq-L y acelerómetro ‘Mywellness Key’ en trabajadores

Objetivo: comparar la concordancia en las mediciones de actividad física en un grupo de trabajadores mediante el acelerómetro ‘Mywellness key’(Technogym, Gambettola, Italy), y un sistema indirecto, cuestionario ‘IPAQ-L’ (International Physical Activity Questionnaire, Long Version), y comprobar la relación entre ambas estimaciones. Sujetos: 59 trabajadores (41 mujeres y 18 hombres), con una edad media de 36 ± 7 años y un IMC de 23.96 ± 3.50. Material y métodos: la medición de los niveles de actividad física se realizó mediante el cuestionario IPAQ-L y acelerómetros ‘Mywellness key’ (Technogym, Gambettola, Italy). Todos los sujetos llevaron puesto el acelerómetro durante 7 días consecutivos (2 festivos y 5 laborables). Se empleó el coeficiente de correlaciones bivariadas de Pearson y el CCI (coeficiente de correlación intraclase) (p<.05). Resultados: los valores se correlacionaron positivamente en todo el grupo de manera significativa en los niveles de AF vigorosa (r = 0.93; CCI = 0.757; p<.001) y de AF Moderada (r=0.91; CCI = 0.951; p<.001). Conclusión: se encontró una muy buena correlación en actividades vigorosas y moderadas entre el acelerómetro ‘Mywellness key’ y el cuestionario IPAQ-L.

A practical MEMS gravimeter

The ability to measure tiny variations in the local gravitational acceleration allows – amongst other applications – the detection of hidden hydrocarbon reserves, magma build-up before volcanic eruptions, and subterranean tunnels. Several technologies are available that achieve the sensitivities required (tens of μGal/√Hz), and stabilities required (periods of days to weeks) for such applications: free-fall gravimeters, spring-based gravimeters, superconducting gravimeters, and atom interferometers. All of these devices can observe the Earth tides; the elastic deformation of the Earth’s crust as a result of tidal forces. This is a universally predictable gravitational signal that requires both high sensitivity and high stability over timescales of several days to measure. All present gravimeters, however, have limitations of excessive cost (£70 k) and high mass (<8 kg). In this thesis, the building of a microelectromechanical system (MEMS) gravimeter with a sensitivity of 40 μGal/√Hz in a package size of only a few cubic centimetres is discussed. MEMS accelerometers – found in most smart phones – can be mass-produced remarkably cheaply, but most are not sensitive enough, and none have been stable enough to be called a ‘gravimeter’. The remarkable stability and sensitivity of the device is demonstrated with a measurement of the Earth tides. Such a measurement has never been undertaken with a MEMS device, and proves the long term stability of the instrument compared to any other MEMS device, making it the first MEMS accelerometer that can be classed as a gravimeter. This heralds a transformative step in MEMS accelerometer technology. Due to their small size and low cost, MEMS gravimeters could create a new paradigm in gravity mapping: exploration surveys could be carried out with drones instead of low-flying aircraft; they could be used for distributed land surveys in exploration settings, for the monitoring of volcanoes; or built into multi-pixel density contrast imaging arrays.

Estado nutricional y etapas de cambio comportamental frente a la actividad física niños y adolescentes de Bogotá, Colombia: Estudio FUPRECOL

Objetivo: El propósito del estudio fue relacionar la etapa en el cambio en el comportamiento frente a la actividad física y el estado nutricional en escolares entre 9 y 17 años de Bogotá- Colombia, pertenecientes al estudio FUPRECOL. Método: Se trata de un estudio transversal, en 6.606 niños y adolescentes entre 9 y 17 años, pertenecientes a 24 instituciones educativas, de Bogotá-Colombia. Se aplicó de manera auto-diligenciada el cuestionario de cambio de comportamiento en función a la intensión de realizar actividad física (CCC-Fuprecol) y se midió el peso y la estatura para determinar el estado nutricional con el índice de masa corporal (IMC). Resultados: El porcentaje de respuesta fue 94% y se consideraron válidos 6,606 registros, siendo 58.3 % (n=3.850) niñas con un promedio de edad de 12,7±2,3 años. En la población general, el 5,3 % de los escolares se encontraban en etapa de pre-contemplación, 31,8 % en contemplación, el 26,7 % en acción y el 36,2 % en etapa de mantenimiento. Al comparar la etapa de cambio con el estado nutricional por IMC, los escolares clasificados en obesidad mostraron mayor frecuencia de respuesta en la etapa de pre-contemplación, mientras que los escolares con peso saludable acusaron mayores porcentajes en la etapa de mantenimiento. Conclusión: En escolares de Bogotá, Colombia, se encontró una relación estadísticamente significativa entre a la intensión de realizar actividad con el estado nutricional medido con el IMC. Fomentar la promoción de la actividad física y monitorear el estado nutricional, deberá ser una prioridad en las agendas y políticas públicas dentro del ámbito escolar.

Validación de criterio concurrente del sistema de observación de tiempo de instrucción de condición física – SOFIT- para la medición de la actividad física durante las clases de educación física en escolares de 1º A 9º de Bogotá, Colombia

Determinar la validez concurrente del Sistema de Observación de Tiempo de Instrucción de Condición Física (SOFIT) a través de acelerometría, como método para medir los niveles de actividad física (AF) de los escolares de 1º a 9º durante la clase de educación física en tres colegios públicos de Bogotá, Colombia. Estudio transversal entre Octubre de 2014 y Marzo de 2015. La medición se realizó en tres colegios públicos de Bogotá. Participaron 48 estudiantes (25 niñas; 23 niños), entre 5 y 17 años, seleccionados de acuerdo al protocolo de SOFIT. El resultado se categoriza en porcentaje de tiempo en comportamiento sedentario, AF moderada, AF vigorosa, y AF moderada a vigorosa. Se validó utilizando como patrón de oro la acelerometría en las mismas categorías. Se realizó diferencia de medias, regresión lineal y modelo de efectos fijos. La correlación entre SOFIT y acelerometría fue buena para AF moderada (rho=,958; p=0,000), AF vigorosa (rho=,937; p=0,000) y AF de moderada a vigorosa (rho=0,962; p=0,000). Al igual que utilizando un modelo de efectos fijos, AF moderada (β1=0,92; p=0,00), vigorosa (β1=0,94; p=0,00) y AF de moderada a vigorosa (β1=0,95; p=0,00), mostrando ausencia de diferencias significativas entre los dos métodos para la medición de los niveles de AF. El comportamiento sedentario correlacionó positivamente en Spearman (rho=,0965; p=0,000), El sistema SOFIT demostró ser válido para medir niveles de AF en clases de educación física, tras buena correlación y concordancia con acelerometría. SOFIT es un instrumento de fácil acceso y de bajo costo para la medición de la AF durante las clases de educación física en el contexto escolar y se recomienda su uso en futuros estudios.

Niveles de actividad física en los periodos de recreo de niños y adolescentes en un colegio distrital de Bogotá

Objetivo: determinar los niveles de actividad física (AF) de niños y adolescentes entre 10 y 17 años durante los periodos de recreo escolar en un colegio distrital de Bogotá. Método: estudio de corte transversal en un colegio distrital de la localidad de Puente Aranda en Bogotá. Fueron observados a través del sistema de observación de juego y de actividad en el tiempo libre en jóvenes (SOPLAY) los niveles y tipos de AF de niños y adolescentes en los periodos de recreo durante tres semanas, utilizando una condición de observación diferente para cada semana. Adicionalmente, las condiciones del contexto de las áreas recreo deportivas fueron evaluadas. Resultados: las prevalencias de escolares sedentarios fueron de 52,4 %, 77,3 % y 64,9 % durante la 1ª, 2ª y 3ª semana respectivamente. El sexo femenino fue más sedentario con el masculino (57 %, 82 % y 73 % vs 45 %, 70 % y 54 %) para cada semana observada. Se obtuvieron diferencias significativas (p<0,05) en los niveles de AF de los escolares. Conclusión: niños y adolescentes presentan elevadas prevalencias de sedentarismo siendo las actividades más frecuentes estar sentado, de pie o acostado durante los periodos de recreo. El sexo masculino mostró porcentajes superiores de participación en AF moderadas vigorosas. Las áreas recreo deportivas no contaban con condiciones del contexto relacionadas con disponibilidad de equipamiento para realizar AF ni existencia de actividades organizadas. Son necesarios programas e intervenciones eficaces que promuevan la AF en niños y adolescentes durante el recreo de la jornada escolar.

Propiedades psicométricas de los instrumentos para la medición de la actividad física en adultos con discapacidad física relacionada con lesión de médula espinal: una revisión sistemática

Objetivo: Evaluar las propiedades psicométricas de los instrumentos para la medición de la actividad física en adultos de 18-65 años con discapacidad física por lesión de médula espinal. Materiales y métodos: Revisión sistemática. Las bases de datos de Medline, Scopus, Web of Science y 19 revistas especializadas fueron consultadas durante once días entre abril de 2015 y febrero de 2016 para identificar estudios originales de validación, sin límite de tiempo y que estuvieran publicados en español, francés y/o inglés. La calidad metodológica de los instrumentos de medición se evaluó usando las diferentes cajas de propiedades de la lista COSMIN. Resultados: Se identificaron 9229 referencias, de las cuales sólo 12 cumplieron los criterios de inclusión, dando como resultado 13 instrumentos de medición. Se evaluaron seis propiedades psicométricas. La propiedad más común fue la confiabilidad, además se observó que la calidad metodológica de los estudios incluidos no representa los resultados de las propiedades psicométricas de los instrumentos de medición. La calidad metodológica de los instrumentos para la evaluación de la actividad física en población con lesión medular espinal es “baja” para propiedades como consistencia interna, error de medición, sensibilidad, validez de criterio (con excepción del WISCI II que tiene buena validez) y excelente para validez de contenido y fiabilidad. Conclusión: Se ha encontrado que instrumentos empleados hasta el presente en la medición de la actividad física en población con discapacidad física relacionada con lesión de médula espinal han sido creados para otros tipos de discapacidad y otros instrumentos deben ser validados en futuros estudios.

Criterion-related validity of the short form of the international physical activity questionnaire in adults who are Deaf

Background: To implement appropriate programs for promoting physical activity (PA) in people who are Deaf, it is important to have valid instruments for assessing PA in this population. Objective: The main purpose of this study was to examine the criterion validity of the short form of the International Physical Activity Questionnaire (IPAQ-S) in Deaf adults. Method: This study included 44 adults (18e65 years) of both genders (63.6% were females) who met the inclusion criteria. Objective measures of PAwere collected using accelerometers, which were worn by each participant during one week. After using the accelerometer, the IPAQ-S was applied to assess participants’ physical activity during the last 7 days. Results: There was no significant correlation between the average time spent in moderate to vigorous physical activity (MVPA) as measured by the accelerometer (40.1 6 24.5 min/day) and by the IPAQ-S (41.3 6 57.5 min/day). The IPAQ-S significantly underestimated the time spent in sedentary behavior (7.6 6 2.7 h/day vs. 10.1 6 1.6 h/day). Sedentary behavior and MVPA as measured by the accelerometer and the IPAQ-S showed limited agreement. Conclusions: Our results show some limitations on the use of IPAQ-S for quantifying PA among adults who are Deaf. The IPAQ-S tends to overestimate the MVPA and to underestimate sedentary behavior in adults who are Deaf.

Preventing worker’s musculoskeletal disorders with wearable inertial sensors: ergonomics and modelling of physical human-robot interactions

The most widespread work-related diseases are musculoskeletal disorders (MSD) caused by awkward postures and excessive effort to upper limb muscles during work operations. The use of wearable IMU sensors could monitor the workers constantly to prevent hazardous actions, thus diminishing work injuries. In this thesis, procedures are developed and tested for ergonomic analyses in a working environment, based on a commercial motion capture system (MoCap) made of 17 Inertial Measurement Units (IMUs). An IMU is usually made of a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer that, through sensor fusion algorithms, estimates its attitude. Effective strategies for preventing MSD rely on various aspects: firstly, the accuracy of the IMU, depending on the chosen sensor and its calibration; secondly, the correct identification of the pose of each sensor on the worker’s body; thirdly, the chosen multibody model, which must consider both the accuracy and the computational burden, to provide results in real-time; finally, the model scaling law, which defines the possibility of a fast and accurate personalization of the multibody model geometry. Moreover, the MSD can be diminished using collaborative robots (cobots) as assisted devices for complex or heavy operations to relieve the worker's effort during repetitive tasks. All these aspects are considered to test and show the efficiency and usability of inertial MoCap systems for assessing ergonomics evaluation in real-time and implementing safety control strategies in collaborative robotics. Validation is performed with several experimental tests, both to test the proposed procedures and to compare the results of real-time multibody models developed in this thesis with the results from commercial software. As an additional result, the positive effects of using cobots as assisted devices for reducing human effort in repetitive industrial tasks are also shown, to demonstrate the potential of wearable electronics in on-field ergonomics analyses for industrial applications.

Novel methods of state estimation and parameter identification for high-performance motorcycles

This research activity aims at providing a reliable estimation of particular state variables or parameters concerning the dynamics and performance optimization of a MotoGP-class motorcycle, integrating the classical model-based approach with new methodologies involving artificial intelligence. The first topic of the research focuses on the estimation of the thermal behavior of the MotoGP carbon braking system. Numerical tools are developed to assess the instantaneous surface temperature distribution in the motorcycle's front brake discs. Within this application other important brake parameters are identified using Kalman filters, such as the disc convection coefficient and the power distribution in the disc-pads contact region. Subsequently, a physical model of the brake is built to estimate the instantaneous braking torque. However, the results obtained with this approach are highly limited by the knowledge of the friction coefficient (μ) between the disc rotor and the pads. Since the value of μ is a highly nonlinear function of many variables (namely temperature, pressure and angular velocity of the disc), an analytical model for the friction coefficient estimation appears impractical to establish. To overcome this challenge, an innovative hybrid solution is implemented, combining the benefit of artificial intelligence (AI) with classical model-based approach. Indeed, the disc temperature estimated through the thermal model previously implemented is processed by a machine learning algorithm that outputs the actual value of the friction coefficient thus improving the braking torque computation performed by the physical model of the brake. Finally, the last topic of this research activity regards the development of an AI algorithm to estimate the current sideslip angle of the motorcycle's front tire. While a single-track motorcycle kinematic model and IMU accelerometer signals theoretically enable sideslip calculation, the presence of accelerometer noise leads to a significant drift over time. To address this issue, a long short-term memory (LSTM) network is implemented.

Acquisition of Vibration Signals for Predictive Maintenance: a Practical Case

As predictive maintenance becomes more and more relevant in industrial environment, the possible range of applications for this maintenance strategy grows. The progresses in components technology and their reduction in price, together with the late years' advances in machine learning and in computational power, are making the implementation of predictive maintenance possible in plants where it would have previously been unreasonably costly. This is leading major pharmaceutical industries to explore the possibility of the application of condition monitoring systems on progressively less and less critical equipment. The focus of this thesis is on the implementation of a system to gather vibrational data from the motors installed in a pre-existing machine using off-the-shelf components. The final goal for the system is to provide the necessary vibration data, in the form of frequency spectra, to a machine learning system developed by IMA Digital, which will be leveraging such data to predict possible upcoming faults and to give the final client all the information necessary to plan maintenance activity according to the estimated machine condition.

Methodologies for attitude determination from MEMS sensors in vehicles

The focus of the thesis is the application of different attitude’s determination algorithms on data evaluated with MEMS sensor using a board provided by University of Bologna. MEMS sensors are a very cheap options to obtain acceleration, and angular velocity. The use of magnetometers based on Hall effect can provide further data. The disadvantage is that they have a lot of noise and drift which can affects the results. The different algorithms that have been used are: pitch and roll from accelerometer, yaw from magnetometer, attitude from gyroscope, TRIAD, QUEST, Magdwick, Mahony, Extended Kalman filter, Kalman GPS aided INS. In this work the algorithms have been rewritten to fit perfectly with the data provided from the MEMS sensor. The data collected by the board are acceleration on the three axis, angular velocity on the three axis, magnetic fields on the three axis, and latitude, longitude, and altitude from the GPS. Several tests and comparisons have been carried out installing the electric board on different vehicles operating in the air and on ground. The conclusion that can be drawn from this study is that the Magdwich filter is the best trade-off between computational capabilities required and results obtained. If attitude angles are obtained from accelerometers, gyroscopes, and magnetometer, inconsistent data are obtained for cases where high vibrations levels are noticed. On the other hand, Kalman filter based algorithms requires a high computational burden. TRIAD and QUEST algorithms doesn’t perform as well as filters.

Recognition of human activities through eSteps insoles

Recognition of everyday human activity through mobile personal sensing technology plays a central role in the field of pervasive healthcare. The Bologna-based American company eSteps Inc. addresses the growing motor disability of the lower limbs by offering pre-, during and post-hospitalisation monitoring solutions with biomechanics and telerehabilitation protocol. It has developed a smart, customised and sustainable device to monitor motor activity, fatigue and injury risk for patients and a special app to share data with caregivers and medical specialists. The objective of this study is the development of an Artificial Intelligence model to recognize the activity performed by a person with Multiple Sclerosis or a healthy person through eSteps devices.