Panagiota Anastasopoulou

Classification of human physical activity and energy expenditure estimation by accelerometry and barometry

Regular exercise and physical activity are among the most important factors influencing the quality of life and make a significant contribution to the maintenance of health and well-being. The assessment of physical activity via accelerometry has become a promising technique often used as means to objectively measure physical activity. This work proposes a simple and reliable method to assess human physical activity and calculate the energy expenditure (EE) by using an acceleration and an air pressure sensor. Our proposed algorithm differentiates between 7 activities with an average accuracy of 98.2% and estimates the second by second EE with an average percent error of 1.59 ± 8.20% using a single measurement unit attached to the subjects hip.

Validation and comparison of two methods to assess human energy expenditure during free-living activities.

Background The measurement of activity energy expenditure (AEE) via accelerometry is the most commonly used objective method for assessing human daily physical activity and has gained increasing importance in the medical, sports and psychological science research in recent years. Objective The purpose of this study was to determine which of the following procedures is more accurate to determine the energy cost during the most common everyday life activities; a single regression or an activity based approach. For this we used a device that utilizes single regression models (GT3X, ActiGraph Manufacturing Technology Inc., FL., USA) and a device using activity-dependent calculation models (move II, movisens GmbH, Karlsruhe, Germany). Material and Methods Nineteen adults (11 male, 8 female; 30.4±9.0 years) wore the activity monitors attached to the waist and a portable indirect calorimeter (IC) as reference measure for AEE while performing several typical daily activities. The accuracy of the two devices for estimating AEE was assessed as the mean differences between their output and the reference and evaluated using Bland-Altman analysis. Results The GT3X overestimated the AEE of walking (GT3X minus reference, 1.26 kcal/min), walking fast (1.72 kcal/min), walking up−/downhill (1.45 kcal/min) and walking upstairs (1.92 kcal/min) and underestimated the AEE of jogging (−1.30 kcal/min) and walking upstairs (−2.46 kcal/min). The errors for move II were smaller than those for GT3X for all activities. The move II overestimated AEE of walking (move II minus reference, 0.21 kcal/min), walking up−/downhill (0.06 kcal/min) and stair walking (upstairs: 0.13 kcal/min; downstairs: 0.29 kcal/min) and underestimated AEE of walking fast (−0.11 kcal/min) and jogging (−0.93 kcal/min). Conclusions Our data suggest that the activity monitor using activity-dependent calculation models is more appropriate for predicting AEE in daily life than the activity monitor using a single regression model.

Characteristics of the activity-affect association in inactive people: an ambulatory assessment study in daily life.

Acute and regular exercise as well as physical activity (PA) is related to well-being and positive affect. Recent studies have shown that even daily, unstructured physical activities increase positive affect. However, the attempt to achieve adherence to PA or exercise in inactive people through public health interventions has often been unsuccessful. Most studies analyzing the activity-affect association in daily life, did not report participants’ habitual activity behavior. Thus, samples included active and inactive people, but they did not necessarily exhibit the same affective reactions to PA in daily life. Therefore the present study investigated whether the association between PA and subsequent affective state in daily life can also be observed in inactive individuals. We conducted a pilot study with 29 inactive university students (mean age 21.3 ± 1.7 years) using the method of ambulatory assessment. Affect was assessed via electronic diary and PA was measured with accelerometers. Participants had to rate affect every 2 h on a six item bipolar scale reflecting the three basic mood dimensions energetic arousal, valence, and calmness. We calculated activity intensity level [mean Metabolic Equivalent (MET) value] and the amount of time spent in light activity over the last 15 min before every diary prompt and conducted within-subject correlations. We did not find significant associations between activity intensity and the three mood dimensions. Due to the high variability in within-subject correlations we conclude that not all inactive people show the same affective reactions to PA in daily life. Analyzing the PA-affect association of inactive people was difficult due to little variance and distribution of the assessed variables. Interactive assessment and randomized controlled trials might help solving these problems. Future studies should examine characteristics of affective responses of inactive people to PA in daily life. General assumptions considering the relation between affect and PA might not be suitable for this target group.

Mobile Multi-parametric Sensor System for Diagnosis of Epilepsy and Brain Related Disorders

Epilepsy is the commonest serious brain disorder, affecting 1-2% of the general population. Epileptic seizures are usually expressed with a wide range of paroxysmal recurring motor, cognitive, autonomic symptoms and EEG changes. Therefore reliable diagnosis requires state of the art monitoring and communication technologies providing real-time, accurate and continuous brain and body multi-parametric data measurements. The purpose of this paper is to present an adequate mobile system comprising all required sensor types for the everyday life monitoring of patients with epilepsy.

Mobile monitoring of epileptic patients using a reconfigurable cyberphysical system that handles multi-parametric data acquisition and analysis

Epilepsy is one of the commonest, serious and divesting brain disorders. Although it is still an incurable disorder in most cases its symptoms can be ameliorated by lifelong pharmaceutical treatment. Depending on the type of epilepsy and due to its multifactorial causes, different brain and body parameters need to be assessed continuously over a long period. This allows clinicians to have a better understanding of the patients state of health and to be able to continuously adjust and change the medical treatment accordingly. Beside this, multi-parametric monitoring could be used for other purposes such as accurate diagnosis, detection of seizures, alerting and prevention and presurgical evaluation. The purpose of this paper is to present the architecture of the whole cyberphysical system, comprising a modular framework that is able to connect all the required sensor types and perform online data analysis for the monitoring of patients with epilepsy.

Platform for ambulatory assessment of psycho-physiological signals and online data capture

Over the last years there has been an increasing interest in finding new methods for capturing psychological, behavioral and physiological data in real-time using infield data acquisition systems. Within our research group a system for ambulatory assessment of psycho-physiological signals for real-time data capture has been developed. The system is based on Smartphones which are equipped with software to record contextual and subjective data from participants and have that information transmitted wirelessly to a central online database. An online database offers the possibility of collecting, storing and analyzing all study related data in a central point. This paper provides an overview of this system.

Using Support Vector Regression for Assessing Human Energy Expenditure Using a Triaxial Accelerometer and a Barometer

Physical inactivity is nowadays defined as the fourth leading risk factor for global mortality. These levels are rising worldwide with major aftereffects on the prevention of several diseases and the general health of the population. Energy expenditure (EE) is a very important parameter usually used as a dimension in physical activity assessment studies. However, the most accurate methods for the measurement of the EE are usually costly, obtrusive and most are limited by laboratory conditions. Recent technological advancements in the sensor technology along with the great progress made in algorithms have made accelerometers a powerful technique often used to assess everyday physical activity. This paper discusses the use of support vector regression (SVR) to predict EE by using a single measurement unit, equipped with a triaxial accelerometer and a barometer, attached to the subject´s hip.

Mobile Sensors for Multiparametric Monitoring in Epileptic Patients

Multiparametric monitoring represents the assessment of physiological, behavioral and/or subjective, data with mobile sensor systems. Recent technological developments like low power microcontrollers, low power wireless standards such as Bluetooth Low Energy or Smartphones with an increasing computing capacity enhance the development of small and distributed multiparametric monitoring systems (Handbook of biomedical telemetry, Wiley, Piscataway, 2014).

Advanced Multi-parametric Monitoring and Analysis for Diagnosis and Optimal Management of Epilepsy and Related Brain Disorders: The ARMOR Project

The ARMOR project addresses the needs of the epileptic patient and healthcare professional, aiming at the design and development of a nonintrusive Personal Health System (PHS) for the monitoring and analysis of epilepsy-relevant multi-parametric data, (i.e. EEG, EOG, EMG, EKG, skin conductance data) and the documentation of the epilepsy related symptoms. ARMOR platform incorporates models derived from data analysis based on already existing state-of-the-art communication platform solutions emphasizing on security issues and required adaptations to meet ARMOR specifications. In this context, this chapter aims to provide an extensive description of the main aspects and issues addressed in the project as well as the main characteristics of the developed platform.