Human activity recognition for an intelligent knee orthosis

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Human activity data discovery based on accelerometry

Dissertation to Obtain Master Degree in Biomedical Engineering

Human activity recognition with accelerometry: novel time and frequency features

Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.

Effects of environmental conditions, human activity, reproduction, antler cycle and grouping on fecal glucocorticoids of free-ranging Pampas deer stags (Ozotoceros bezoarticus bezoarticus)

In this study, a commercial enzyme immunoassay (EIA) was validated in detecting glucocorticoids in Pampas deer feces, in order to investigate the influence of several factors on the adrenocortical function. Fecal samples, behavioral data and information concerning male grouping and antlers status were collected at a monthly basis during a 1 year period from free-ranging stags living at Emas National Park, Brazil (18 degrees S/52 degrees W). The results revealed that concentrations of fecal glucocorticoids in winter were significantly higher than those corresponding to spring and summer. In addition, dry season data presented higher levels than during the wet season. Significant difference was found between fecal levels of breeding stags in summer and nonbreeding stags, whereas no difference was observed between breeding stags in winter and nonbreeding stags. on the other hand, males from areas with frequent human disturbance exhibited higher glucocorticoid concentrations and flight distances than individuals from areas of lower human activity. Males with antlers in velvet had elevated levels compared with animals in hard antler or antler casting. Also, we found that glucocorticoid levels were higher in groups with three or more males than in groups with only one male. The flight distances showed positive correlation with fecal glucocorticoid. These data indicate that fecal glucocorticoid provides a useful approach in the evaluation of physiological effects of environment, inter-individuals relationship and human-induced stressors on free-ranging Pampas deer stags. (c) 2005 Elsevier B.V. All rights reserved.

The Interaction Between Forest Fires and Human Activity in Southern Switzerland

The impact of human activities on the fire regime in southern Switzerland was studied using (pre)historical charcoal and pollen data from lake sediments and statistical data from the 20th century. The cultural impact on forest fire was established by correlating charcoal-influx data with pollen percentages of anthropogenic indicators such as Plantago lanceolata, the Cerealia (sum of Avena t., Triticum t. and Hordeum t.) and Secale. During the 20th century, fire frequency was correlated with precipitation, dry and very dry periods and landscape management indicators. The effects of human activity on the fire regime are clearly recognisable since at least the Neolithic period. Using palaeoecological or statistical data, the variations in fire regime originating from anthropogenic actions may be differentiated from those due to climatic changes if they are sufficiently conspicuous.

Local Human Activity in two communities of Formosa, Argentina

author must provide abstract

Efficiency of human activity on information spreading on Twitter

In this work we study Twitter data to understand influence dynamics in social networks. We define user efficiency on Twitter, as the ratio between the emergent spreading process and the activity employed by the user. We characterize this property by means of a quantitative analysis of the structural and dynamical patterns emergent from human interactions, and show it to be universal across several Twitter conversations.

Human activity recognition by inertial signals obtained from a smartphone

Human Activity Recognition (HAR) is an emerging research field with the aim to identify the actions carried out by a person given a set of observations and the surrounding environment. The wide growth in this research field inside the scientific community is mainly explained by the high number of applications that are arising in the last years. A great part of the most promising applications are related to the healthcare field, where it is possible to track the mobility of patients with motor dysfunction as also the physical activity in patients with cardiovascular risk. Until a few years ago, by using distinct kind of sensors, a patient follow-up was possible. However, far from being a long-term solution and with the smartphone irruption, that monitoring can be achieved in a non-invasive way by using the embedded smartphone’s sensors. For these reasons this Final Degree Project arises with the main target to evaluate new feature extraction techniques in order to carry out an activity and user recognition, and also an activity segmentation. The recognition is done thanks to the inertial signals integration obtained by two widespread sensors in the greater part of smartphones: accelerometer and gyroscope. In particular, six different activities are evaluated walking, walking-upstairs, walking-downstairs, sitting, standing and lying. Furthermore, a segmentation task is carried out taking into account the activities performed by thirty users. This can be done by using Hidden Markov Models and also a set of tools tested satisfactory in speech recognition: HTK (Hidden Markov Model Toolkit).

Human activity recognition by inertial signals obtained from a smartphone

El Reconocimiento de Actividades Humanas es un área de investigación emergente, cuyo objetivo principal es identificar las acciones realizadas por un sujeto analizando las señales obtenidas a partir de unos sensores. El rápido crecimiento de este área de investigación dentro de la comunidad científica se explica, en parte, por el elevado número de aplicaciones que están surgiendo en los últimos años. Gran parte de las aplicaciones más prometedoras se encuentran en el campo de la salud, donde se puede hacer un seguimiento del nivel de movilidad de pacientes con trastornos motores, así como monitorizar el nivel de actividad física en pacientes con riesgo cardiovascular. Hasta hace unos años, mediante el uso de distintos tipos de sensores se podía hacer un seguimiento del paciente. Sin embargo, lejos de ser una solución a largo plazo y gracias a la irrupción del teléfono inteligente, este seguimiento se puede hacer de una manera menos invasiva, haciendo uso de la gran variedad de sensores integrados en este tipo de dispositivos. En este contexto nace este Trabajo de Fin de Grado, cuyo principal objetivo es evaluar nuevas técnicas de extracción de características para llevar a cabo un reconocimiento de actividades y usuarios así como una segmentación de aquellas. Este reconocimiento se hace posible mediante la integración de señales inerciales obtenidas por dos sensores presentes en la gran mayoría de teléfonos inteligentes: acelerómetro y giróscopo. Concretamente, se evalúan seis tipos de actividades realizadas por treinta usuarios: andar, subir escaleras, bajar escaleras, estar sentado, estar de pie y estar tumbado. Además y de forma paralela, se realiza una segmentación temporal de los distintos tipos de actividades realizadas por dichos usuarios. Todo ello se llevará a cabo haciendo uso de los Modelos Ocultos de Markov, así como de un conjunto de herramientas probadas satisfactoriamente en reconocimiento del habla: HTK (Hidden Markov Model Toolkit).