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Avaliação da atividade de lesões de cárie em levantamentos epidemiológicos com crianças pré-escolares; Dental caries activity assessment in oral health epidemiological survey with preschool children

Piovesan, Chaiana
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 05/02/2013 PT
Relevância na Pesquisa
35.89%
O objetivo do presente estudo foi avaliar a magnitude da redução nos parâmetros de cárie após a inclusão da avaliação da atividade e investigar a associação dos fatores socioeconômicos e biológicos com o status de atividade das lesões de cárie em pré-escolares brasileiros. A pesquisa foi realizada em Santa Maria, Brasil, durante a Campanha Nacional de Multivacinação Infantil, e 639 crianças com idade entre 12 meses e 59 meses foram incluídas. Quinze examinadores avaliaram as crianças utilizando o International Caries Detection and Assessment System (ICDAS) e um critério adicional para avaliação da atividade das lesões. Um questionário estruturado foi aplicado aos responsáveis da criança para coletar informações relacionadas às características demográficas, socioeconômicas e biológicas. A média de dentes e superfícies cariadas e a prevalência de cárie foram calculadas inicialmente nos diferentes pontos de corte do ICDAS. A avaliação da atividade foi realizada, e as lesões inativas foram consideradas como hígidas na segunda análise. Posteriormente, os mesmos parâmetros de cárie, nos mesmos pontos de corte do ICDAS, foram recalculados. A redução nos parâmetros de cárie após a inclusão da atividade e o número de crianças que precisavam ser avaliadas com a finalidade de mudar sua classificação de cariada para hígida foram também calculados. Além disso...

The use of classification methods for modeling the antioxidant activity of flavonoid compounds

Weber, Karen C.; Honorio, Kathia M.; Bruni, Aline T.; da Silva, Alberico B. F.
Fonte: Springer Publicador: Springer
Tipo: Artigo de Revista Científica Formato: 915-920
ENG
Relevância na Pesquisa
45.84%
A study using two classification methods (SDA and SIMCA) was carried out in this work with the aim of investigating the relationship between the structure of flavonoid compounds and their free-radical-scavenging ability. In this work, we report the use of chemometric methods (SDA and SIMCA) able to select the most relevant variables (steric, electronic, and topological) responsible for this ability. The results obtained with the SDA and SIMCA methods agree perfectly with our previous model, in which we used other chemometric methods (PCA, HCA and KNN) and are also corroborated with experimental results from the literature. This is a strong indication of how reliable the selection of variables is.

A multi-modal approach for activity classification and fall detection

Castillo, José Carlos; Carneiro, Davide Rua; Serrano-Cuerda, Juan; Novais, Paulo; Fernández-Caballero, Antonio; Neves, José
Fonte: Taylor & Francis Publicador: Taylor & Francis
Tipo: Artigo de Revista Científica
Publicado em //2014 ENG
Relevância na Pesquisa
45.77%
"Special issue : Intelligent multisensory systems in support of information society"; The society is changing towards a new paradigm in which an increasing number of old adults live alone. In parallel, the incidence of conditions that affect mobility and independence is also rising as a consequence of a longer life expectancy. In this paper, the specific problem of falls of old adults is addressed by devising a technological solution for monitoring these users. Video cameras, accelerometers and GPS sensors are combined in a multi-modal approach to monitor humans inside and outside the domestic environment. Machine learning techniques are used to detect falls and classify activities from accelerometer data. Video feeds and GPS are used to provide location inside and outside the domestic environment. It results in a monitoring solution that does not imply the confinement of the users to a closed environment.; This work is funded by National Funds through the FCT-Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project PEst- OE/EEI/UI0752/2011. The work of Davide Carneiro is also supported by a doctoral grant by FCT (SFRH/BD/64890/2009). This work is also partially supported by the Spanish Ministerio de Economía y Competitividad / FEDER under project TIN2010-20845-C03-01.

Hierarchical, Multi-Sensor Based Classification of Daily Life Activities: Comparison with State-of-the-Art Algorithms Using a Benchmark Dataset

Leutheuser, Heike; Schuldhaus, Dominik; Eskofier, Bjoern M.
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 09/10/2013 EN
Relevância na Pesquisa
36.01%
Insufficient physical activity is the 4th leading risk factor for mortality. Methods for assessing the individual daily life activity (DLA) are of major interest in order to monitor the current health status and to provide feedback about the individual quality of life. The conventional assessment of DLAs with self-reports induces problems like reliability, validity, and sensitivity. The assessment of DLAs with small and light-weight wearable sensors (e.g. inertial measurement units) provides a reliable and objective method. State-of-the-art human physical activity classification systems differ in e.g. the number and kind of sensors, the performed activities, and the sampling rate. Hence, it is difficult to compare newly proposed classification algorithms to existing approaches in literature and no commonly used dataset exists. We generated a publicly available benchmark dataset for the classification of DLAs. Inertial data were recorded with four sensor nodes, each consisting of a triaxial accelerometer and a triaxial gyroscope, placed on wrist, hip, chest, and ankle. Further, we developed a novel, hierarchical, multi-sensor based classification system for the distinction of a large set of DLAs. Our hierarchical classification system reached an overall mean classification rate of 89.6% and was diligently compared to existing state-of-the-art algorithms using our benchmark dataset. For future research...

Prediction Models Discriminating between Nonlocomotive and Locomotive Activities in Children Using a Triaxial Accelerometer with a Gravity-removal Physical Activity Classification Algorithm

Hikihara, Yuki; Tanaka, Chiaki; Oshima, Yoshitake; Ohkawara, Kazunori; Ishikawa-Takata, Kazuko; Tanaka, Shigeho
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 22/04/2014 EN
Relevância na Pesquisa
45.81%
The aims of our study were to examine whether a gravity-removal physical activity classification algorithm (GRPACA) is applicable for discrimination between nonlocomotive and locomotive activities for various physical activities (PAs) of children and to prove that this approach improves the estimation accuracy of a prediction model for children using an accelerometer. Japanese children (42 boys and 26 girls) attending primary school were invited to participate in this study. We used a triaxial accelerometer with a sampling interval of 32 Hz and within a measurement range of ±6 G. Participants were asked to perform 6 nonlocomotive and 5 locomotive activities. We measured raw synthetic acceleration with the triaxial accelerometer and monitored oxygen consumption and carbon dioxide production during each activity with the Douglas bag method. In addition, the resting metabolic rate (RMR) was measured with the subject sitting on a chair to calculate metabolic equivalents (METs). When the ratio of unfiltered synthetic acceleration (USA) and filtered synthetic acceleration (FSA) was 1.12, the rate of correct discrimination between nonlocomotive and locomotive activities was excellent, at 99.1% on average. As a result, a strong linear relationship was found for both nonlocomotive (METs = 0.013×synthetic acceleration +1.220...

Energy-aware Activity Classification using Wearable Sensor Networks

Dong, Bo; Montoye, Alexander; Moore, Rebecca; Pfeiffer, Karin; Biswas, Subir
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
45.85%
This paper presents implementation details, system characterization, and the performance of a wearable sensor network that was designed for human activity analysis. Specific machine learning mechanisms are implemented for recognizing a target set of activities with both out-of-body and on-body processing arrangements. Impacts of energy consumption by the on-body sensors are analyzed in terms of activity detection accuracy for out-of-body processing. Impacts of limited processing abilities for the on-body scenario are also characterized in terms of detection accuracy, by varying the background processing load in the sensor units. Impacts of varying number of sensors in terms of activity classification accuracy are also evaluated. Through a rigorous systems study, it is shown that an efficient human activity analytics system can be designed and operated even under energy and processing constraints of tiny on-body wearable sensors.

In silico Antibacterial Activity Modeling Based on the TOMOCOMD-CARDD Approach

Castillo-Garit,Juan A.; Marrero-Ponce,Yovani; Barigye,Stephen J.; Medina-Marrero,Ricardo; Bernal,Milagros G.; Vega,José M. G. de la; Torrens,Francisco; Arán,Vicente J.; Pérez-Giménez,Facundo; García-Domenech,Ramón; Acevedo-Barrios,Rosa
Fonte: Sociedade Brasileira de Química Publicador: Sociedade Brasileira de Química
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/06/2015 EN
Relevância na Pesquisa
35.92%
In the recent times, the race to cope with the increasing multidrug resistance of pathogenic bacteria has lost much of its momentum and health professionals are grasping for solutions to deal with the unprecedented resistance levels. As a result, there is an urgent need for a concerted effort towards the development of new antimicrobial drugs to stay ahead in the fight against the ever adapting bacteria. In the present report, antibacterial classification functions (models) based on the topological molecular computational design-computer aided ‘‘rational’’ drug design (TOMOCOMD-CARDD) atom-based non-stochastic and stochastic bilinear indices are presented. These models were built using the linear discriminant analysis (LDA) method over a balanced chemical compounds dataset of 2230 molecular structures, with a diverse range of structural and molecular mechanism modes. The results of this study indicated that the non-stochastic and stochastic bilinear indices provided excellent classification of the chemical compounds (with accuracies of 86.31% and 84.92%, respectively, in the training set). These models were further externally validated yielding correct classification percentages of 86.55% and 87.91% for the non-stochastic and stochastic bilinear models...

INERTIAL SENSORS FOR KINEMATIC MEASUREMENT AND ACTIVITY CLASSIFICATION OF GAIT POST-STROKE

Laudanski, ANNEMARIE
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado
EN; EN
Relevância na Pesquisa
55.84%
The ability to walk and negotiate stairs is an important predictor of independent ambulation. The superposition of mobility impairments to the effects of natural aging in persons with stroke render the completion of many daily activities unsafe, thus limiting individuals’ independence within their communities. Currently however, no means exist for the monitoring of mobility levels during daily living in survivors after the completion of rehabilitation programs. The application of inertial sensors for stroke survivors could provide a basis for the study of gait outside of traditional laboratory settings. The main objective of this thesis was to evaluate the performance of inertial sensors in measuring gait of hemiparetic stroke survivors through the completion of three studies. The first study explored the use of inertial measurement units (IMUs) for the measurement of lower limb joint kinematics during stair ascent and descent in both stroke survivors and healthy older adults. Results suggested that IMUs were suitable for the measurement of lower limb range of motion in both healthy and post-stroke subjects during stair ambulation. The second study evaluated the measurement of step length and spatial symmetry during overground walking using IMUs. A systematic error resulting in the underestimation of step lengths calculated using IMUs compared with those measured using video analysis was found...

The Epidemiology of Physical Activity in Canada

BRYAN, SHIRLEY
Fonte: Quens University Publicador: Quens University
Tipo: Tese de Doutorado Formato: 1697005 bytes; application/pdf
EN; EN
Relevância na Pesquisa
35.9%
The four studies of this thesis provide an overview of the epidemiology of physical activity in Canada. In the first study two methods of coding activities used in estimating leisure-time physical activity energy expenditure (LTPAEE), from a questionnaire including 21 specific activities, and up to three “other” activities were compared. The authors assessed whether the assignment of activity intensity for “other” activities has an effect on LTPAEE and the classification of respondents as physically active versus inactive. The results indicate that the population classification of activity level is not affected by the intensity code; however, individual level LTPAEE is under-estimated from light and vigorous activities and over-estimated from moderate activities using the current method. In study two the proportion of Canadians meeting Canada’s physical activity guidelines for moderate and vigorous activities was estimated. The prevalence of adults reporting no activity has not changed since 1994/95 and the prevalence of meeting the guidelines has increased by about 11%. Men, younger adults, those with higher income and lower body mass index (BMI) meet the guidelines more often than their peers. The epidemiology of walking among Canadians between 1994 and 2007 was assessed in the third study. Walking was the most popular activity...

Classification of Sporting Activities Using Smartphone Accelerometers

Mitchell, Edmond; Monaghan, David; O'Connor, Noel E.
Fonte: Molecular Diversity Preservation International (MDPI) Publicador: Molecular Diversity Preservation International (MDPI)
Tipo: Artigo de Revista Científica
Publicado em 19/04/2013 EN
Relevância na Pesquisa
35.91%
In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today's society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers...

Sensor Data Acquisition and Processing Parameters for Human Activity Classification

Bersch, Sebastian D.; Azzi, Djamel; Khusainov, Rinat; Achumba, Ifeyinwa E.; Ries, Jana
Fonte: MDPI Publicador: MDPI
Tipo: Artigo de Revista Científica
Publicado em 04/03/2014 EN
Relevância na Pesquisa
35.91%
It is known that parameter selection for data sampling frequency and segmentation techniques (including different methods and window sizes) has an impact on the classification accuracy. For Ambient Assisted Living (AAL), no clear information to select these parameters exists, hence a wide variety and inconsistency across today's literature is observed. This paper presents the empirical investigation of different data sampling rates, segmentation techniques and segmentation window sizes and their effect on the accuracy of Activity of Daily Living (ADL) event classification and computational load for two different accelerometer sensor datasets. The study is conducted using an ANalysis Of VAriance (ANOVA) based on 32 different window sizes, three different segmentation algorithm (with and without overlap, totaling in six different parameters) and six sampling frequencies for nine common classification algorithms. The classification accuracy is based on a feature vector consisting of Root Mean Square (RMS), Mean, Signal Magnitude Area (SMA), Signal Vector Magnitude (here SMV), Energy, Entropy, FFTPeak, Standard Deviation (STD). The results are presented alongside recommendations for the parameter selection on the basis of the best performing parameter combinations that are identified by means of the corresponding Pareto curve.

Pre-Processing Effect on the Accuracy of Event-Based Activity Segmentation and Classification through Inertial Sensors

Fida, Benish; Bernabucci, Ivan; Bibbo, Daniele; Conforto, Silvia; Schmid, Maurizio
Fonte: MDPI Publicador: MDPI
Tipo: Artigo de Revista Científica
Publicado em 11/09/2015 EN
Relevância na Pesquisa
35.95%
Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. For monitoring and fitness applications, it is crucial to develop methods able to segment each activity cycle, e.g., a gait cycle, so that the successive classification step may be more accurate. To increase detection accuracy, pre-processing is often used, with a concurrent increase in computational cost. In this paper, the effect of pre-processing operations on the detection and classification of locomotion activities was investigated, to check whether the presence of pre-processing significantly contributes to an increase in accuracy. The pre-processing stages evaluated in this study were inclination correction and de-noising. Level walking, step ascending, descending and running were monitored by using a shank-mounted inertial sensor. Raw and filtered segments, obtained from a modified version of a rule-based gait detection algorithm optimized for sequential processing, were processed to extract time and frequency-based features for physical activity classification through a support vector machine classifier. The proposed method accurately detected >99% gait cycles from raw data and produced >98% accuracy on these segmented gait cycles. Pre-processing did not substantially increase classification accuracy...

Activity classification with smart phones for sports activities

Taylor, Ken; Abdulla, Umran A.; Helmer, Richard J N; Lee, Jungoo; Blanchonette, Ian
Fonte: Conference Organising Committee Publicador: Conference Organising Committee
Tipo: Conference paper
Relevância na Pesquisa
45.77%
Activity classification using mobile phones is useful for identifying training activities, then capturing short periods of high frequency training data and capturing and archiving appropriate training statistics for various training activities. Some avail

Novel Methods for Activity Classification and Occupany Prediction Enabling Fine-grained HVAC Control

Rana, Rajib; Kusy, Brano; Wall, Josh; Hu, Wen
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 05/09/2014
Relevância na Pesquisa
45.85%
Much of the energy consumption in buildings is due to HVAC systems, which has motivated several recent studies on making these systems more energy- efficient. Occupancy and activity are two important aspects, which need to be correctly estimated for optimal HVAC control. However, state-of-the-art methods to estimate occupancy and classify activity require infrastructure and/or wearable sensors which suffers from lower acceptability due to higher cost. Encouragingly, with the advancement of the smartphones, these are becoming more achievable. Most of the existing occupancy estimation tech- niques have the underlying assumption that the phone is always carried by its user. However, phones are often left at desk while attending meeting or other events, which generates estimation error for the existing phone based occupancy algorithms. Similarly, in the recent days the emerging theory of Sparse Random Classifier (SRC) has been applied for activity classification on smartphone, however, there are rooms to improve the on-phone process- ing. We propose a novel sensor fusion method which offers almost 100% accuracy for occupancy estimation. We also propose an activity classifica- tion algorithm, which offers similar accuracy as of the state-of-the-art SRC algorithms while offering 50% reduction in processing.

Accelerometer based Activity Classification with Variational Inference on Sticky HDP-SLDS

Basbug, Mehmet Emin; Ozcan, Koray; Velipasalar, Senem
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 19/10/2015
Relevância na Pesquisa
45.81%
As part of daily monitoring of human activities, wearable sensors and devices are becoming increasingly popular sources of data. With the advent of smartphones equipped with acceloremeter, gyroscope and camera; it is now possible to develop activity classification platforms everyone can use conveniently. In this paper, we propose a fast inference method for an unsupervised non-parametric time series model namely variational inference for sticky HDP-SLDS(Hierarchical Dirichlet Process Switching Linear Dynamical System). We show that the proposed algorithm can differentiate various indoor activities such as sitting, walking, turning, going up/down the stairs and taking the elevator using only the acceloremeter of an Android smartphone Samsung Galaxy S4. We used the front camera of the smartphone to annotate activity types precisely. We compared the proposed method with Hidden Markov Models with Gaussian emission probabilities on a dataset of 10 subjects. We showed that the efficacy of the stickiness property. We further compared the variational inference to the Gibbs sampler on the same model and show that variational inference is faster in one order of magnitude.

Entropy-based Classification of 'Retweeting' Activity on Twitter

Ghosh, Rumi; Surachawala, Tawan; Lerman, Kristina
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 01/06/2011
Relevância na Pesquisa
35.97%
Twitter is used for a variety of reasons, including information dissemination, marketing, political organizing and to spread propaganda, spamming, promotion, conversations, and so on. Characterizing these activities and categorizing associated user generated content is a challenging task. We present a information-theoretic approach to classification of user activity on Twitter. We focus on tweets that contain embedded URLs and study their collective `retweeting' dynamics. We identify two features, time-interval and user entropy, which we use to classify retweeting activity. We achieve good separation of different activities using just these two features and are able to categorize content based on the collective user response it generates. We have identified five distinct categories of retweeting activity on Twitter: automatic/robotic activity, newsworthy information dissemination, advertising and promotion, campaigns, and parasitic advertisement. In the course of our investigations, we have shown how Twitter can be exploited for promotional and spam-like activities. The content-independent, entropy-based activity classification method is computationally efficient, scalable and robust to sampling and missing data. It has many applications...

Aperture Effects on Spectroscopic Galaxy Activity Classification

Maragkoudakis, A.; Zezas, A.; Ashby, M. L. N.; Willner, S. P.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 02/04/2014
Relevância na Pesquisa
45.9%
Activity classification of galaxies based on long-slit and fiber spectroscopy can be strongly influenced by aperture effects. Here we investigate how activity classification for 14 nearby galaxies depends on the proportion of the host galaxy's light that is included in the aperture. We use both observed long-slit spectra and simulated elliptical-aperture spectra of different sizes. The degree of change varies with galaxy morphology and nuclear activity type. Starlight removal techniques can mitigate but not remove the effect of host galaxy contamination in the nuclear aperture. Galaxies with extra-nuclear star formation can show higher [O III] {\lambda}5007/H{\beta} ratios with increasing aperture, in contrast to the naive expectation that integrated light will only dilute the nuclear emission lines. We calculate the mean dispersion for the diagnostic line ratios used in the standard BPT diagrams with respect to the central aperture of spectral extraction to obtain an estimate of the uncertainties resulting from aperture effects.; Comment: 16 pages, 10 figures, 2 tables. Accepted for pubblication in MNRAS

Frequency Domain Approach for Activity Classification using Accelerometer

Chung, Wan-Young; Purwar, Amit; Sharma, Annapurna
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 22/07/2011
Relevância na Pesquisa
45.93%
Activity classification was performed using MEMS accelerometer and wireless sensor node for wireless sensor network environment. Three axes MEMS accelerometer measures body's acceleration and transmits measured data with the help of sensor node to base station attached to PC. On the PC, real time accelerometer data is processed for movement classifications. In this paper, Rest, walking and running are the classified activities of the person. Both time and frequency analysis was performed to classify running and walking. The classification of rest and movement is done using Signal magnitude area (SMA). The classification accuracy for rest and movement is 100%. For the classification of walk and Run two parameters i.e. SMA and Median frequency were used. The classification accuracy for walk and running was detected as 81.25% in the experiments performed by the test persons.; Comment: 30th Annual International IEEE EMBS Conference, Vancouver, British Columbia, Canada, August 20-24, 2008

The CR-Ω+ Classification Algorithm for Spatio-Temporal Prediction of Criminal Activity

Godoy-Calderón,S.; Calvo,H.; Martínez-Hernández,V. M.; Moreno-Armendáriz,M. A.
Fonte: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico Publicador: UNAM, Centro de Ciencias Aplicadas y Desarrollo Tecnológico
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/04/2010 EN
Relevância na Pesquisa
35.9%
We present a spatio-temporal prediction model that allows forecasting of the criminal activity behavior in a particular region by using supervised classification. The degree of membership of each pattern is interpreted as the forecasted increase or decrease in the criminal activity for the specified time and location. The proposed forecasting model (CR-Ω+) is based on the family of Kora-Q Logical-Combinatorial algorithms operating on large data volumes from several heterogeneous sources using an inductive learning process. We propose several modifications to the original algorithms by Bongard and Baskakova and Zhuravlëv which improve the prediction performance on the studied dataset of criminal activity. We perform two analyses: punctual prediction and tendency analysis, which show that it is possible to predict punctually one of four crimes to be perpetrated (crime family, in a specific space and time), and 66% of effectiveness in the prediction of the place of crime, despite of the noise of the dataset. The tendency analysis yielded an STRMSE (Spatio-Temporal RMSE) of less than 1.0.

A hierarchical classification of trophic guilds for North American birds and mammals

González-Salazar,Constantino; Martínez-Meyer,Enrique; López-Santiago,Guadalupe
Fonte: Instituto de Biología Publicador: Instituto de Biología
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/01/2014 EN
Relevância na Pesquisa
35.92%
The identification and analysis of ecological guilds have been fundamental to understand the processes that determine the structure and organization of communities. However, reviewing studies that have tried to categorize species into trophic guilds we found many different criteria on which such categorizations are based; consequently, a single species may have several guild designations, limiting its accuracy and applicability. In this paper we propose a classification scheme for trophic guilds as a first step to establish a common terminology. For this purpose we considered 1502 species of mainland birds and mammals from North America (Mexico, USA, and Canada). This classification takes into account 3 main criteria to identify each guild: main food type, foraging substrate and activity period. To determine the trophic guilds and assign species to them, we performed a cluster analysis to classify species according to their similarities in feeding patterns. The resulting hierarchical classification distinguishes 6 main levels of organization, which may occur in different combinations among taxonomic groups and sites: 1) taxon (e. g., birds or mammal), 2) diet (e. g. granivore, insectivore), 3) foraging habitat (e. g., terrestrial, arboreal)...