Activity recognition using binary tree SVM

This paper presents an effective classification method based on Support Vector Machines (SVM) in the context of activity recognition. Local features that capture both spatial and temporal information in activity videos have made significant progress recently. Efficient and effective features, feature representation and classification plays a crucial role in activity recognition. For classification, SVMs are popularly used because of their simplicity and efficiency; however the common multi-class SVM approaches applied suffer from limitations including having easily confused classes and been computationally inefficient. We propose using a binary tree SVM to address the shortcomings of multi-class SVMs in activity recognition. We proposed constructing a binary tree using Gaussian Mixture Models (GMM), where activities are repeatedly allocated to subnodes until every new created node contains only one activity. Then, for each internal node a separate SVM is learned to classify activities, which significantly reduces the training time and increases the speed of testing compared to popular the `one-against-the-rest' multi-class SVM classifier. Experiments carried out on the challenging and complex Hollywood dataset demonstrates comparable performance over the baseline bag-of-features method.

A model of social work classification in health care

The introduction of casemix funding for Australian acute health care services has challenged Social Work to demonstrate clear reporting mechanisms, demonstrate effective practice and to justify interventions provided. The term 'casemix' is used to describe the mix and type of patients treated by a hospital or other health care services. There is wide acknowledgement that the procedure-based system of Diagnosis Related Groupings (DRGs) is grounded in a medical/illness perspective and is unsatisfactory in describing and predicting the activity of Social Work and other allied health professions in health care service delivery. The National Allied Health Casemix Committee was established in 1991 as the peak body to represent allied health professions in matters related to casemix classification. This Committee has pioneered a nationally consistent, patient-centred information system for allied health. This paper describes the classification systems and codes developed for Social Work, which includes a minimum data set, a classification hierarchy, the set of activity (input) codes and 'indicator for intervention' codes. The advantages and limitations of the system are also discussed.

CLASSIFICATION AND EVOLUTION OF ASIAN COLOBINES

In order to study the differentiation of Asian colobines, 14 variables measured on 123 skulls, including Rhinopithecus, Presbytis, Presbytiscus (Rhinopithecus avunculus), Pygathrix and Nasalis were analyzed by one-way, cluster and discriminant function analyses. Information on paleoenvironmental changes in China and southeast Asia since the late Tertiary was used to examine the influences of migratory routes and range of distribution in Asian colobines. A cladogram for 6 genera of Asian colobines was constructed from the results of various analyses. Some new points or revisions were suggested: (1) Following one of two migratory routes, ancient species of Asian colobines perhaps passed through Xizang (Tibet) along the northern bank of the Tethys sea and through the Heng Duan Shan regions of Yunnan into Vietnam. An ancient landmass linking Yunnan and Xizang was already present on the east bank of the Tethys sea. Accordingly, Asian colobines would have two centers of evolutionary origin: Sundaland and the Heng Duan Shan regions of China. (2) Pygathrix shares more cranial features with Presbytiscus than with Rhinopithecus. This differs somewhat from the conclusion reached by Groves. (3) Nasalis (karyotype: 2n = 48) may be the most primitive genus among Asian colobines. Certain features shared with Rhinopithecus, e.g. large body size, terrestrial activity and limb proportions, can be interpreted as symple-siomorphic characters. (4) Rhinopithecus, with respect to craniofacial features, is a special case among Asian colobines. It combines a high degree of evolutionary specialization with retention of some primitive features thought to have been present in the ancestral Asian colobine.

Comparison of manual and user guided methodologies for the classification and retrieval of construction site images

The amount of original imaging information produced yearly during the last decade has experienced a tremendous growth in all industries due to the technological breakthroughs in digital imaging and electronic storage capabilities. This trend is affecting the construction industry as well, where digital cameras and image databases are gradually replacing traditional photography. Owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks like monitoring an activity's progress and keeping evidence of the "as built" in case any disputes arise. So far, retrieval methodologies are done manually with the user being responsible for imaging classification according to specific rules that serve a limited number of construction management tasks. New methods that, with the guidance of the user, can automatically classify and retrieve construction site images are being developed and promise to remove the heavy burden of manually indexing images. In this paper, both the existing methods and a novel image retrieval method developed by the authors for the classification and retrieval of construction site images are described and compared. Specifically a number of examples are deployed in order to present their advantages and limitations. The results from this comparison demonstrates that the content based image retrieval method developed by the authors can reduce the overall time spent for the classification and retrieval of construction images while providing the user with the flexibility to retrieve images according different classification schemes.

Digital signal processing and artificial intelligence for the automated classification of food allergy

As a by-product of the ‘information revolution’ which is currently unfolding, lifetimes of man (and indeed computer) hours are being allocated for the automated and intelligent interpretation of data. This is particularly true in medical and clinical settings, where research into machine-assisted diagnosis of physiological conditions gains momentum daily. Of the conditions which have been addressed, however, automated classification of allergy has not been investigated, even though the numbers of allergic persons are rising, and undiagnosed allergies are most likely to elicit fatal consequences. On the basis of the observations of allergists who conduct oral food challenges (OFCs), activity-based analyses of allergy tests were performed. Algorithms were investigated and validated by a pilot study which verified that accelerometer-based inquiry of human movements is particularly well-suited for objective appraisal of activity. However, when these analyses were applied to OFCs, accelerometer-based investigations were found to provide very poor separation between allergic and non-allergic persons, and it was concluded that the avenues explored in this thesis are inadequate for the classification of allergy. Heart rate variability (HRV) analysis is known to provide very significant diagnostic information for many conditions. Owing to this, electrocardiograms (ECGs) were recorded during OFCs for the purpose of assessing the effect that allergy induces on HRV features. It was found that with appropriate analysis, excellent separation between allergic and nonallergic subjects can be obtained. These results were, however, obtained with manual QRS annotations, and these are not a viable methodology for real-time diagnostic applications. Even so, this was the first work which has categorically correlated changes in HRV features to the onset of allergic events, and manual annotations yield undeniable affirmation of this. Fostered by the successful results which were obtained with manual classifications, automatic QRS detection algorithms were investigated to facilitate the fully automated classification of allergy. The results which were obtained by this process are very promising. Most importantly, the work that is presented in this thesis did not obtain any false positive classifications. This is a most desirable result for OFC classification, as it allows complete confidence to be attributed to classifications of allergy. Furthermore, these results could be particularly advantageous in clinical settings, as machine-based classification can detect the onset of allergy which can allow for early termination of OFCs. Consequently, machine-based monitoring of OFCs has in this work been shown to possess the capacity to significantly and safely advance the current state of clinical art of allergy diagnosis

Energy efficiency in gait, activity, participation, and health status in children with cerebral palsy

The aim of the study was to establish if a relationship exists between the energy efficiency of gait, and measures of activity limitation, participation restriction, and health status in a representative sample of children with cerebral palsy (CP). Secondary aims were to investigate potential differences between clinical subtypes and gross motor classification, and to explore other relationships between the measures under investigation. A longitudinal study of a representative sample of 184 children with ambulant CP was conducted (112 males, 72 females; 94 had unilateral spastic C P, 84 had bilateral spastic C P, and six had non-spastic forms; age range 4-17y; Gross Motor Function Classification System Level I, n=57; Level II, n=91; Level III, n=22; and Level IV, n=14); energy efficiency (oxygen cost) during gait, activity limitation, participation restriction, and health status were recorded. Energy efficiency during gait was shown to correlate significantly with activity limitations; no relationship between energy efficiency during gait was found with either participation restriction or health status. With the exception of psychosocial health, all other measures showed significant differences by clinical subtype and gross motor classification. The energy efficiency of walking is not reflective of participation restriction or health status. Thus, therapies leading to improved energy efficiency may not necessarily lead to improved participation or general health.

Age-related changes in energy efficiency of gait, activity, and participation in children with cerebral palsy

Aim
The aim of this study was to use a prospective longitudinal study to describe age-related trends in energy efficiency during gait, activity, and participation in ambulatory children with cerebral palsy (CP).

Method
Gross Motor Function Measure (GMFM), Paediatric Evaluation of Disability Inventory (PEDI), and Lifestyle Assessment Questionnaire-Cerebral Palsy (LAQ-CP) scores, and energy efficiency (oxygen cost) during gait were assessed in representative sample of 184 children (112 male; 72 female; mean age 10y 9mo; range 4–16y) with CP. Ninety-four children had unilateral spastic CP, 84 bilateral spastic CP, and six had other forms of CP. Fifty-seven were classified as Gross Motor Function Classification System (GMFCS) level I, 91 as level II, 22 as level III, and 14 as level IV). Assessments were carried out on two occasions (visit 1 and visit 2) separated by an interval of 2 years and 7 months. A total of 157 participants returned for reassessment.

Results
Significant improvements in mean raw scores for GMFM, PEDI, and LAQ-CP were recorded; however, mean raw oxygen cost deteriorated over time. Age-related trends revealed gait to be most inefficient at the age of 12 years, but GMFM scores continued to improve until the age of 13 years, and two PEDI subscales to age 14 years, before deteriorating (p<0.05). Baseline score was consistently the single greatest predictor of visit 2 score. Substantial agreement in GMFCS ratings over time was achieved (?lw=0.74–0.76).

Interpretation
These findings have implications in terms of optimal provision and delivery of services for young people with CP to maximize physical capabilities and maintain functional skills into adulthood.

An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.

Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.

Rhythmic Masticatory Muscle Activity during Sleep: Etiology and Clinical Perspectives

L’activité rythmique des muscles masticateurs (ARMM) pendant le sommeil se retrouve chez environ 60% de la population générale adulte. L'étiologie de ce mouvement n'est pas encore complètement élucidée. Il est cependant démontré que l’augmentation de la fréquence des ARMM peut avoir des conséquences négatives sur le système masticatoire. Dans ce cas, l'ARMM est considérée en tant que manifestation d'un trouble moteur du sommeil connue sous le nom de bruxisme. Selon la Classification Internationale des Troubles du Sommeil, le bruxisme est décrit comme le serrement et grincement des dents pendant le sommeil. La survenue des épisodes d’ARMM est associée à une augmentation du tonus du système nerveux sympathique, du rythme cardiaque, de la pression artérielle et elle est souvent en association avec une amplitude respiratoire accrue. Tous ces événements peuvent être décrits dans le contexte d’un micro-éveil du sommeil. Cette thèse comprend quatre articles de recherche visant à étudier i) l'étiologie de l’ARMM pendant le sommeil en relation aux micro-éveils, et à évaluer ii) les aspects cliniques du bruxisme du sommeil, du point de vue diagnostique et thérapeutique. Pour approfondir l'étiologie de l’ARMM et son association avec la fluctuation des micro-éveils, nous avons analysé le patron cyclique alternant (ou cyclic alternating pattern (CAP) en anglais), qui est une méthode d’analyse qui permet d’évaluer l'instabilité du sommeil et de décrire la puissance des micro-éveils. Le CAP a été étudié chez des sujets bruxeurs et des sujets contrôles qui ont participé à deux protocoles expérimentaux, dans lesquels la structure et la stabilité du sommeil ont été modifiées par l'administration d'un médicament (la clonidine), ou avec l'application de stimulations sensorielles (de type vibratoire/auditif) pendant le sommeil. Dans ces deux conditions expérimentales caractérisées par une instabilité accrue du sommeil, nous étions en mesure de démontrer que les micro-éveils ne sont pas la cause ou le déclencheur de l’ARMM, mais ils représentent plutôt la «fenêtre permissive» qui facilite l'apparition de ces mouvements rythmiques au cours du sommeil. Pour évaluer la pertinence clinique du bruxisme, la prévalence et les facteurs de risque, nous avons effectué une étude épidémiologique dans une population pédiatrique (7-17 ans) qui était vue en consultation en orthodontie. Nous avons constaté que le bruxisme est un trouble du sommeil très fréquent chez les enfants (avec une prévalence de 15%), et il est un facteur de risque pour l'usure des dents (risque relatif rapproché, RRR 8,8), la fatigue des muscles masticateurs (RRR 10,5), les maux de tête fréquents (RRR 4,3), la respiration bruyante pendant le sommeil (RRR 3,1), et divers symptômes liés au sommeil, tels que la somnolence diurne (RRR 7,4). Ces résultats nous ont amenés à développer une étude expérimentale pour évaluer l'efficacité d'un appareil d'avancement mandibulaire (AAM) chez un groupe d'adolescents qui présentaient à la fois du bruxisme, du ronflement et des maux de tête fréquents. L'hypothèse est que dans la pathogenèse de ces comorbidités, il y a un mécanisme commun, probablement lié à la respiration pendant le sommeil, et que l'utilisation d'un AAM peut donc agir sur plusieurs aspects liés. À court terme, le traitement avec un AAM semble diminuer l'ARMM (jusqu'à 60% de diminution), et améliorer le ronflement et les maux de tête chez les adolescents. Cependant, le mécanisme d'action exact des AAM demeure incertain; leur efficacité peut être liée à l'amélioration de la respiration pendant le sommeil, mais aussi à l'influence que ces appareils pourraient avoir sur le système masticatoire. Les interactions entre le bruxisme du sommeil, la respiration et les maux de tête, ainsi que l'efficacité et la sécurité à long terme des AAM chez les adolescents, nécessitent des études plus approfondies.

Participation in leisure and physical activity among children and adolescents with juvenile idiopathic arthritis

Contexte: L’arthrite juvénile idiopathique (AJI) est l’une des maladies chroniques auto-immune les plus répandues chez les enfants et est caractérisée par des enflures articulaires (maladie active), de la douleur, de la fatigue et des raideurs matinales pouvant restreindre leur niveau de participation aux activités quotidiennes (par exemple: les loisirs, l’activité physique, la mobilité et les soins personnels) à la maison comme à l’école. Participer aux activités de loisirs et à l’activité physique a des bienfaits au niveau de la santé et du développement de tous les enfants et démontrent aussi des effets positifs qui réduisent les symptômes des maladies chroniques telle l’AJI. Malgré ces bienfaits la participation aux loisirs chez les jeunes avec l’AJI demeure largement sous-étudiée. Objectifs: Cette étude vise à évaluer le niveau de participation aux loisirs et à l’activité physique chez les enfants et les adolescents atteints d’AJI, ainsi qu’à identifier les facteurs liés à la maladie, la personne et l’environnement. Méthodes : L’évaluation du niveau de participation et l’exploration des facteurs associés aux loisirs et à l’activité physique ont été complétés par l’entremise d’une revue systématique de la littérature, l’analyse de données d’un échantillon national représentatif d’enfants canadiens atteints d’arthrite âgés entre 5 et 14 ans (npondéré = 4350), ainsi que l’analyse standardisée du niveau de participation aux loisirs à l’aide du Children’s Assessment of Participation and Enjoyment (n=107) et la mesure objective de l’activité physique par accéléromètre (n=76) auprès d’un échantillon d’enfants (âgés entre 8 et 11 ans ) et d’adolescents (âgés entre 12 et 17 ans) suivis en clinique de rhumatologie à l’hôpital de Montréal pour enfants, Centre Universitaire de Santé McGill. Les résultats cliniques ont été comparés à des données normatives, ainsi qu’à un groupe contrôle sans AJI. Nous avons exploré les facteurs associés avec le niveau de participation aux loisirs et à l’activité physique en utilisant les modèles de régression linéaire multiple et l’analyse hiérarchique. Résultats : Les enfants et les adolescents atteints d’AJI participent à une multitude d’activités de loisirs; cependant ils sont moins souvent impliqués dans des activités physiques et de raffinement en comparaison aux autres types d’activités de loisirs. Ceux avec l’AJI étaient en général moins actifs que leurs pairs sans arthrite et la plupart n’atteignaient pas les recommandations nationales d’activité physique. Les garçons avec l’AJI participent plus souvent à des activités physiques et moins aux activités sociales, de raffinement et de développement de soi en comparaison avec les filles ayant l’AJI. En général, être un garçon, être plus âgé, avoir une meilleure motivation pour participer aux activités de motricité globale, avoir un statut socio-économique plus élevé et être d’origine culturelle canadienne sont associés à un niveau de participation plus élevé aux activités physiques. La préférence pour les activités de raffinement, un niveau d’éducation maternelle plus élevé et être une fille étaient associés à un niveau de participation plus élevé aux activités de raffinement. Conclusion: La participation aux loisirs et à l’activité physique en AJI est un concept complexe et semble surtout être expliqué par des facteurs personnels et environnementaux. L’identification des facteurs associés aux loisirs et à l’activité physique est très importante en AJI puisqu’elle peut permettre aux professionnels de la santé de développer des interventions significatives basées sur les activités préférées des enfants, améliorer l’observance au traitement et promouvoir des habitudes de vie saine.

Analysis of intrinsic cardiac neuron activity in relation to neurogenic atrial fibrillation and vagal stimulation

La fibrillation auriculaire est le trouble du rythme le plus fréquent chez l'homme. Elle conduit souvent à de graves complications telles que l'insuffisance cardiaque et les accidents vasculaires cérébraux. Un mécanisme neurogène de la fibrillation auriculaire mis en évidence. L'induction de tachyarythmie par stimulation du nerf médiastinal a été proposée comme modèle pour étudier la fibrillation auriculaire neurogène. Dans cette thèse, nous avons étudié l'activité des neurones cardiaques intrinsèques et leurs interactions à l'intérieur des plexus ganglionnaires de l'oreillette droite dans un modèle canin de la fibrillation auriculaire neurogène. Ces activités ont été enregistrées par un réseau multicanal de microélectrodes empalé dans le plexus ganglionnaire de l'oreillette droite. L'enregistrement de l'activité neuronale a été effectué continument sur une période de près de 4 heures comprenant différentes interventions vasculaires (occlusion de l'aorte, de la veine cave inférieure, puis de l'artère coronaire descendante antérieure gauche), des stimuli mécaniques (toucher de l'oreillette ou du ventricule) et électriques (stimulation du nerf vague ou des ganglions stellaires) ainsi que des épisodes induits de fibrillation auriculaire. L'identification et la classification neuronale ont été effectuées en utilisant l'analyse en composantes principales et le partitionnement de données (cluster analysis) dans le logiciel Spike2. Une nouvelle méthode basée sur l'analyse en composante principale est proposée pour annuler l'activité auriculaire superposée sur le signal neuronal et ainsi augmenter la précision de l'identification de la réponse neuronale et de la classification. En se basant sur la réponse neuronale, nous avons défini des sous-types de neurones (afférent, efférent et les neurones des circuits locaux). Leur activité liée à différents facteurs de stress nous ont permis de fournir une description plus détaillée du système nerveux cardiaque intrinsèque. La majorité des neurones enregistrés ont réagi à des épisodes de fibrillation auriculaire en devenant plus actifs. Cette hyperactivité des neurones cardiaques intrinsèques suggère que le contrôle de cette activité pourrait aider à prévenir la fibrillation auriculaire neurogène. Puisque la stimulation à basse intensité du nerf vague affaiblit l'activité neuronale cardiaque intrinsèque (en particulier pour les neurones afférents et convergents des circuits locaux), nous avons examiné si cette intervention pouvait être appliquée comme thérapie pour la fibrillation auriculaire. Nos résultats montrent que la stimulation du nerf vague droit a été en mesure d'atténuer la fibrillation auriculaire dans 12 des 16 cas malgré un effet pro-arythmique défavorable dans 1 des 16 cas. L'action protective a diminué au fil du temps et est devenue inefficace après ~ 40 minutes après 3 minutes de stimulation du nerf vague.

A classification system for delirium subtyping with the use of a commercial mobility monitor

The usefulness of motor subtypes of delirium is unclear due to inconsistency in subtyping methods and a lack of validation with objective measures of activity. The activity of 40 patients was measured over 24 h with a commercial accelerometer-based activity monitor. Accelerometry data from patients with DSM-IV delirium that were readily divided into hyperactive, hypoactive and mixed motor subtypes, were used to create classification trees that were Subsequently applied to the remaining cohort to define motoric subtypes. The classification trees used the periods of sitting/lying, standing, stepping and number of postural transitions as measured by the activity monitor as determining factors from which to classify the delirious cohort. The use of a classification system shows how delirium subtypes can be categorised in relation to overall activity and postural changes, which was one of the most discriminating measures examined. The classification system was also implemented to successfully define other patient motoric subtypes. Motor subtypes of delirium defined by observed ward behaviour differ in electronically measured activity levels. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved.

A continuous wavelet transform and classification method for delirium motoric subtyping

The usefulness of motor subtypes of delirium is unclear due to inconsistency in subtyping methods and a lack of validation with objective measures of activity. The activity of 40 patients was measured over 24 h with a discrete accelerometer-based activity monitor. The continuous wavelet transform (CWT) with various mother wavelets were applied to accelerometry data from three randomly selected patients with DSM-IV delirium that were readily divided into hyperactive, hypoactive, and mixed motor subtypes. A classification tree used the periods of overall movement as measured by the discrete accelerometer-based monitor as determining factors for which to classify these delirious patients. This data used to create the classification tree were based upon the minimum, maximum, standard deviation, and number of coefficient values, generated over a range of scales by the CWT. The classification tree was subsequently used to define the remaining motoric subtypes. The use of a classification system shows how delirium subtypes can be categorized in relation to overall motoric behavior. The classification system was also implemented to successfully define other patient motoric subtypes. Motor subtypes of delirium defined by observed ward behavior differ in electronically measured activity levels.