Les auteurs reconnaissent Evan Beisheim, Nicole Capela, Andrew Herbert-Copley pour l&#39;assistance technique et la collecte de données. Le financement du projet a été reçue du Conseil de recherches en génie du Canada (CRSNG) en sciences naturelles et BlackBerry Ltd, y compris les smartphones utilisés dans l&#39;étude.

Ce protocole a été approuvé par le Comité d&#39;éthique de la recherche en santé d&#39;Ottawa Science Network. 1. Préparation <ol><li> Fournir aux participants un aperçu de la recherche, répondre à vos questions, et obtenir le consentement éclairé. Les caractéristiques des participants d&#39;enregistrement (par exemple, âge, sexe, taille, poids, tour de taille, la hauteur des pieds de l&#39;épine iliaque antéro-supérieure de la malléole interne), code d&#39;identification, et la date sur une feuille de données. Assurez-vous que le second smartphone qui est utilisé pour capturer de la vidéo est fixé à au moins 30 images par seconde taux de capture. </li><li> Attacher solidement un étui de téléphone à la ceinture avant droite ou pantalon taille du participant. Démarrez l&#39;application smartphone qui sera utilisé pour collecter les données de capteur (par exemple, l&#39;exploitation forestière de données ou application WMMS) sur le smartphone de mesure de la mobilité et de veiller à ce que l&#39;application fonctionne correctement. Placez le smartphone dans l&#39;étui, avec le dos de l&#39;appareil (à l&#39;arrière camera) vers l&#39;extérieur. </li><li> Début de l&#39;enregistrement vidéo numérique sur un deuxième smartphone. Pour l&#39;anonymat, enregistrer la vidéo de comparaison sans montrer le visage de la personne, mais veiller à enregistrer toutes les transitions d&#39;activité. Le téléphone peut être portatif. </li></ol> 2. Activité Circuit <ol><li> Suivez le participant et vidéo à leurs actions, sur le deuxième smartphone, pendant qu&#39;ils effectuent les actions suivantes, prononcées par l&#39;enquêteur: <ol><li> À partir d&#39;une position debout, secouer le smartphone pour indiquer le début du procès. </li><li> Continuer debout pendant au moins 10 secondes. Cette phase debout peut être utilisé pour l&#39;orientation de téléphone étalonnage 14. </li><li> Marchez à une chaise à proximité et asseoir. </li><li> Levez-vous et marchez 60 mètres à un ascenseur. </li><li> Levez-vous et attendre l&#39;ascenseur, puis marcher dans l&#39;ascenseur. </li><li> Prenez l&#39;ascenseur jusqu&#39;au deuxième étage. </li><li> Tournez et marcher dans l&#39;environnement de la maison. </li><li> Promenez-vous dans la salle de bains et SiMulate se brosser les dents. </li><li> Simuler peigner les cheveux. </li><li> Simuler le lavage des mains. </li><li> Séchez vos mains avec une serviette. </li><li> Marche pour la cuisine. </li><li> Prenez des plats à partir d&#39;un rack et placez-les sur le comptoir. </li><li> Remplir une bouilloire avec de l&#39;eau de l&#39;évier de la cuisine. </li><li> Placer la bouilloire sur l&#39;élément de fourneau. </li><li> Placez le pain dans un grille-pain. </li><li> Marche pour la salle à manger. </li><li> Asseyez-vous à une table de salle à manger. </li><li> Simuler un repas à la table. </li><li> Se tenir debout et marcher de nouveau à l&#39;évier de cuisine. </li><li> Rincez les plats et les placer dans un rack. </li><li> Marche de la cuisine vers l&#39;ascenseur. </li><li> Levez-vous et attendre l&#39;ascenseur, puis marcher dans l&#39;ascenseur. </li><li> Prenez l&#39;ascenseur jusqu&#39;au premier étage. </li><li> Marcher 50 mètres pour une cage d&#39;escalier. </li><li> Ouvrez la porte et entrez la cage d&#39;escalier. </li><li> Monter des escaliers (13 étapes, autour de l&#39;atterrissage, 13 étapes). </li><li> Ouvrez la porte de la cage d&#39;escalier dans le couloir. </li><li> Tournez à droite et marcher dans le couloir de 15 mètres. </li><li> Tourner autour et marcher 15 mètres en arrière de la cage d&#39;escalier. </li><li> Ouvrez la porte et entrez la cage d&#39;escalier. </li><li> Descendez les escaliers (13 étapes, autour de l&#39;atterrissage, 13 étapes). </li><li> Sortez de la cage d&#39;escalier et marcher dans une pièce. </li><li> Allongez-vous sur un lit. </li><li> Se lever et marcher 10 mètres pour une rampe. </li><li> Marcher jusqu&#39;à la rampe, tourner autour, puis vers le bas de la rampe (20 mètres). </li><li> Continuez à marcher dans le couloir et ouvrez la porte vers l&#39;extérieur. </li><li> Marcher 100 mètres sur la voie pavée. </li><li> Demi-tour et revenir à pied à la salle. </li><li> Marchez dans la salle et se tenir au point de départ. </li><li> Continuer debout, puis secouer le smartphone pour indiquer la fin du procès. </li></ol></li></ol> 3. fin de l&#39;essai <ol><li> Arrêtez le smartphone d&#39;enregistrement vidéo et de demander au participant de retirer et de retourner le smartphone et l&#39;étui. Arrêter l&#39;enregistrement de données ou application WMMS sur le SMArtphone. Copiez les fichiers de données de mouvement acquis et le fichier vidéo de deux téléphones à un ordinateur pour le post-traitement. </li></ol> 4. Post-traitement <ol><li> Synchroniser la synchronisation entre la vidéo et les données de capteur brutes en déterminant le moment où l&#39;action d&#39;agitation a commencé. Ce mouvement d&#39;agitation correspond à une trame de signal de l&#39;accéléromètre et de la vidéo distincte. Vérifiez erreur de synchronisation en soustrayant le temps de bougé de l&#39;extrémité de l&#39;heure de début de bougé, pour les sources de données de capteurs et vidéo. Les différences de temps devrait être similaire entre les deux ensembles de données. </li><li> Déterminer les temps de la vidéo de l&#39;étalon-or réelles de changement d&#39;état par l&#39;enregistrement de la différence de temps entre l&#39;heure de début de l&#39;agitation pour la trame vidéo à la transition entre les activités. Utilisez un logiciel de montage vidéo pour obtenir le calendrier à l&#39;intérieur de 0,033 sec (soit 30 images par seconde vidéo). Utilisez un logiciel pour générer WMMS changements d&#39;état comparables à partir des données de capteurs. </li><li> Générer deux ensembles de données, l&#39;uneavec des activités vrai et le deuxième avec les activités prévues, par marquage de l&#39;activité pour chaque trame vidéo (sur la base du changement d&#39;état de synchronisation), puis en calculant l&#39;activité prévue à chaque fois à partir de la sortie WMMS de trame vidéo. Pour l&#39;évaluation de la performance WMMS, calculer vrais positifs, faux négatifs, vrais négatifs, faux positifs entre l&#39;activité de l&#39;étalon-or et WMMS prévu une activité. Utilisez ces paramètres pour calculer les résultats sensibilité, la spécificité, et F-scores mesures. Note: Un réglage de tolérance de 3 fenêtres de données de chaque côté de la fenêtre en cours d&#39;analyse peut être utilisée pour la détermination des résultats de changement de l&#39;état, et de 2 fenêtres de données pour les résultats de classification. Par exemple, depuis 1 seconde fenêtres de données ont été utilisés pour la WMMS dans cette étude, 3 secondes avant et après la fenêtre en cours ont été examinés afin que les changements consécutifs à l&#39;intérieur de cette tolérance sont ignorés. La contrepartie a été que les changements d&#39;état qui se produisent en moins de 3 secondes peut être ignorée de HUMA brutn analyse de mouvement puisque ces Etats serait considérée comme transitoire. </li></ol>

<ol>
	<li>Lara, O. D., Labrador, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Survey+on+Human+Activity+Recognition+using+Wearable+Sensors.">A Survey on Human Activity Recognition using Wearable Sensors.</a> IEEE Communications Surveys Tutorials. 15 (3), 1192-1209 (2013).</li><li>Foerster, F., Smeja, M., Fahrenberg, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detection+of+posture+and+motion+by+accelerometry:+a+validation+study+in+ambulatory+monitoring.">Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring.</a> Computers in Human Behavior. 15 (5), 571-583 (1999).</li><li>Elsmore, T. F., Naitoh, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Monitoring Activity With a Wrist-Worn Actigraph: Effects of Amplifier Passband and threshold Variations. , (1993).</li><li>Kripke, D. F., Webster, J. B., Mullaney, D. J., Messin, S., Mason, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Measuring sleep by wrist actigraph. , (1981).</li><li>Lockhart, J. W., Pulickal, T., Weiss, G. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Applications+of+mobile+activity+recognition.">Applications of mobile activity recognition.</a> Proceedings of the 2012 ACM Conference on Ubiquitous Computing. , 1054-1058 (2012).</li><li>Incel, O. D., Kose, M., Ersoy, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Review+and+Taxonomy+of+Activity+Recognition+on+Mobile+Phones.">A Review and Taxonomy of Activity Recognition on Mobile Phones.</a> BioNanoScience. 3 (2), 145-171 (2013).</li><li>Yang, C. C., Hsu, Y. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Review+of+Accelerometry-Based+Wearable+Motion+Detectors+for+Physical+Activity+Monitoring.">A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring.</a> Sensors. 10 (8), 7772-7788 (2010).</li><li>He, Y., Li, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Physical+Activity+Recognition+Utilizing+the+Built-In+Kinematic+Sensors+of+a+Smartphone.">Physical Activity Recognition Utilizing the Built-In Kinematic Sensors of a Smartphone.</a> International Journal of Distributed Sensor Networks. 2013, (2013).</li><li>Vo, Q. V., Hoang, M. T., Choi, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Personalization+in+Mobile+Activity+Recognition+System+using+K-medoids+Clustering+Algorithm.">Personalization in Mobile Activity Recognition System using K-medoids Clustering Algorithm.</a> International Journal of Distributed Sensor Networks. 2013, (2013).</li><li>Dernbach, S., Das, B., Krishnan, N. C., Thomas, B. L., Cook, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Simple+and+Complex+Activity+Recognition+through+Smart+Phones.">Simple and Complex Activity Recognition through Smart Phones.</a> , 214-221 (2012).</li><li>Hache, G., Lemaire, E. D., Baddour, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mobility+change-of-state+detection+using+a+smartphone-based+approach.">Mobility change-of-state detection using a smartphone-based approach.</a> IEEE International Workshop on Medical Measurements and Applications Proceedings (MeMeA). , 43-46 (2010).</li><li>Wu, H. H., Lemaire, E. D., Baddour, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Change-of-state+determination+to+recognize+mobility+activities+using+a+BlackBerry+smartphone.">Change-of-state determination to recognize mobility activities using a BlackBerry smartphone.</a> , 5252-5255 (2011).</li><li>Wu, H., Lemaire, E. D., Baddour, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Activity+Change-of-state+Identification+Using+a+Blackberry+Smartphone.">Activity Change-of-state Identification Using a Blackberry Smartphone.</a> Journal of Medical and Biological Engineering. 32 (4), 265-271 (2012).</li><li>Tundo, M. D., Lemaire, E., Baddour, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Correcting+Smartphone+orientation+for+accelerometer-based+analysis.">Correcting Smartphone orientation for accelerometer-based analysis.</a> IEEE International Symposium on Medical Measurements and Applications Proceedings (MeMeA). , 58-62 (2013).</li><li>Tundo, M. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Development of a human activity recognition system using inertial measurement unit sensors on a smartphone. , (2014).</li></ol>

The authors declare that they have no competing financial interests.

La reconnaissance de l&#39;activité humaine avec un système de surveillance de la mobilité portable a reçu plus d&#39;attention au cours des dernières années en raison des progrès techniques dans l&#39;informatique et les smartphones portable et systématique des besoins pour les mesures des résultats quantitatifs qui aident à la prise de décision clinique et l&#39;évaluation des interventions en santé. La méthodologie décrite dans le présent document a été efficace pour évaluer le développement WMMS ...

Détection Ubiquitous est devenue un domaine de recherche engageante raison de plus en plus puissant, petit, faible coût de l&#39;informatique et de l&#39;équipement de détection 1. Suivi de la mobilité en utilisant les capteurs portables a suscité beaucoup d&#39;intérêt depuis la microélectronique au niveau des consommateurs sont capables de détecter des caractéristiques de mouvement avec une grande précision 1. La reconnaissance de l&#39;activité humaine (HAR) en utilisant les capteurs portables est une zone récente de la recherche, des études préliminaires effectuées dans les années 1980 et 1990 2 - 4. Smartphones modernes contiennent les capteurs nécessaires et de la capacité de calcul en temps réel pour la reconnaissance de l&#39;activité de mobilité. Analyse en temps réel sur le dispositif permet la classification de l&#39;activité et le téléchargement de données sans intervention de l&#39;utilisateur ou enquêteur. Un smartphone avec le logiciel d&#39;analyse de la mobilité pourrait fournir un suivi de remise en forme, la surveillance de la santé, détection de chute, la maison ou l&#39;automatisation de travail, et exercis autogestionnaireles programmes de E5. Smartphones peuvent être considérés comme des plates-formes de mesure inertielle pour détecter les activités mobiles et les modèles mobiles chez les humains, en utilisant les caractéristiques de signaux mathématiques générées calculées avec des sorties de capteurs embarqués 6. Méthodes de production de longs courants comprennent heuristique, dans le domaine temporel, domaine de fréquence, et les approches fondées sur l&#39;analyse en ondelettes-7. Systèmes de smartphones HAR modernes ont montré une précision élevées de prédiction lors de la détection des activités spécifiées 1,5,6,7. Ces études varient dans la méthodologie d&#39;évaluation ainsi que la précision puisque la plupart des études ont leur propre ensemble de la formation, la configuration de l&#39;environnement, et le protocole de collecte des données. Sensibilité, la spécificité, la précision, le rappel, la précision, et F-score sont couramment utilisés pour décrire la qualité de prédiction. Cependant, peu ou pas d&#39;informations sont disponibles sur les méthodes pour &quot;activité simultanée&quot; la reconnaissance et l&#39;évaluation de la capacité de détecter les changements d&#39;activité en temps réel1, pour les systèmes HAR qui tentent de catégoriser plusieurs activités. Les méthodes d&#39;évaluation pour la précision du système HAR varient considérablement entre les études. Indépendamment de l&#39;algorithme de classification ou caractéristiques appliquées, des descriptions de méthodes d&#39;évaluation standard d&#39;or sont vagues pour la plupart des recherches HAR. La reconnaissance de l&#39;activité dans un environnement de la vie quotidienne n&#39;a pas été largement étudié. La plupart des systèmes de reconnaissance activité basée smartphone sont évalués d&#39;une manière contrôlée, conduisant à un protocole d&#39;évaluation qui peuvent être avantageux de l&#39;algorithme plutôt que réaliste à un environnement du monde réel. Au sein de leur système d&#39;évaluation, les participants effectuent souvent que les actions destinées à la prévision, plutôt que l&#39;application d&#39;un large éventail d&#39;activités réalistes pour le participant à effectuer consécutivement, imitant les événements de la vie réelle. Certains smartphones HAR étudie 8,9 groupe des activités similaires ensemble, tels que les escaliers et la marche, Mais excluent les autres activités de l&#39;ensemble de données. Précision de la prédiction est alors déterminé par la façon dont l&#39;algorithme a identifié les activités cibles. Dernbach et al. 9 avaient participants écrivent l&#39;activité qu&#39;ils étaient sur ​​le point d&#39;exécuter avant de passer, interrompre transitions continues de changement d&#39;état. HAR système évaluations devraient évaluer l&#39;algorithme alors que le participant effectue des actions naturelles dans un cadre de vie quotidien. Cela permettrait une évaluation de la vie réelle qui reproduit l&#39;utilisation quotidienne de l&#39;application. Un circuit réaliste comprend de nombreux changements d&#39;état ainsi que d&#39;un mélange des actions non prévisibles par le système. Un enquêteur peut alors évaluer la réponse de l&#39;algorithme à ces mouvements supplémentaires, évaluant ainsi la robustesse de l&#39;algorithme à des mouvements anormaux. Cet article présente un système de surveillance de la mobilité Wearable (WMMS) protocole d&#39;évaluation qui utilise un cours contrôlé qui tient compte des environnements de la vie quotidienne de la vie réelle. WMMSévaluation peut alors être faite dans des conditions contrôlées, mais réalistes. Dans ce protocole, nous utilisons une troisième génération WMMS qui a été développé à l&#39;Université d&#39;Ottawa et Ottawa Hospital Research Institute 11-15. Le WMMS a été conçu pour les smartphones avec un accéléromètre tri-axe et du gyroscope. Représente l&#39;algorithme de la mobilité pour l&#39;utilisateur de la variabilité, permet une réduction dans le nombre de faux positifs pour l&#39;identification des changements d&#39;état, et augmente la sensibilité de l&#39;activité catégorisation. Minimiser les faux positifs est important car la WMMS déclenche court enregistrement de vidéo clip lorsque l&#39;activité des changements d&#39;état sont détectées, pour l&#39;évaluation de l&#39;activité contextuel qui améliore encore la classification WMMS. Enregistrement vidéo inutile crée des inefficacités dans le stockage et l&#39;utilisation de la batterie. L&#39;algorithme WMMS est structuré comme un modèle d&#39;apprentissage à faible calcul et évaluée en utilisant différents niveaux de prédiction, où une augmentation du niveau de prédiction, signifie une augmentation de la quantitéactions de reconnaissable.

<table border="0" cellpadding="0" cellspacing="0" width="1314">
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:516px;">Smartphone or wearable measurement device</td>
			<td style="width:627px;">Blackberry</td>
			<td style="width:108px;">Z10</td>
			<td style="width:64px;"></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Smartphone for video recording</td>
			<td>Blackberry</td>
			<td>Z10 or 9800</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Phone holster</td>
			<td>Any</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Data logger application for the smartphone</td>
			<td>BlackBerry World - TOHRC Data Logger for BlackBerry 10</td>
			<td></td>
			<td><a href="http://appworld.blackberry.com/webstore/content/32013891/?countrycode=CA">http://appworld.blackberry.com/webstore/content/32013891/?countrycode=CA</a></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Wearable mobility measurement</td>
			<td>Custom Blackberry 10 and Matlab software for mobility monitoring</td>
			<td></td>
			<td><a href="http://www.irrd.ca/cag/smartphone/">http://www.irrd.ca/cag/smartphone/</a></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Video editing or analysis software</td>
			<td>Motion Analysis Tools</td>
			<td></td>
			<td><a href="http://www.irrd.ca/cag/mat/">http://www.irrd.ca/cag/mat/</a></td>
		</tr>
	</tbody>
</table>

evaluation of a smartphone-based human activity recognition system in a daily living environment

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). 
To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated.
The mobile versus immobile classification set had a sensitivity of 86.30% &#177; 7.2% and specificity of 98.96% &#177; 0.6%, while the second prediction set had a sensitivity of 88.35% &#177; 7.80% and specificity of 98.51% &#177; 0.62%. For the third classification set, sensitivity was 84.92% &#177; 6.38% and specificity was 98.17 &#177; 0.62. F1 scores for the first, second and third classification sets were 86.17 &#177; 6.3, 80.19 &#177; 6.36, and 78.42 &#177; 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

Le protocole de l&#39;étude a été menée auprès d&#39;un échantillon de commodité de quinze participants valides dont le poids moyen était de 68,9 (± 11,1) kg, hauteur était de 173,9 (± 11,4) cm, et l&#39;âge était de 26 (± 9) ans, recrutés dans L&#39;Hôpital d&#39;Ottawa et de l&#39;Université d&#39;Ottawa du personnel et des étudiants. Un smartphone capturé les données des capteurs à un taux de 40-50 Hz variable. Les variations de taux d&#39;échantillonnage sont typiques pour l&#39;échantillonnag...

Watch this Scientific Journal Video about Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment at JoVE.com

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.

Évaluation d&#39;un système de reconnaissance de l&#39;activité humaine basée à Smartphone dans un milieu de vie quotidien

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that ...

evaluation-smartphone-based-human-activity-recognition-system-daily

Research

JoVE Journal

Behavior

8.8K vues.  University of Ottawa. A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.

Vidéo: Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Construction of Microdrive Arrays for Chronic Neural Recordings in Awake Behaving Mice

State-of-the-art electrophysiological recordings from the brains of freely behaving animals allow researchers to simultaneously examine local field potentials (LFPs) from populations of neurons and action potentials from individual cells, as the animal engages in experimentally relevant tasks. Chronically implanted microdrives allow for brain recordings to last over periods of several weeks. Miniaturized drives and lightweight components allow for these long-term recordings to occur in small mammals, such as mice. By using tetrodes, which consist of tightly braided bundles of four electrodes in which each wire has a diameter of 12.5 &mu;m, it is possible to isolate physiologically active neurons in superficial brain regions such as the cerebral cortex, dorsal hippocampus, and subiculum, as well as deeper regions such as the striatum and the amygdala. Moreover, this technique insures stable, high-fidelity neural recordings as the animal is challenged with a variety of behavioral tasks. This manuscript describes several techniques that have been optimized to record from the mouse brain. First, we show how to fabricate tetrodes, load them into driveable tubes, and gold-plate their tips in order to reduce their impedance from M&Omega; to K&Omega; range. Second, we show how to construct a custom microdrive assembly for carrying and moving the tetrodes vertically, with the use of inexpensive materials. Third, we show the steps for assembling a commercially available microdrive (Neuralynx VersaDrive) that is designed to carry independently movable tetrodes. Finally, we present representative results of local field potentials and single-unit signals obtained in the dorsal subiculum of mice. These techniques can be easily modified to accommodate different types of electrode arrays and recording schemes in the mouse brain.

The design and assembly of microdrives for in vivo electrophysiological recordings of brain signals from the mouse is described. By attaching microelectrode bundles to sturdy driveable carriers, these techniques allow for long-term and stable neural recordings. The lightweight design allows for unrestricted behavioral performance by the animal following drive implantation.

Construction de tableaux Microdrive pour les enregistrements de neurones chroniques chez la souris Behaving Awake

State-of-the-art electrophysiological recordings from the brains of freely behaving animals allow researchers to simultaneously examine local field ...

Recherche

Sciences du comportement

Two-photon Calcium Imaging in Mice Navigating a Virtual Reality Environment

In recent years, two-photon imaging has become an invaluable tool in neuroscience, as it allows for chronic measurement of the activity of genetically identified cells during behavior1-6. Here we describe methods to perform two-photon imaging in mouse cortex while the animal navigates a virtual reality environment. We focus on the aspects of the experimental procedures that are key to imaging in a behaving animal in a brightly lit virtual environment. The key problems that arise in this experimental setup that we here address are: minimizing brain motion related artifacts, minimizing light leak from the virtual reality projection system, and minimizing laser induced tissue damage. We also provide sample software to control the virtual reality environment and to do pupil tracking. With these procedures and resources it should be possible to convert a conventional two-photon microscope for use in behaving mice.

Here we describe the experimental procedures involved in two-photon imaging of mouse cortex during behavior in a virtual reality environment.

Deux photons Imaging calcium dans la souris Naviguer une réalité environnement virtuel

In recent years, two-photon imaging has become an invaluable tool in neuroscience, as it allows for chronic measurement of the activity of genetically ...

Development of a Virtual Reality Assessment of Everyday Living Skills

Cognitive impairments affect the majority of patients with schizophrenia and these impairments predict poor long term psychosocial outcomes.&#160; Treatment studies aimed at cognitive impairment in patients with schizophrenia not only require demonstration of improvements on cognitive tests, but also evidence that any cognitive changes lead to clinically meaningful improvements.&#160; Measures of &#8220;functional capacity&#8221; index the extent to which individuals have the potential to perform skills required for real world functioning. &#160;Current data do not support the recommendation of any single instrument for measurement of functional capacity.&#160; The Virtual Reality Functional Capacity Assessment Tool (VRFCAT) is a novel, interactive gaming based measure of functional capacity that uses a realistic simulated environment to recreate routine activities of daily living. Studies are currently underway to evaluate and establish the VRFCAT&#8217;s sensitivity, reliability, validity, and practicality.&#160;This new measure of functional capacity is practical, relevant, easy to use, and has several features that improve validity and sensitivity of measurement of function in clinical trials of patients with CNS disorders.

A challenge for proving treatment efficacy for cognitive impairments in schizophrenia is finding the optimizing measurement of skills related to everyday functioning.&#160; The Virtual Reality Functional Capacity Assessment Tool (VRFCAT) is an interactive gaming based computerized measure aimed at skills associated with everyday functioning, including baseline impairments and treatment related changes.

Développement d&#39;une évaluation de la vie quotidienne des compétences de la réalité virtuelle

Cognitive impairments affect the majority of patients with schizophrenia and these impairments predict poor long term psychosocial outcomes.&#160; ...

A Protocol for Housing Mice in an Enriched Environment

Environmental enrichment (EE) is a housing environment for mice that boosts mental and physical health compared to standard laboratory housing. Our recent studies demonstrate that environmental enrichment decreases adiposity, increases energy expenditure, resists diet induced obesity, and causes cancer remission and inhibition in mice. EE typically consists of larger living space, a variety of &#8216;toys&#8217; to interact with, running wheels, and can include a number of other novel environmental changes. All of this fosters a more complex social engagement, cognitive and physical stimulations. Importantly, the toy location and type of toy is changed regularly, which encourages the mice to adapt to a frequently changing and novel environment. Many variables can be manipulated in EE to promote health effects in mice. Thus these approaches are difficult to control and must be properly managed to successfully replicate the associated phenotypes. Therefore, the goal of this video is to demonstrate how EE is properly set up and maintained to assure a complex, challenging, and controlled environment so that other researchers can easily reproduce the protective effects of EE against obesity and cancer.

Environmental enrichment for mice requires a complex and challenging setup, as well as comprehensive husbandry and handling techniques to assure robust metabolic and anti-cancer effects in the mice. This protocol provides detailed procedures to reproduce the above mentioned effects in mice.

Un protocole pour souris Bricolage dans un environnement enrichi

Environmental enrichment (EE) is a housing environment for mice that boosts mental and physical health compared to standard laboratory housing. Our recent ...

A Method to Study Adaptation to Left-Right Reversed Audition

An unusual sensory space is one of the effective tools to uncover the mechanism of adaptability of humans to a novel environment. Although most of the previous studies have used special spectacles with prisms to achieve unusual spaces in the visual domain, a methodology for studying the adaptation to unusual auditory spaces has yet to be fully established. This study proposes a new protocol to set-up, validate, and use a left-right reversed stereophonic system using only wearable devices, and to study the adaptation to left-right reversed audition with the help of neuroimaging. Although individual acoustic characteristics are not yet implemented, and slight spillover of unreversed sounds is relatively uncontrollable, the constructed apparatus shows high performance in a 360&#176; sound source localization coupled with hearing characteristics with little delay. Moreover, it looks like a mobile music player and enables a participant to focus on daily life without arousing curiosity or drawing attention of other individuals. Since the effects of adaptation were successfully detected at the perceptual, behavioral, and neural levels, it is concluded that this protocol provides a promising methodology for studying adaptation to left-right reversed audition, and is an effective tool for uncovering the adaptability of humans to a novel environments in the auditory domain.

The present study proposes a protocol to investigate the adaptation to left-right reversed audition achieved only by wearable devices, using neuroimaging, which can be an effective tool for uncovering the adaptability of humans to a novel environment in the auditory domain.

Une méthode pour étudier l’Adaptation de gauche droite inversé Audition

An unusual sensory space is one of the effective tools to uncover the mechanism of adaptability of humans to a novel environment. Although most of the ...

Use of a Video Scoring Anchor for Rapid Serial Assessment of Social Communication in Toddlers

Reciprocal social behavior (RSB), an early-emerging capacity to engage in social contingency&#8212;which is foundational for both social learning and social competency&#8212;is hypothesized to be disrupted in autism spectrum disorder (ASD). The ability to quantify the full range of RSB during the toddler period, when core symptoms of ASD often arise, is pivotal for evaluating early risk for ASD, characterizing social development, and tracking response to early interventions. However, important parameters of variation in RSB&#8212;especially prior to the development of verbal language&#8212;can be nuanced and difficult to characterize using questionnaire-based methods. To address this challenge, we developed a system for measuring quantitative variation in RSB in toddlers (ages 18 - 30 months) that incorporated not only standard questionnaire data from caregivers but also a novel set of video-referenced items, through which a respondent compares the behavior of a subject to that observed in a short video of a young child manifesting a highly competent level of social communication. Testing of this measure in a general population sample of twins confirmed that both the video-referenced items and the RSB Total Score (video-referenced items plus non-video-referenced items) displayed unimodal, continuous distributions, strong internal consistency, marked preservation of individual differences, and extremely high heritability. In addition, video-referenced items were particularly sensitive to quantifying incremental changes in social communication, a major element of RSB, over the course of early childhood development. Scores on the vrRSB clearly differentiated children with and without ASD and these data comprise an initial validation of this promising method for quantifying early RSB&#8212;cross-sectionally, over time, and as a function of early intervention.

Here we present a protocol for administration of the video-referenced rating of reciprocal social behavior (vrRSB) using a paper or online version. This survey quantifies RSB, a prerequisite for social competence, in toddlers through &#34;video-referenced&#34; items whereby a subject is compared to a reference child shown in a three-minute video.

Utilisation d’une vidéo marquant l’ancre pour une évaluation rapide série de Communication sociale chez les jeunes enfants

Reciprocal social behavior (RSB), an early-emerging capacity to engage in social contingency&#8212;which is foundational for both social learning and ...

Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality

The inability to complete instrumental activities of daily living (IADL) is a precursor to various neuropsychological diseases. Questionnaire-based assessments of IADL are easy to use but prone to subjective bias. Here, we describe a novel virtual reality (VR) test to assess two complex IADL tasks: handling financial transactions and using public transportation. While a participant performs the tasks in a VR setting, a motion capture system traces the position and orientation of the dominant hand and head in a three-dimensional Cartesian coordinate system. Kinematic raw data are collected and converted into &#39;kinematic performance measures,&#39; i.e., motion trajectory, moving distance, and time to completion. Motion trajectory is the path of a particular body part (e.g., dominant hand or head) in space. Moving distance refers to the total distance of the trajectory, and time to completion is how long it took to complete an IADL task. These kinematic measures could discriminate patients with cognitive impairment from healthy controls. The development of this kinematic measuring protocol allows detection of early IADL-related cognitive impairments.

We designed a virtual reality test to assess instrumental activities of daily living (IADL) with a motion capture system. We propose a detailed kinematic analysis to interpret the participant&#39;s various movements, including trajectory, moving distance, and time to completion to evaluate IADL capabilities.

Mesurer la cinématique des mouvements de la vie quotidienne avec les systèmes de Capture de mouvement en réalité virtuelle

The inability to complete instrumental activities of daily living (IADL) is a precursor to various neuropsychological diseases. Questionnaire-based ...

Novel Object Recognition and Object Location Behavioral Testing in Mice on a Budget

Ethologically relevant behavioral testing is a critical component of any study that uses mouse models to study the cognitive effects of various physiological or pathological changes. The object location task (OLT) and the novel object recognition task (NORT) are two effective behavioral tasks commonly used to reveal the function and relative health of specific brain regions involved in memory. While both of these tests exploit the inherent preference of mice for the novelty to reveal memory for previously encountered objects, the OLT primarily evaluates spatial learning, which relies heavily on hippocampal activity. The NORT, in contrast, evaluates non-spatial learning of object identity, which relies on multiple brain regions. Both tasks require an open-field-testing arena, objects with equivalent intrinsic value to mice, appropriate environmental cues, and video recording equipment and the software. Commercially available systems, while convenient, can be costly. This manuscript details a simple, cost-effective method for building the arenas and setting up the equipment necessary to perform the OLT and NORT. Furthermore, the manuscript describes an efficient testing protocol that incorporates both OLT and NORT and provides typical methods for data acquisition and analysis, as well as representative results. Successful completion of these tests can provide valuable insight into the memory function of various mouse model systems and appraise the underlying neural regions that support these functions.

Here we provide a protocol which includes comprehensive instructions for the economical establishment of murine object location and novel object recognition behavioral testing, including the design, cost, and construction of required equipment as well as execution of behavioral testing, data collection, and analysis.

Reconnaissance d’objets nouveaux et emplacement de l’objet comportementale essais chez la souris sur un Budget

Ethologically relevant behavioral testing is a critical component of any study that uses mouse models to study the cognitive effects of various ...

Impact of High-intensity Interval Exercise and Moderate-Intensity Continuous Exercise on the Cardiac Troponin T Level at an Early Stage of Training

An elevation in cardiac troponin T (cTnT), as a highly specific biomarker of cardiomyocyte damage, after moderate-intensity continuous exercise (MCE) has been described. The exercise-induced cTnT response distorts the diagnostic role of the cTnT assay. Although high-intensity interval exercise (HIE) is growing in popularity and concerns remain about its safety, available data related to cTnT release after HIE is limited, which hampers the use of HIE as a health intervention. Here, we present three representative HIE protocols [traditional HIE (repeated 4 min cycling at 90% V&#775;O2max interspersed with 3 min rest, 200 kJ/session); sprint interval exercise (SIE, repeated 1 min cycling at 120% V&#775;O2max interspersed with 1.5 min rest, 200 kJ/session); and repeated sprint exercise (RSE, 40 x 6 s all-out sprints interspersed with 9 s rest)] and one representative MCE protocol (continuous cycling exercise at an intensity of 60% V&#775;O2max, 200 kJ/session). Forty-seven sedentary, overweight young women were randomly assigned to one of four groups (HIE, SIE, RSE, and MCE). Six bouts of respective exercise were performed by every single group, with each being 48 h apart. Meanwhile, for four groups, the duration of the entire testing period was identical, being 10 days. Before and after the first and final exercise bouts, an assessment was conducted of cTnT. The current study provides a frame of reference giving a clear picture of how a specific exercise session affects the circulating cTnT concentration at the early stage of training. The information may assist with clinical interpretations of post-exercise cTnT elevation and guide the prescription of exercise, especially for HIE.

Here, we present protocols of high-intensity interval and moderate-intensity continuous exercise to observe the response of circulating cardiac troponin T (cTnT) concentration to acute exercise over 10 days. The information may assist with clinical interpretations of post-exercise cTnT elevation and guide the prescription of exercise.

Impact de l'exercice d'intervalle de haute intensité et de l'exercice continu à intensité modérée sur le niveau T de la troponine cardiaque à un stade précoce de l'entraînement

An elevation in cardiac troponin T (cTnT), as a highly specific biomarker of cardiomyocyte damage, after moderate-intensity continuous exercise (MCE) has ...

Optogenetic Manipulation of Neuronal Activity to Modulate Behavior in Freely Moving Mice

Optogenetic modulation of neuronal circuits in freely moving mice affects acute and long-term behavior. This method is able to perform manipulations of single neurons and region-specific transmitter release, up to whole neuronal circuitries in the central nervous system, and allows the direct measurement of behavioral outcomes. Neurons express optogenetic tools via an injection of viral vectors carrying the DNA of choice, such as Channelrhodopsin2 (ChR2). Light is brought into specific brain regions via chronic optical implants that terminate directly above the target region. After two weeks of recovery and proper tool-expression, mice can be repeatedly used for behavioral tests with optogenetic stimulation of the neurons of interest.
Optogenetic modulation has a high temporal and spatial resolution that can be accomplished with high cell specificity, compared to the commonly used methods such as chemical or electrical stimulation. The light does not harm neuronal tissue and can therefore be used for long-term experiments as well as for multiple behavioral experiments in one mouse. The possibilities of optogenetic tools are nearly unlimited and enable the activation or silencing of whole neurons, or even the manipulation of a specific receptor type by light.
The results of such behavioral experiments with integrated optogenetic stimulation directly visualizes changes in behavior caused by the manipulation. The behavior of the same animal without light stimulation as a baseline is a good control for induced changes. This allows a detailed overview of neuronal types or neurotransmitter systems involved in specific behaviors, such as anxiety. The plasticity of neuronal networks can also be investigated in great detail through long-term stimulation or behavioral observations after optical stimulation. Optogenetics will help to enlighten neuronal signaling in several kinds of neurological diseases.

With optogenetic manipulation of specific neuronal populations or brain regions, behavior can be modified with high temporal and spatial resolution in freely moving animals. By using different optogenetic tools in combination with chronically implanted optical fibers, a variety of neuronal modulations and behavioral testing can be performed.

Manipulation optogénétique de l’activité neuronale pour moduler le comportement chez les souris en mouvement libre

Optogenetic modulation of neuronal circuits in freely moving mice affects acute and long-term behavior. This method is able to perform manipulations of ...