Los autores reconocen Evan Beisheim, Nicole Capela, Andrew Herbert-Copley para la asistencia técnica y la recogida de datos. La financiación del proyecto se recibió de Ciencias Naturales e Ingeniería de Investigación de Canadá (NSERC) y BlackBerry Ltd., incluyendo teléfonos inteligentes utilizados en el estudio.

Este protocolo fue aprobado por el Consejo de Ética de Investigación de Ottawa Red de Ciencias de la Salud. 1. Preparación <ol><li> Proporcionar a los participantes con un resumen de la investigación, responder a cualquier pregunta, y obtener el consentimiento informado. Características de los participantes de grabación (por ejemplo, edad, sexo, altura, peso, circunferencia de la cintura, la altura de la pierna desde la anterior espina ilíaca ilíaca al maléolo medial), código de identificación, y la fecha en una hoja de datos. Asegúrese de que el segundo teléfono inteligente que se utiliza para capturar vídeo se establece en al menos un 30 fotogramas por segundo velocidad de captura. </li><li> Fije firmemente una funda de teléfono para cinturón o pantalón delante de la cintura el derecho del participante. Inicie la aplicación de teléfono inteligente que se utiliza para recoger los datos de los sensores (es decir, de registro de datos o aplicación WMMS) en el teléfono inteligente de medición movilidad y garantizar que la aplicación se ejecuta correctamente. Coloque el teléfono inteligente en la funda, con la parte posterior del dispositivo (ca traseraMera) hacia afuera. </li><li> Iniciar grabación de vídeo digital en un segundo teléfono inteligente. Para el anonimato, grabar el video de comparación sin mostrar la cara de la persona, pero asegurarse de registrar todas las transiciones de actividad. El teléfono puede ser de mano. </li></ol> 2. Actividad Circuito <ol><li> Siga el participante y video de sus acciones, en el segundo teléfono inteligente, mientras realizan las siguientes acciones, pronunciadas por el investigador: <ol><li> Desde una posición de pie, agitar el teléfono inteligente para indicar el inicio del juicio. </li><li> Continuar en reposo durante al menos 10 seg. Esta fase de pie se puede utilizar para la calibración orientación teléfono 14. </li><li> A pie de una silla cercana y sentarse. </li><li> Ponte de pie y caminar 60 metros a un ascensor. </li><li> Ponte de pie y esperar a que el ascensor y luego entrar en el ascensor. </li><li> Tome el ascensor hasta el segundo piso. </li><li> Gire y entrar en el entorno del hogar. </li><li> Al entrar en el cuarto de baño y simular cepillarse los dientes. </li><li> Simular peinarse. </li><li> Simular el lavado de manos. </li><li> Seque las manos con una toalla. </li><li> A pie hasta la cocina. </li><li> Tome los platos de un estante y colocarlos en el mostrador. </li><li> Llene una tetera con agua del fregadero de la cocina. </li><li> Coloque la olla en el elemento de estufa. </li><li> Coloque el pan en una tostadora. </li><li> A pie hasta el comedor. </li><li> Sentarse en una mesa de comedor. </li><li> Simular una comida en la mesa. </li><li> Ponte de pie y caminar de regreso a la pileta de la cocina. </li><li> Enjuague los platos y colocarlos en un estante. </li><li> A pie desde la cocina hacia el ascensor. </li><li> Ponte de pie y esperar a que el ascensor y luego entrar en el ascensor. </li><li> Tome el ascensor hasta el primer piso. </li><li> Caminar 50 metros a una escalera. </li><li> Abra la puerta y entrar en el hueco de la escalera. </li><li> Sube por las escaleras (13 escalones, alrededor de aterrizaje, 13 pasos). </li><li> Abra la puerta de la escalera en el pasillo. </li><li> Gire a la derecha y caminar por el pasillo de 15 metros. </li><li> Date la vuelta y caminar 15 metros de nuevo a la escalera. </li><li> Abra la puerta y entrar en el hueco de la escalera. </li><li> Caminar por escaleras (13 escalones, alrededor de aterrizaje, 13 pasos). </li><li> Salga de la escalera y entrar en una habitación. </li><li> Acuéstese sobre una cama. </li><li> Levántese y camine 10 metros a una rampa. </li><li> Sube por la rampa, girar a su alrededor, luego por la rampa (20 metros). </li><li> Continúe caminando por el pasillo y abrir la puerta hacia el exterior. </li><li> Caminar 100 metros de la vía pavimentada. </li><li> Date la vuelta y caminar de regreso a la habitación. </li><li> Al entrar en la habitación y situarse en el punto de partida. </li><li> Continuar en pie, y luego agitar el teléfono inteligente para indicar el final de la prueba. </li></ol></li></ol> 3. Prueba de finalización <ol><li> Detenga el teléfono inteligente de grabación de vídeo y pedir al participante para retirar y devolver el smartphone y funda. Detenga el registro de datos o aplicación WMMS en la smartphone. Copie los archivos de datos de movimiento adquiridos y el archivo de vídeo de los dos teléfonos a un ordenador para el procesamiento posterior. </li></ol> 4. Post-procesamiento <ol><li> Sincronizar temporización entre el vídeo y los datos del sensor en bruto mediante la determinación del momento en que el movimiento de la acción comenzó. Este movimiento de sacudida corresponde a una señal del acelerómetro y el vídeo del marco distinta. Compruebe si hay error de sincronización restando el tiempo de batido de finales de la época sacudida inicial, para fuentes de datos de los sensores y vídeo. Diferencias de tiempo deben ser similares entre los dos conjuntos de datos. </li><li> Determinar los tiempos reales de cambio de estado del video estándar de oro registrando la diferencia de tiempo desde el momento en el movimiento de comienzo para el fotograma de vídeo en la transición entre las actividades. Utilice el software de edición de vídeo para obtener la sincronización dentro de 0.033 segundos (es decir, 30 fotogramas por segundo velocidad de vídeo). Utilice el software WMMS para generar cambios del estado comparables de los datos del sensor. </li><li> Generar dos conjuntos de datos, unocon actividades reales y la segunda con actividades previstas, mediante el etiquetado de la actividad para cada fotograma de vídeo (basado en el cambio de estado de temporización) y luego el cálculo de la actividad pronosticada en cada tiempo de fotograma de vídeo desde la salida WMMS. Para la evaluación del desempeño WMMS, calcule verdaderos positivos, falsos negativos, verdaderos negativos, falsos positivos entre la actividad del patrón oro y WMMS actividad predijo. Utilice estos parámetros para el cálculo de medidas de resultados de sensibilidad, especificidad, y F-score. Nota: Un valor de tolerancia de 3 ventanas de datos a cada lado de la ventana que se analiza se puede utilizar para determinar los resultados de cambio de estado, y 2 ventanas de datos para los resultados de clasificación. Por ejemplo, ya que se utilizaron 1 segundas ventanas de datos para la WMMS en este estudio, 3 seg antes y después de la ventana actual se examinaron de manera que se ignoran los cambios consecutivos dentro de esta tolerancia. La consideración fue que los cambios de estado que se producen en menos de 3 segundos se puede ignorar para huma brutoanálisis n movimiento, ya que estos estados sería considerado transitorio. </li></ol>

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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. 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The authors declare that they have no competing financial interests.

Reconocimiento de la actividad humana con un sistema de monitoreo de movilidad portátil ha recibido más atención en los últimos años debido a los avances técnicos en computación y smartphones ponible y necesidades sistemáticas para medidas de resultados cuantitativos que ayuden en la toma de decisiones clínicas y la evaluación intervención de salud. La metodología descrita en este trabajo fue eficaz para evaluar el desarrollo WMMS ya que se encontraron errores de clasificación de actividades que no habrían...

Detección Ubicua ha convertido en un área de investigación de acoplamiento debido a la cada vez más poderosa, pequeño, la computación de bajo costo y equipos de detección 1. Monitoreo Movilidad utilizando sensores vestibles ha generado un gran interés desde la microelectrónica a nivel de los consumidores son capaces de detectar características de movimiento con alta precisión 1. Reconocimiento de la actividad humana (HAR), utilizando sensores vestibles es un área reciente de investigación, con los estudios preliminares realizados en los años 1980 y 1990 2-4. Smartphones modernos contienen los sensores necesarios y la capacidad de cálculo en tiempo real para el reconocimiento de la actividad de la movilidad. Análisis en tiempo real en el dispositivo permite la clasificación de la actividad y de la carga de datos sin intervención del usuario o investigador. Un smartphone con software de análisis de la movilidad podría proporcionar seguimiento gimnasio, vigilancia de la salud, la caída de detección, el hogar o la automatización del trabajo, y exercis autogestiónprogramas e 5. Smartphones pueden considerarse plataformas de medición inercial para detectar actividades móviles y patrones móviles en los seres humanos, el uso de características de señal matemáticos generados calculados con salidas de los sensores a bordo 6. Métodos de generación de características comunes incluyen heurística, el dominio del tiempo, dominio de la frecuencia, y los enfoques basados ​​en análisis wavelet-7. Sistemas HAR smartphones modernos han demostrado una precisión alta de predicción al detectar actividades especificadas 1,5,6,7. Estos estudios varían en la metodología de evaluación, así como la precisión ya que la mayoría de estudios tienen su propio conjunto de entrenamiento, la configuración del medio ambiente, y el protocolo de recogida de datos. La sensibilidad, la especificidad, la exactitud, el recuerdo, la precisión, y F es la puntuación se utilizan comúnmente para describir la calidad de predicción. Sin embargo, poco o nada de información está disponible en los métodos de reconocimiento &quot;actividad concurrente&quot; y la evaluación de la capacidad de detectar cambios en la actividad en tiempo real1, para los sistemas de HAR que intentan categorizar varias actividades. Métodos de evaluación de HAR precisión del sistema varían considerablemente entre los estudios. Independientemente del algoritmo de clasificación o características aplicadas, las descripciones de los métodos de evaluación estándar de oro son vagos para más investigación HAR. El reconocimiento de actividades en un ambiente de la vida diaria no ha sido ampliamente investigado. La mayoría de los sistemas de reconocimiento de la actividad basada en teléfonos inteligentes se evalúan de una manera controlada, dando lugar a un protocolo de evaluación que puede ser ventajoso para el algoritmo en lugar de realista para un entorno del mundo real. Dentro de su esquema de evaluación, los participantes suelen realizar sólo las acciones destinadas a la predicción, en lugar de aplicar una amplia gama de actividades realistas para el participante para realizar consecutivamente, imitando los acontecimientos de la vida real. Algunos HAR teléfono inteligente estudia 8,9 grupales actividades similares juntos, como escaleras y caminar, Pero excluir otras actividades del conjunto de datos. Precisión de la predicción se determina entonces por lo bien el algoritmo identifica las actividades de destino. Dernbach et al. 9 tenían los participantes escriben la actividad que estaban a punto de ejecutar antes de pasar, interrumpiendo transiciones continuas cambio-de-estado. Evaluaciones del sistema HAR deberían evaluar el algoritmo, mientras que el participante realiza acciones naturales en un entorno vida diaria. Esto permitiría una evaluación de la vida real que se replica el uso diario de la aplicación. Un circuito realista incluye muchos cambios del estado, así como una mezcla de acciones no predecibles por el sistema. Un investigador puede entonces evaluar la respuesta del algoritmo para estos movimientos adicionales, evaluando así la robustez del algoritmo para movimientos anómalos. Este artículo presenta una Wearable Sistema de Control de la Movilidad (WMMS) protocolo de evaluación que utiliza un supuesto control que refleja los ambientes de la vida diaria de la vida real. WMMSevaluación puede entonces hacerse bajo condiciones controladas, pero realistas. En este protocolo, se utiliza un WMMS de tercera generación que se desarrolló en la Universidad de Ottawa y Ottawa Hospital Research Institute 11-15. El WMMS fue diseñado para los teléfonos inteligentes con un acelerómetro de tres ejes y giroscopio. El algoritmo de la movilidad representa la variabilidad de usuario, proporciona una reducción en el número de falsos positivos para la identificación de los cambios del estado, y aumenta la sensibilidad de la actividad de categorización. Reducir al mínimo los falsos positivos es importante ya que el WMMS desencadena la grabación de videoclips cortos cuando se detectan cambios en la actividad del Estado, para la evaluación de la actividad sensible al contexto que mejora aún más la clasificación WMMS. Grabación de vídeo innecesario crea ineficiencias en el almacenamiento y uso de la batería. El algoritmo WMMS está estructurado como un modelo de bajo computacional aprendizaje y evaluó utilizando diferentes niveles de predicción, donde un aumento en el nivel de predicción representa un aumento en la cantidadacciones de reconocibles.

<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.

El protocolo del estudio se realizó con una muestra de quince participantes aptos cuyo peso promedio fue 68,9 (± 11,1) kg, altura fue 173,9 (± 11,4) cm, y la edad fue de 26 (± 9) años, reclutados en el Hospital de Ottawa y la Universidad de Ottawa y personal de los estudiantes. Un smartphone capturado los datos del sensor a una tasa variable de 40 a 50 Hz. Variaciones del tipo de muestra son típicos para el muestreo del sensor smartphone. Un segundo teléfono inteligente se utiliza para grabar vídeo digital con u...

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.

Evaluación de un sistema de reconocimiento de la actividad humana a base de Smartphone en una de Vida Diaria

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

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JoVE Journal

Behavior

8.8K vistas.  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.

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

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.

Dos fotones de imágenes de calcio en ratones Recorriendo un entorno de realidad virtual

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

Investigación

Comportamiento

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.

Desarrollo de una Evaluación de la Realidad Virtual de habilidades diarias de vida

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

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Adolescence is a critical developmental period where the early symptoms of schizophrenia frequently emerge. First-degree relatives of people with schizophrenia who are at familial high risk (FHR) may show similar cognitive and emotional changes. However, the neurological changes underlying the emergence of these symptoms remain unclear. This study sought to identify differences in frontal, striatal, and limbic regions in children and adolescents with FHR using functional magnetic resonance imaging. Groups of 21 children and adolescents at FHR and 21 healthy controls completed an emotional oddball task that relied on selective attention and the suppression of task-irrelevant emotional information. The standard oddball task was modified to include aversive and neutral distractors in order to examine potential group differences in both emotional and executive processing. This task was designed specifically to allow for children and adolescents to complete by keeping the difficulty and emotional image content age-appropriate. Furthermore, we demonstrate a technique for suitable fMRI registration for children and adolescent participants. This paradigm may also be applied in future studies to measure changes in neural activity in other populations with hypothesized developmental changes in executive and emotional processing.

This paper describes how to use the emotional oddball task and fMRI to measure brain activation in children and adolescents at familial high risk for schizophrenia (FHR).&#160;FMRI was used to measure differences in fronto-striato-limbic regions during an emotional oddball task. Children with FHR exhibited abnormal functional activation during adolescence.

Medición de Fronto-límbico Actividad Usando una Emocional bicho raro de tareas en niños con alto riesgo familiar para la esquizofrenia

Adolescence is a critical developmental period where the early symptoms of schizophrenia frequently emerge. First-degree relatives of people with ...

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 Protocolo para ratones Viviendas en un ambiente enriquecido

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.

Un método para estudiar la adaptación a izquierda y derecha invertida 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 ...

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.

Medición de la cinemática de los movimientos diarios de vida con sistemas de captura de movimiento en la realidad Virtual

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.

Reconocimiento de objetos nuevos y ubicación del objeto conductual en ratones en un presupuesto

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.

Impacto del ejercicio de intervalo de alta intensidad y ejercicio continuo de intensidad moderada en el nivel T de troponina cardíaca en una etapa temprana de entrenamiento

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.

Manipulación optogenética de la actividad neuronal para modular el comportamiento en ratones que se mueven libremente

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

Iterative Development of an Innovative Smartphone-Based Dietary Assessment Tool: Traqq

To collect dietary intake data in a fast and reliable manner, a flexible and innovative smartphone application (app) called Traqq was developed (iOS/Android). This app can be used as a food record and&#160;24-h recall (or shorter recall periods). Different sampling schemes can be created on either prespecified or random days/times within a predetermined period for both methods, with&#160;push notifications to urge the participants to register their food intake. In case of non-response, notifications are automatically rescheduled to ensure complete data collection. For use as a food record, respondents can access the app and log their food intake throughout the day. Food records close automatically at the end of the day; recalls close after submission of the consumed items. The recall as well as the food record module provide access to an extensive food list based on the Dutch food composition database (FCDB), which can be accustomed to fit different research purposes. When selecting a food item, respondents are simultaneously prompted to insert portion size, i.e., in household measures (e.g., cups, spoons, glasses), standard portion sizes (e.g., small, medium, large), or weight in grams, and eating occasion/time of consumption. Portion size options can be adjusted, e.g., only entry in grams in case of a weighed food record or time of consumption instead of eating occasion). The app also includes a My Dishes function, which allows the respondent to create their own recipes or product combinations (e.g., a daily breakfast) and only report the total quantity consumed. Subsequently, the app accounts for yield and retention factors. The data are stored on a secure server. If desired, additional questions, i.e., in general or those related to specific food items or eating occasions can be incorporated. This paper describes the development of the system (app and backend), including expert evaluations and usability testing.

This article describes the protocol for the development of an innovative smartphone-based dietary assessment application Traqq, including expert evaluations and usability testing.