Mobility change-of-state detection using a smartphone-based approach

Hache, G.; Lemaire, Edward D.; Baddour, Natalie

doi:10.1109/memea.2010.5480206

Cited by 31 publications

(17 citation statements)

References 13 publications

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“…al. [16] use an accelerometer integrated with a blackberry Bold 9000 platform for detecting changes in the state of the subject caused by starting/stopping and postural changes in activities. Khan et.…”

Section: Related Workmentioning

confidence: 99%

Simple and Complex Activity Recognition through Smart Phones

Dernbach

Das

Krishnan

et al. 2012

2012 Eighth International Conference on Intelligent Environments

277

155

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Abstract-Due to an increased popularity of assistive healthcare technologies activity recognition has become one of the most widely studied problems in technology-driven assistive healthcare domain. Current approaches for smart-phone based activity recognition focus only on simple activities such as locomotion. In this paper, in addition to recognizing simple activities, we investigate the ability to recognize complex activities, such as cooking, cleaning, etc. through a smart phone. Features extracted from the raw inertial sensor data of the smart phone corresponding to the user's activities, are used to train and test supervised machine learning algorithms. The results from the experiments conducted on ten participants indicate that, in isolation, while simple activities can be easily recognized, the performance of the prediction models on complex activities is poor. However, the prediction model is robust enough to recognize simple activities even in the presence of complex activities.

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Section: Related Workmentioning

confidence: 99%

Simple and Complex Activity Recognition through Smart Phones

Dernbach

Das

Krishnan

et al. 2012

2012 Eighth International Conference on Intelligent Environments

277

155

View full text Add to dashboard Cite

show abstract

“…Las capacidades tecnológicas de los teléfonos inteligentes han generado el interés para investigar fenó-menos físicos como caídas [7] o el caminar humano [8,9]. La Figura 2 ilustra la secuencia para obtener señales de verificación e identificación del caminar usando el acelerómetro [10].…”

Section: Estado Del Arteunclassified

Operación Remota De Un Robot Móvil Usando Un Teléfono Inteligente

Flores

G.²,

Ramírez³

2016

ings

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Recibido: 30-09-2016, aprobado tras revisión: 16-12-2016 Forma sugerida de citación: Flores, C.; Rojas, F.; Trejo, K. (2017). «Operación remota de un robot móvil usando un teléfono inteligente». Ingenius. N.• 17, (Enero-Junio). pp. 29-35. ISSN: 1390-650X. Resumen AbstractEn este artículo se presenta un acercamiento al mando a distancia de un robot móvil que emplea un telé-fono inteligente para comandarlo. La idea principal es recolectar los datos generados por el acelerómetro incluido en el teléfono inteligente. Los datos son los resultados de mover el teléfono en la dirección de los ejes Y y Z. Tales datos serán usados para entrenar dos redes neuronales que definirán la dirección del movimiento del robot móvil. Las salidas obtenidas de las redes neuronales serán procesadas para calcular y trazar la trayectoria, que es determinada por el modelo cinemático para un robot móvil tipo triciclo.This paper presents an approach to remote control a robot using smartphone. The main idea is to collect data generated by the accelerometer sensor included in the smartphone. The data are the results of moving the smartphone in direction of the axis Y and Z. Such data will be used for training two neural networks that will define the direction of the movement of the mobile robot. The outputs obtained from the neural networks will be processed to compute and plot the trajectory, which is determined by the kinematic model for a tricycle mobile robot.Palabras clave: sistemas remotos, redes neuronales, robot móvil, teléfono inteligente, acelerómetro.

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“…WMMS evaluation can then be made under controlled but realistic conditions. In this protocol, we use a thirdgeneration WMMS that was developed at the University of Ottawa and Ottawa Hospital Research Institute [11][12][13][14][15] . The WMMS was designed for smartphones with a tri-axis accelerometer and gyroscope.…”

Section: Introductionmentioning

confidence: 99%

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

Lemaire

Tundo

Baddour

2015

JoVE

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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% ± 7.2% and specificity of 98.96% ± 0.6%, while the second prediction set had a sensitivity of 88.35% ± 7.80% and specificity of 98.51% ± 0.62%. For the third classification set, sensitivity was 84.92% ± 6.38% and specificity was 98.17 ± 0.62. F1 scores for the first, second and third classification sets were 86. 17 ± 6.3, 80.19 ± 6.36, and 78.42 ± 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. Video LinkThe video component of this article can be found at

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Mobility change-of-state detection using a smartphone-based approach

Cited by 31 publications

References 13 publications

Simple and Complex Activity Recognition through Smart Phones

Simple and Complex Activity Recognition through Smart Phones

Operación Remota De Un Robot Móvil Usando Un Teléfono Inteligente

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

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