Instrumented Timed Up and Go Test Using Inertial Sensors from Consumer Wearable Devices
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Otros documentos de la autoría: Matey-Sanz, Miguel; González-Pérez, Alberto; Casteleyn, Sven; Granell, Carlos
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Título
Instrumented Timed Up and Go Test Using Inertial Sensors from Consumer Wearable DevicesFecha de publicación
2022-07Editor
Springer Nature Switzerland AGISBN
9783031093418; 9783031093425Cita bibliográfica
Matey-Sanz, M., González-Pérez, A., Casteleyn, S., & Granell, C. (2022). Instrumented Timed Up and Go Test Using Inertial Sensors from Consumer Wearable Devices. In International Conference on Artificial Intelligence in Medicine (pp. 144-154). Springer, Cham.Tipo de documento
info:eu-repo/semantics/conferenceObjectVersión de la editorial
https://link.springer.com/chapter/10.1007/978-3-031-09342-5_14Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Precision medicine pursues the ambitious goal of providing
personalized interventions targeted at individual patients. Within this
vision, digital health and mental health, where fine-grained monitoring
of patients ... [+]
Precision medicine pursues the ambitious goal of providing
personalized interventions targeted at individual patients. Within this
vision, digital health and mental health, where fine-grained monitoring
of patients form the basis for so-called ecological momentary assessments
and interventions, play a central role as complementary technologybased and data-driven instruments to traditional psychological treatments. Mobile devices are hereby key enablers: consumer smartphones
and wearables are ubiquitously present and used in daily life, while they
come with the necessary embedded physiological, inertial and movement
sensors to potentially recognise user’s activities and behaviors. In this
article, we explore whether real-time detection of fine-grained activities
- relevant in the context of wellbeing - is feasible, applying machine
learning techniques and based on sensor data collected from a consumer
smartwatch device. We present the system architecture, whereby data
collection is performed in the wearable device, real-time data processing
and inference is delegated to the paired smartphone, and model training
is performed offline. Finally, we demonstrate its use by instrumenting
the well-known Timed Up and Go (TUG) test, typically used to assess
the risk of fall in elderly people. Experiments show that consumer smartwatches can be used to automate the assessment of TUG tests and obtain
satisfactory results, comparable with the classical manually performed
version of the test. [-]
Descripción
Part de la conferència: 20th International Conference on Artificial Intelligence in Medicine, AIME 2022
https://doi.org/10.1007/978-3-031-09342-5
Publicado en
Artificial Intelligence in Medicine 20th International Conference on Artificial Intelligence in Medicine, AIME 2022, Halifax, NS, Canada, June 14–17, 2022, ProceedingsEntidad financiadora
Ministerio de Universidades | Ministerio de Ciencia e Innovación
Código del proyecto o subvención
FPU19/05352 | FPU17/03832 | PID2020-120250RB-100 | MCIN/AEI/10.13039/501100011033.
Título del proyecto o subvención
Applying mobile and geospatial technologies to ecological momentary interventions | Sensor and mobile based mental health solutions: Exposure therapy (SyMptOMS-ET)
Derechos de acceso
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
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