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Instrumented Timed Up and Go Test Using Inertial Sensors from Consumer Wearable Devices
dc.contributor.author | Matey-Sanz, Miguel | |
dc.contributor.author | González-Pérez, Alberto | |
dc.contributor.author | Casteleyn, Sven | |
dc.contributor.author | Granell, Carlos | |
dc.date.accessioned | 2022-12-09T14:47:48Z | |
dc.date.available | 2022-12-09T14:47:48Z | |
dc.date.issued | 2022-07 | |
dc.identifier.citation | 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. | ca_CA |
dc.identifier.isbn | 9783031093418 | |
dc.identifier.isbn | 9783031093425 | |
dc.identifier.uri | http://hdl.handle.net/10234/201062 | |
dc.description | Part de la conferència: 20th International Conference on Artificial Intelligence in Medicine, AIME 2022 https://doi.org/10.1007/978-3-031-09342-5 | ca_CA |
dc.description.abstract | 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. | ca_CA |
dc.format.extent | 10 p. | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Springer Nature Switzerland AG | ca_CA |
dc.relation | Applying mobile and geospatial technologies to ecological momentary interventions | ca_CA |
dc.relation | Sensor and mobile based mental health solutions: Exposure therapy (SyMptOMS-ET) | ca_CA |
dc.relation.isPartOf | Artificial Intelligence in Medicine 20th International Conference on Artificial Intelligence in Medicine, AIME 2022, Halifax, NS, Canada, June 14–17, 2022, Proceedings | ca_CA |
dc.rights | © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | ca_CA |
dc.subject | timed up and go | ca_CA |
dc.subject | smartwatch | ca_CA |
dc.subject | mobile sensing | ca_CA |
dc.title | Instrumented Timed Up and Go Test Using Inertial Sensors from Consumer Wearable Devices | ca_CA |
dc.type | info:eu-repo/semantics/conferenceObject | ca_CA |
dc.rights.accessRights | info:eu-repo/semantics/restrictedAccess | ca_CA |
dc.relation.publisherVersion | https://link.springer.com/chapter/10.1007/978-3-031-09342-5_14 | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
project.funder.name | Ministerio de Universidades | ca_CA |
project.funder.name | Ministerio de Ciencia e Innovación | ca_CA |
oaire.awardNumber | FPU19/05352 | ca_CA |
oaire.awardNumber | FPU17/03832 | ca_CA |
oaire.awardNumber | PID2020-120250RB-100 | ca_CA |
oaire.awardNumber | MCIN/AEI/10.13039/501100011033. | ca_CA |
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