Browsing by Author "Medina Quero, Javier"

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    Analyzing daily behaviours from wearable trackers using linguistic protoforms and fuzzy clustering
    (2020-09-01) Gramajo, Sergio; Medina Quero, Javier; Martinez-Cruz, Carmen; Espinilla Estevez, Macarena
    The proliferation of low-cost wearable trackers is enabling users to collect daily data on human activity in a non-invasive manner and outside laboratory environments. Properly exploiting these data allows for remote supervision and counseling by experts; however, extracting key indicators from the lengthy data streams is challenging, often relying on statistical metrics or raw data clustering lacking interpretability. To address this issue, we propose an interpretable definition of key indicators using linguistic protoforms, incorporating fuzzy temporal processing and fuzzy semantic quantification. Furthermore, we utilize protoforms defined by experts to evaluate the source data stream, providing a straightforward description of users' daily activity. Subsequently, the degrees of truth of each protoform are analyzed using fuzzy clustering methods to offer an interpretable description of long-term user activity. This work includes a case study wherein data from user activity (heartbeats per minute and sleep stages) were collected using a Fitbit wearable device.
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    Monwatch : a fuzzy application to monitor the user behavior using wearable trackers
    (2020-07-19) Gramajo, Sergio; Medina Quero, Javier; Serrano, Jose; Martinez-Cruz, Carmen
    Nowadays, the proliferation of wearable devices has enabled monitoring user behaviors and activities in a non-invasive, autonomous, and straightforward way. Moreover, new trend analysis has been boosted by biosignal sensors from wearable trackers, such as inertial or heart rate sensors. The knowledge of such user activity presents personalized monitoring to prevent any kind of physical or neurological disorders through sensor evaluation by an expert. To this end, the definition of key indicators and the display of results and relevant analyses require agile and effective tools. Therefore, this proposal presents a novel web application where the data obtained from a Fitbit Ionic smartwatch wearable are collected, synchronized, and compiled to present a summary of a user's daily activity, which is defined by a linguistic description using fuzzy logic to represent the most relevant Health Key Indicators (HKI). Moreover, an analysis of the user's behavior over time is proposed by inferring relevant patterns from a fuzzy clustering algorithm.