相关论文: CosinorAge: Unified Python and Web Platform for Bi…
Sleep is among the most important factors affecting one's daily performance, well-being, and life quality. Nevertheless, it became possible to measure it in daily life in an unobtrusive manner with wearable devices. Rather than camera…
Clinical-grade wearable sleep monitoring is a challenging problem since it requires concurrently monitoring brain activity, eye movement, muscle activity, cardio-respiratory features and gross body movements. This requires multiple sensors…
Neuroimaging-driven prediction of brain age, defined as the predicted biological age of a subject using only brain imaging data, is an exciting avenue of research. In this work we seek to build models of brain age based on functional…
The gradual accumulation of damage and dysregulation during the aging of living organisms can be quantified. Even so, the aging process is complex and has multiple interacting physiological scales -- from the molecular to cellular to whole…
Assessment of job performance, personalized health and psychometric measures are domains where data-driven and ubiquitous computing exhibits the potential of a profound impact in the future. Existing techniques use data extracted from…
Visually impaired individuals often require a guide runner to safely participate in outdoor running. However, maintaining synchronized pacing with verbal cues or tethers can be mentally taxing and physically restrictive. Existing solutions…
This work presents a smartwatch attached to patients at remote locations, which would help in the navigation of wheel chair and monitor the vitals of patients and relay it through IoT. This wearable smartwatch is equipped with sensors to…
In order to develop infocommunications devices so that the capabilities of the human brain may interact with the capabilities of any artificially cognitive system a deeper knowledge of aging is necessary. Especially if society does not want…
Background: Photoplethysmography (PPG), increasingly available through wearable devices, provides a non-invasive means of monitoring human hemodynamics. In this study, we introduce artificial intelligence-derived photoplethysmography…
Physical activity is crucial for human health. With the increasing availability of large-scale mobile health data, strong associations have been found between physical activity and various diseases. However, accurately capturing this…
We present WEARDA, the open source WEARable sensor Data Acquisition software package. WEARDA facilitates the acquisition of human activity data with smartwatches and is primarily aimed at researchers who require transparency, full control,…
Normative aging trends of the brain can serve as an important reference in the assessment of neurological structural disorders. Such models are typically developed from longitudinal brain image data -- follow-up data of the same subject…
This paper reports on the development of a wearable system using wireless biomedical sensors for ubiquitous healthcare service provisioning. The prototype system is developed to address current healthcare challenges such as increasing cost…
Background: The rise of mobile technology and health apps has increased the use of person-generated health data (PGHD). PGHD holds significant potential for clinical decision-making but remains challenging to manage. Objective: This study…
The cosinor model is frequently used to represent the oscillatory behavior of different genes over time. When data are collected from multiple individuals, the cosinor model is estimated with recorded gene expression levels and the 24 hour…
In a world where data is the new currency, wearable health devices offer unprecedented insights into daily life, continuously monitoring vital signs and metrics. However, this convenience raises privacy concerns, as these devices collect…
As the world population gets older, the healthcare system must be adapted, among others by providing continuous health monitoring at home and in the city. The social activities have a significant role in everyone health status. Hence, this…
The lack of standardization across Wearable Human Activity Recognition (WHAR) datasets limits reproducibility, comparability, and research efficiency. We introduce WHAR datasets, an open-source library designed to simplify WHAR data…
Despite advances in practical and multimodal fine-grained Human Activity Recognition (HAR), a system that runs entirely on smartwatches in unconstrained environments remains elusive. We present WatchHAR, an audio and inertial-based HAR…
Advances in deep learning for human activity recognition have been relatively limited due to the lack of large labelled datasets. In this study, we leverage self-supervised learning techniques on the UK-Biobank activity tracker dataset--the…