This review presents the state of the art and a global overview of research challenges of real-
time distributed activity recognition in the field of healthcare. Offline activity recognition is …

Multimodal data streams are essential for analyzing personal life, environmental conditions,
and social situations. Since these data streams have different granularities and semantics …

Current digital mental healthcare solutions conventionally take on a reactive approach,
requiring individuals to self-monitor and document existing symptoms. These solutions are …

Complex human activity recognition suffers from ambiguity of interpretation problem. A novel
neutrosophic formal concept analysis method has been proposed to quantify non …

Traditionally, the regime of mental healthcare has followed an episodic psychotherapy
model wherein patients seek care from a provider through a prescribed treatment plan …

Real time extraction of user context like current location, position, physiological parameters,
actions etc. from sensor data is a challenging problem. Probabilistic machine learning …

Human beings have always been interested in understanding themselves and their
surroundings. Learning about the relationship between the two can reveal facts of the …

With increasing ubiquity of wearable sensor based devices, smartphones and social
applications, rich streams of personal data are being generated with phenomenal volume …

Data from sensor logs in raw form is generally continuous valued. This data from multiple
sensors in continuous stream becomes voluminous. For knowledge discovery like extraction …