Wearable devices for health-related quality of life evaluation

Medical and biomedical research fields are paying even closer attention
to the health-related quality of life (HRQoL). Furthermore, having a
precise snapshot of a subject’s daily life and of the related vital
parameters (heart rate, ECG pattern, movement, sleeping habits, etc.)
helps medical and social structures having a precise scenario of the
elders. HRQoL is largely assessed by means of patient-reported outcomes
(PROs), a flawed methodology if used for quality of life evaluation.
Several kinds of biometrical parameters have been demonstrated to be
significant in the evaluation of the HRQoL alongside with the PROs. It
has also been shown that individual quality of life is tightly related
to the patient frailty status (PFS). Pre-frail elders need a constant
monitoring to catch any drift in their frailty markers for foretelling a
possible shift in their PFS. A scalable hardware/software architecture
has been realized with the aim of gathering vital parameters keeping low
cumbersomeness. Systems should be able to gather, post-process and
analyze monitored person’s vital parameters but, at the same time,
patient therapies’ effectiveness can be constantly monitored and
examined in deep. In the next future, the widely spreading of these
systems will produce an huge quantity of structured, semi- or
quasi-structured data. In order to reduce the complexity and manage such
data, new storage techniques and new processing algorithms are
desirable. Aim of this paper is to describe a novel architecture and an
example of the algorithm to reduce the complexity of the non-structured
data like a single-channel ECG.

Wearable devices for health-related quality of life evaluation

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