D. Sokas “Methods for unobtrusive monitoring of patients with frailty syndrome” doctoral dissertation defense

Author, Institution: Daivaras Sokas, Kaunas University of Technology

Science area, field of science: Technological Sciences, Electrical and Electronic Engineering, T001

Scientific Supervisor: Assoc. Prof. Dr. Andrius Petrėnas (Kaunas University of Technology, Technological Sciences, Electrical and Electronic Engineering, T001).

Dissertation Defense Board of Electrical and Electronic Engineering Science Field:
Prof. Dr. Arminas Ragauskas (Kaunas University of Technology, Technological Sciences, Electrical and Electronic Engineering, T001) – chairperson
Prof. Dr. Hab. Arūnas Lukoševičius (Kaunas University of Technology, Technological Sciences, Electrical and Electronic Engineering, T001)
Prof. Dr. Jūratė Macijauskienė (Lithuanian University of Health Sciences, Medical and Health Sciences, Medicine, M001)
Prof. Dr. Dangirutis Navikas (Kaunas University of Technology, Technological Sciences, Electrical and Electronic Engineering, T001)
Assoc. Prof. Dr. Jari Viik (Tampere University, Finland, Technological Sciences, Electrical and Electronic Engineering, T001)

Dissertation defense meeting will be at Rectorate Hall of Kaunas University of Technology (K. Donelaičio 73 – 402, Kaunas)

The doctoral dissertation is available at the library of Kaunas University of Technology (Gedimino 50, Kaunas)

Annotation: With an increasing number of patients with frailty being referred for surgery, there is a need for convenient tools that would help to improve the understanding of the effectiveness of exercise-based rehabilitation. Accordingly, this doctoral thesis proposes and investigates a wearable-based approach for unobtrusive assessment of frailty. Currently, the clinical practice for assessing frailty is limited to in-clinic evaluations. However, wearable technology has advanced to the point where frailty can potentially be assessed outside of the clinical setting. The majority of previous research has focused on identifying frailty or pre-frailty in older adults. However, the feasibility of capturing subtle changes in the frailty status during exercise training has not yet been deeply explored. The aim of this thesis is to develop, investigate, and validate algorithms for unobtrusive monitoring of the frailty status in the activities of daily living. A derivative dynamic time warping-based algorithm has been proposed to detect physical stressors, namely, walking and stair-climbing, in wearable-based biosignals. For parametrization of frailty, algorithms that assess the kinematic properties and heart rate to physical stressors have been explored. A concept of interpretable machine learning has been proposed for identifying clinically informative features that would provide information on the frail physiological functions of an individual patient.

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