A Soft Computing-Based Hierarchical Sport Activity Risk Level Calculation Model for Supporting Home Exercises

Abstract

With the spread of active styles of living, regular health monitoring and risk estimation of exercises became an essential part of everyday life. In this paper, a fuzzy logic-based hierarchical, classified risk calculation model is introduced, which can be used to assess the risk level of sport activity in realtime. The model considers the current physical status and the preliminary assessed medical conditions of the person, the activity load of the exercise, as well as the environmental conditions. Based on this information, a hierarchical fuzzy decision making system evaluates the risk level and sends warning (to the person to stop the activity) or alerting (to a medical doctor/hospital) or both if necessary. By this, serious health problems/crisis situations can be avoided. The complexity of the model is optimized by the application of the singular value decomposition-based complexity reduction algorithm. In critical situations [when the available (dynamically changing) amount of time, resources, and data become insufficient], the anytime mode of operation helps to cope with the temporal conditions and to find a tradeoff between the computational complexity of the evaluations and the accuracy of the results. The system can be operated real-time at home, thus making more comfortable and safer the active (preventive) life and rehabilitation processes of conscious people.

Home telemonitoring of vital signs through a TV-based application for elderly patients

“…Another project with similar objectives is the ELF@Home project, that tries to exploit the advantages of elderly fitness to develop a self-care solution based on self-check of health status and fitness at home (www.elfathome.eu). Many other health status monitors and risk assessment systems are currently being developed, like [8], where current physical status and activity load of exercise are used to assess the risk level of sport activity in real-time.…”

Personal-Statistics-Based Heart Rate Evaluation in Anytime Risk Calculation Model

“…Besides the general rules and average thresholds, the personal factors, the person-dependent characteristics, and the unavoidable changing of the dynamic reactions of the human organism are also considered during the evaluation. In the proposed new method, the previously published real-time evaluation (for details, see [16]) is extended by patient-specific statisticsbased decision support. It is important to emphasize that the used statistics belong to one person, instead of the persons with similar characteristics, i.e., instead of typical average or maximum values.…”

“…We have used relative time during the comparison, because it is more authoritative than the absolute; it does not depend on such factors as the architecture, the software environment, and the size of the system. Consequently, if the system size changes, the relative execution time remains more or less unchanged (for details, see [16]). First, a histogram of the stored data [heart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP) values] measured under the same conditions is prepared during an offline preprocessing procedure.…”

Fuzzy Logic-based Risk Assessment Framework to Evaluate Physiological Parameters

“…Due to the large number of input factors and complex interactions between them, most systems examine only one measured parameter and some of its influential parameters [11], [12]. Usually the examined factors are physiological parameters [13], [14] but, in addition, motion detection can also be used [15], [16]. The aim of these systems may differ, they can also be applied as a teaching device in the fields of sports and rehabilitation [17], [18].…”