Nonlinear fuzzy control of human heart rate during aerobic endurance training with respect to significant model variations

Abstract

In the field of physical activity a significant importance is held by aerobic endurance training, which is a relatively low intensity exercise that depends primarily on aerobic energy generating processes. This type of training is used for the overall endurance and fitness of the body by engaging all the major systems of the body and pushing the limits of their functions with the ultimate goal of adapting these structures to the stress of the physical effort and thus improving the performance output. However, using classic training methods, it is often impossible to ensure the necessary operating points in order to obtain the desired results. The human heart rate model with respect to the velocity of walking or running presents inherent nonlinearities that are required to be taken into account when designing computer integrated training aids. This paper implements such a tool using a fuzzy control system for human heart rate during aerobic endurance training. The controller is tested and validated using two nonlinear human-on-treadmill models, and it presents robustness to significant model variations, as well as uncertainties in the model parameters and white noise type of disturbances. The designed control system ensures desired heart rate profiles, finding its usefulness in supporting the training configuration process by specialized professionals.

Measurement, Prediction, and Control of Individual Heart Rate Responses to Exercise—Basics and Options for Wearable Devices

“…A commonly used application for HR models is control of HR on a treadmill (Mazenc et al, 2010 ; Nguyen et al, 2011 ; Pătraşcu et al, 2014 ; Hunt and Fankhauser, 2016 ; Hunt and Liu, 2017 ), on a bicycle ergometer (Mohammad et al, 2012 ; Paradiso et al, 2013 ; Argha et al, 2014 , 2015a , b ; Leitner et al, 2014 ), for gait training (Koenig et al, 2011 ) or to control strain in exergames (Sinclair et al, 2009 ). Even apart from strain or stress control, use of HR models is conceivable for many other areas like training planning (Brzostowski et al, 2013 ; Schäfer et al, 2015 ), generating individualized training zones based on past training sessions, keeping track of performance development and adjustment of HR training zones, potentially enhancing accuracy by predicting the HR after a model is individualized and adjust the displayed HR according to measurement and model prediction, compensate missing or incorrectly detected HR values [see Jang et al ( 2016 )], and more.…”

Computational intelligence in sports: Challenges and opportunities within a new research domain

“…Stability analysis of motion patterns in biathlon shooting [32] Jump detection [49] Nonlinear fuzzy control of human heart rate [47] Ambient intelligence systems for personalized sport training [60] Peloton phase oscillations [58] Problem of nutrition during sport competitions [9] Avoiding over-training [12] consumption and respiratory exchange ratio (RER). In the past, these measures could only be measured in the ambulance.…”

Training and Evaluation of Human Cardiorespiratory Endurance Based on a Fuzzy Algorithm

“…A fuzzy PID based on the classic PI control approach was confirmed in a system that included minimizing heart-rate deviations during a treadmill exercise [14]. The controller was tested and validated using two nonlinear human-on-treadmill models [15,16].…”