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

Abstract

The use of wearable devices or “wearables” in the physical activity domain has been increasing in the last years. These devices are used as training tools providing the user with detailed information about individual physiological responses and feedback to the physical training process. Advantages in sensor technology, miniaturization, energy consumption and processing power increased the usability of these wearables. Furthermore, available sensor technologies must be reliable, valid, and usable. Considering the variety of the existing sensors not all of them are suitable to be integrated in wearables. The application and development of wearables has to consider the characteristics of the physical training process to improve the effectiveness and efficiency as training tools. During physical training, it is essential to elicit individual optimal strain to evoke the desired adjustments to training. One important goal is to neither overstrain nor under challenge the user. Many wearables use heart rate as indicator for this individual strain. However, due to a variety of internal and external influencing factors, heart rate kinetics are highly variable making it difficult to control the stress eliciting individually optimal strain. For optimal training control it is essential to model and predict individual responses and adapt the external stress if necessary. Basis for this modeling is the valid and reliable recording of these individual responses. Depending on the heart rate kinetics and the obtained physiological data, different models and techniques are available that can be used for strain or training control. Aim of this review is to give an overview of measurement, prediction, and control of individual heart rate responses. Therefore, available sensor technologies measuring the individual heart rate responses are analyzed and approaches to model and predict these individual responses discussed. Additionally, the feasibility for wearables is analyzed.

“HIIT” the ExerCube: Comparing the Effectiveness of Functional High-Intensity Interval Training in Conventional vs. Exergame-Based Training

“…Future fHIIT exergames should therefore allow for a more individual game difficulty adaption by allowing users to manually insert their pre-assessed individual CHR max or more specific HR prediction models (Ludwig et al, 2018), to then serve as the basis for the algorithm (Hoffmann et al, 2016). In the interest of safety, the implemented explorative algorithm used in the present study could also be refined further to check HR-and if required-adapt more frequently (e.g., every 10-20 s from the very beginning of the game).…”

Including the Past: Performance Modeling Using a Preload Concept by Means of the Fitness-Fatigue Model

“…Usage of mathematical models itself in sport science has become more relevant in the last years. Additionally, the increase in usage of wearables can improve and simplify analysis of several topics in the area of training (Ludwig, Hoffmann, Endler, Asteroth, & Wiemeyer, 2018;Passfield & Hopker, 2016).…”

Evaluation of mobile applications for fitness training and physical activity in healthy low-trained people - A modular interdisciplinary framework

“…In the majority of applications, standard smartphone sensors (i.e., accelerometer, gyroscope, orientation sensors, camera, and GPS, He & Li, 2013) are used to assess simple PA indicators such as step or activity counts, covered distance, velocity or activity (profiles), total training time, repetitions and energy expenditure (e.g., Knight et al, 2015;Wiemeyer et al, 2016). In addition, smartphones can be coupled to a bunch of wireless biosensors such as optical or electric sensors via USB, Bluetooth, ANT, ZigBee or WiFi, to measure HR (Ludwig et al, 2018), hormones, electrolytes, or metabolites (Roda et al, 2016;Kassal, Steinberg, & Steinberg, 2018). Currently, the most commonly measured parameters from external sensors are HR and EE (Knight et al, 2015).…”

“…The physiological signal measured most often by mobile PA and FT apps is heart rate (HR; Mukhopadhyay, 2015). Regarding HR, numerous procedures and devices are available (Ludwig et al, 2018). Electrographical procedures such as electrocardiogram (ECG) are considered the gold standard; they show the best validity compared to other procedures.…”