Heart rate regulation during cycle-ergometer exercise via bio-feedback

© 2015 IEEE. This paper explains our developed control system which regulates the heart rate (HR) to track a desired trajectory. The controller is indeed a non-conventional non-model-based proportional, integral and derivative (PID) controller. The controller commands are interpreted as biofeedback auditory commands. These commands can be heard and implemented by the exercising subject as a part of the control-loop. However, transmitting a feedback signal while the pedals are not in the appropriate position to efficiently exert force may lead to a cognitive disengagement of the user from the feedback controller. This note explains a novel form of control system regarding as 'actuator-based event-driven control system', designed specifically for the purpose of this project. We conclude that the developed event-driven controller makes it possible to precisely regulate HR to a predetermined HR profile

Designing adaptive integral sliding mode control for heart rate regulation during cycle-ergometer exercise using bio-feedback

© 2015 IEEE. This paper considers our developed control system which aims to regulate the exercising subjects' heart rate (HR) to a predefined profile. The controller would be an adaptive integral sliding mode controller. Here it is assumed that the controller commands are interpreted as biofeedback auditory commands. These commands can be heard and implemented by the exercising subject as a part of the control-loop. However, transmitting a feedback signal while the pedals are not in the appropriate position to efficiently exert force may lead to a cognitive disengagement of the user from the feedback controller. To address this problem this paper will employ a different form of control system regarding as 'actuator-based event-driven control system'. This paper will claim that the developed event-driven controller makes it possible to effectively regulate HR to a predetermined HR profile

On heart rate regulation in cycle-ergometer exercise

© 2014 IEEE. In this paper, we have focused on the issue of regulating the human heart rate (HR) to a predefined reference trajectory, especially for cycle-ergometer exercise used for training or rehabilitation. As measuring HR is relatively easy compared to exercise intensity, it has been used in the wide range of training programs. The aim of this paper is to develop a non-model-based control strategy using proportional, integral and derivative (PID) controller/relay controller to regulate the HR to track a desired trajectory. In the case of using PID controller, the controller output signal is interpreted as a voice or auditory command, referred to as biofeedback, which can be heard by the exercising subject as a part of the control-loop. Alternatively, the relay controller output signals can be converted to some special words which can be recognised by the exerciser. However, in both cases, to effectively communicate to the user a change in exercise intensity, the timing of this feedback signal relative to the positions of the pedals becomes quite critical. A feedback signal delivered when the pedals are not in a suitable position to efficiently exert force may be ineffective and may lead to a cognitive disengagement of the user form the feedback controller. In this paper we examine the need and the consequence of synchronising the delivery of the feedback signal with an optimal and user specific placement of the pedal

Advances in Discrete-Time Sliding Mode Control Theory and Applications

The focus of this book is on the design of a specific control strategy using digital computers

Online auto-calibration of triaxial accelerometer with time-variant model structures

© 2017 Elsevier B.V. In this paper, an online auto-calibration method for MicroElectroMechanical Systems (MEMS) triaxial accelerometer (TA) is proposed, which can simultaneously identify the time-dependent model structure and its parameters during the changes of the operating environment. Firstly, the model as well as its associated cost function is linearized by a new proposed linearization approach. Then, exploiting an online sparse recursive least square (SPARLS) estimation, the unknown parameters are identified. In particular, the online sparse recursive method is based on an L1-norm penalized expectation-maximum (EM) algorithm, which can amend the model automatically by penalizing the insignificant parameters to zero. Furthermore, this method can reduce computational complexity and be implemented in a low-cost Micro-Controller-Unit (MCU). Based on the numerical analysis, it can be concluded that the proposed recursive algorithm can calculate the unknown parameters reliably and accurately for most MEMS triaxial accelerometers available in the market. Additionally, this method is experimentally validated by comparing the output estimations before and after calibration under various scenarios, which further confirms its feasibility and effectiveness for online TA calibration

Portable sensor based dynamic estimation of human oxygen uptake via nonlinear multivariable modelling

Noninvasive portable sensors are becoming popular in biomedical engineering practice due to its ease of use. This paper investigates the estimation of human oxygen uptake (VO2) of treadmill exercises by using multiple portable sensors (wireless heart rate sensor and triaxial accelerometers). For this purpose, a multivariable Hammerstein model identification method is developed. Well designed PRBS type of exercises protocols are employed to decouple the identification of linear dynamics with that of nonlinearities of Hammerstein systems. The support vector machine regression is applied to model the static nonlinearities. Multivariable ARX modelling approach is used for the identification of dynamic part of the Hammerstein systems. It is observed the obtained nonlinear multivariable model can achieve better estimations compared with single input single output models. The established multivariable model has also the potential to facilitate dynamic estimation of energy expenditure for outdoor exercises, which is the next research step of this study. © 2008 IEEE

An equivalent circuit model for onset and offset exercise response

© 2014 Zhang et al. Background: The switching exercise (e.g., Interval Training) has been a commonly used exercise protocol nowadays for the enhancement of exerciser's cardiovascular fitness. The current difficulty for simulating human onset and offset exercise responses regarding the switching exercise is to ensure the continuity of the outputs during onset-offset switching, as well as to accommodate the exercise intensities at both onset and offset of exercise. Methods: Twenty-one untrained healthy subjects performed treadmill trials following both single switching exercise (e.g., single-cycle square wave protocol) and repetitive switching exercise (e.g., interval training protocol). During exercise, heart rate (HR) and oxygen uptake (VO2) were monitored and recorded by a portable gas analyzer (K4b2, Cosmed). An equivalent single-supply switching resistance-capacitor (RC) circuit model was proposed to accommodate the observed variations of the onset and offset dynamics. The single-cycle square wave protocol was utilized to investigate the respective dynamics at onset and offset of exercise with the aerobic zone of approximate 70% - 77% of HRmax, and verify the adaption feature for the accommodation of different exercise strengths. The design of the interval training protocol was to verify the transient properties during onset-offset switching. A verification method including Root-mean-square-error (RMSE) and correlation coefficient, was introduced for comparisons between the measured data and model outputs. Results: The experimental results from single-cycle square wave exercises clearly confirm that the onset and offset characteristics for both HR and VO2are distinctly different. Based on the experimental data for both single and repetitive square wave exercise protocols, the proposed model was then presented to simulate the onset and offset exercise responses, which were well correlated indicating good agreement with observations. Conclusions: Compared with existing works, this model can accommodate the different exercise strengths at both onset and offset of exercise, while also depicting human onset and offset exercise responses, and guarantee the continuity of outputs during onset-offset switching. A unique adaption feature by allowing the time constant and steady state gain to re-shift back to their original states, more closely mimics the different exercise strengths during normal daily exercise activities

Modulation of neural regulators of energy homeostasis, and of inflammation, in the pups of mice exposed to e-cigarettes

© 2018 Background: Maternal smoking can lead to perturbations in central metabolic regulators such as neuropeptide Y (NPY) and pro-opiomelanocortin (POMC) signalling components in offspring. With the growing interest in e-cigarettes as a tobacco replacement, this short report assessed central metabolic regulation in offspring of mouse dams exposed to e-cigarettes. We examined the impact of continuous use of e-cigarettes, and e-cigarette replacement of tobacco cigarettes during pregnancy. Supplementation of an antioxidant L-carnitine was also co-used with tobacco cigarette in the mother to determine whether the impact of maternal tobacco smoking was oxidative stress driven. Methods: Balb/c mice were exposed to either nicotine-containing (E-cig18) or nicotine-free (E-cig0) e-cigarette aerosols or tobacco smoke (SE) prior to mating and until their pups were weaned. After mating, two SE sub-groups were changed to E-cig18 exposure (Replacement), or supplementation L-carnitine while SE was continued. Male offspring were studied at weaning age. Results: The offspring of E-cig0 dams were the heaviest with the most body fat. Replacing SE with E-cig18 during pregnancy resulted in offspring with significantly less body fat. E-cig0 offspring had significantly increased mRNA expression of brain NPY and iNOS. Maternal SE upregulated mRNA expression of NPY, NPY Y1 receptor, POMC downstream components, and iNOS expression, which were normalised in Replacement offspring, but only partially normalised with maternal L-carnitine supplementation during gestation and lactation. Conclusions: Maternal exposure to either tobacco and nicotine-free e-cigarettes lead to disturbances in the level of central homeostatic control markers in offspring, suggesting that maternal exposure to e-cigarettes is not without risks

Impact of maternal e-cigarette vapor exposure on renal health in the offspring

© 2019 New York Academy of Sciences. Maternal smoking during pregnancy is a significant risk factor of renal pathology in the offspring. E-cigarettes are perceived to be a safe option and are increasingly used by pregnant women either continuously during pregnancy or as a replacement for tobacco cigarettes. This study aimed to determine the effects of replacing tobacco cigarettes with e-cigarettes during pregnancy, and continuous e-cigarette use during pregnancy on the offspring's kidneys. Female Balb/c mice were exposed to either air (sham) or tobacco cigarette smoke (SE) for 6 weeks prior to mating, during gestation and lactation. A subset of the “SE group” received e-cigarette vapor (containing nicotine) after mating until pups weaned. Additional female mice were continuously exposed to e-vapor (either with or without nicotine) for 6 weeks prior to mating until pups weaned. Kidneys and urine from the male offspring were assessed at postnatal day 1, day 20 (weaning), and 13 weeks of age (adulthood). E-cigarette replacement was less detrimental to renal development and albuminuria than continuous SE during pregnancy. However, continuous e-vapor exposure during pregnancy increased markers of oxidative stress, inflammation, and fibrosis in the adult offspring, independent of nicotine. E-cigarette use during pregnancy confers future risk to the offspring's kidneys