A Personalized and Context-Aware Mobile Assistance System for Cardiovascular Prevention and Rehabilitation

Theobalt, Alexandra; Feodoroff, Boris; Werth, Dirk; Loos, Peter

doi:10.1007/978-3-642-37988-8_11

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…This review also points out that blood oxygen saturation may be used to control the stress caused by exercise [53,54] and also by environmental factors such as pollution [66]. Blood oxygen saturation is commonly used in the medical field to supervise patients in intensive care units and during the cardiac or pulmonary rehabilitation process [102].…”

Section: Discussionmentioning

confidence: 99%

“…They are used in almost every motor control system since they retrieve the cyclist's power or torque produced and their rate. This information correlated that the heart rate can be used to estimate the state of fatigue of the cyclist [52,53,57,65,68] as well as the road difficulty to assist the user in the best manner and reduce strain during their travels. GPS and accelerometer data are mainly used for road classification as slope recognition (flat, uphill, and downhill) and road segmentation [72,73], however they can be used in other applications (e.g., fall detection [74][75][76]).…”

Section: Sensorsmentioning

confidence: 99%

“…Nowadays, most people own a smartphone which can be exploited as a human machine interface (HMI). With these devices, users can parameter the algorithm and have visual updates of their physical activity in real-time [53,55,57,58,77]. It is possible to process the information and aid the controller in providing a better response, or transmit the collected data to a server or personal computer (PC) for further analysis [59,[63][64][65][66][67].…”

Section: Architecturesmentioning

confidence: 99%

“…Uploading data in real-time to a server is mainly used to retrieve, process, and analyze data, providing feedback to users [53,59,60,64,65,69] and allowing the health professionals to potentially monitor their patients remotely and adjust the algorithm (i.e., surveillance thresholds, training workload).…”

Section: Architecturesmentioning

confidence: 99%

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Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review

Avina-Bravo

Cassirame

Escriba

et al. 2022

Sensors

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This paper aims to provide a review of the electrically assisted bicycles (also known as e-bikes) used for recovery of the rider’s physical and physiological information, monitoring of their health state, and adjusting the “medical” assistance accordingly. E-bikes have proven to be an excellent way to do physical activity while commuting, thus improving the user’s health and reducing air pollutant emissions. Such devices can also be seen as the first step to help unhealthy sedentary people to start exercising with reduced strain. Based on this analysis, the need to have e-bikes with artificial intelligence (AI) systems that recover and processe a large amount of data is discussed in depth. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used to complete the relevant papers’ search and selection in this systematic review.

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Section: Discussionmentioning

confidence: 99%

Section: Sensorsmentioning

confidence: 99%

Section: Architecturesmentioning

confidence: 99%

Section: Architecturesmentioning

confidence: 99%

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Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review

Avina-Bravo

Cassirame

Escriba

et al. 2022

Sensors

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“…Dosed walking should be done either before or 1 h after eating. Electrocardiogram data are recorded before and after exercises (Theobalt et al 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Rehabilitation of athletes following cardiovascular events

Паначев

Ponomarev

Ковальчук

et al. 2020

J Public Health (Berl.)

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Aim The relevance of this study lies in the fact that cardiovascular mortality decreases in countries promoting active healthy lifestyles. The authors conducted experimental studies of the cardiovascular parameter prediction accuracy at the initial and final stages of the rehabilitation process. Subjects and methods The experiments showed sufficient accuracy of the constructed models. In particular, the maximum relative error was 11% for the simulation of the tolerable physical activity duration and 5.5% for the simulation of the exercise tolerance. The novelty of the study is that experimental studies were performed to predict the accuracy of the patient's condition at the final stage of the rehabilitation process by the assessment of exercise tolerance using bicycle ergometry test data only at the initial stage.Results The experiments showed sufficient accuracy of the constructed method. In particular, the maximum relative error of blood pressure simulation at the final stage of the rehabilitation period was 5%. The authors propose an approach to the implementation of exercise monitoring, according to which the observed pulse values are supplemented by simulated pressure values. Conclusions Experimental studies of the accuracy of the proposed approach have shown its sufficient accuracy. In particular, the maximum relative error in blood pressure simulation was 8.6%.

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