A PWM-Based Muscle Fatigue Detection and Recovery System

Ma, Bin; Li, Chunxiao; Wu, Zhaolong; Huang, Yulong; Zijp-Tan, Ada Chaeli van der; Tan, Shaobo; Li, Dongqi; Fong, Ada; Basetty, Chandan; Borchert, Glen M.; Huang, Jingshan

doi:10.1109/bibm.2018.8621418

Cited by 9 publications

(3 citation statements)

References 4 publications

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“…However, most of the above studies are based on small sample data sets and lack large-scale clinical applications. Moreover, it is possible to select too many features in feature engineering, resulting in too much computation [33]. Or choose too few features, resulting in low clas sification accuracy.…”

Section: Related Workmentioning

confidence: 99%

OSA Patient Monitoring System Based on the Internet of Things Framework

Cai¹,

Le²,

Ruan³

et al. 2020

Preprint

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This paper presents an OSA patient interactive monitoring system based on the Internet of Things (IoT) framework. This system allows OSA patients to get timely rescue when they are sleepy outside. Because the Beidou position marker has an interactive function, it can reduce the anxiety of the patient while waiting for the rescue. At the same time, if a friend helps the OSA patients to call the doctor, the friend can also report the patient's condition in time. This system uses the popular IoT framework. At the bottom is the data acquisition layer, which uses wearable sensors to collect vital signs from patients, with a focus on ECG and SpO2 signals. The middle layer is the network layer that transmits the collected physiological signals to the Beidou indicator using the Bluetooth Low Energy (BLE) protocol. The top layer is the application layer, and the application layer uses the mature rescue interactive platform of Beidou. Since the GPS indicator has not included the communication satellite, So it has no SMS function. OSA patients can only passively wait for a rescue. Moreover, due to the lack of satellites in Asia and the insufficient density of the ground-enhanced system, the positioning error of OSA patients is large. The Beidou system developed by China itself, the main coverage of the satellite is in Asia, and is equipped with a high-density ground-based augmentation system. Therefore, the Beidou model improves the positioning accuracy and is equipped with a special communication satellite, which increases the short message interaction function. Therefore, patients can report disease progression in time while waiting for a rescue. After our simulation test, the effectiveness of the OSA patient rescue monitoring system based on the Internet of Things framework and the positioning accuracy of OSA patients have been greatly improved. Especially when OSA patients work outdoors, the cell phone base station signal coverage is relatively weak. The satellite signal is well covered, plus the SMS function of the Beidou indicator. Therefore, the system can be used to provide timely patient progress and provide data support for the medical rescue team to provide a more accurate rescue plan. After a comparative trial, the rescue rate of OSA patients using the detection device of this system was increased by 15 percentage points compared with the rescue rate using only GPS satellite phones.

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Section: Related Workmentioning

confidence: 99%

OSA Patient Monitoring System Based on the Internet of Things Framework

Cai¹,

Le²,

Ruan³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Internet of Things (IoT) strategies are widely utilized in an array of areas including sensor related applications [15][16][17] and detection systems [18][19][20]. Furthermore, combining IoT, cloud computing, and machine learning has previously proven effective for precise, realtime sleep apnea detection and diagnosis [21].…”

Section: Introductionmentioning

confidence: 99%

Development of a support vector machine learning and smart phone Internet of Things-based architecture for real-time sleep apnea diagnosis

et al. 2020

BMC Med Inform Decis Mak

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Background The breathing disorder obstructive sleep apnea syndrome (OSAS) only occurs while asleep. While polysomnography (PSG) represents the premiere standard for diagnosing OSAS, it is quite costly, complicated to use, and carries a significant delay between testing and diagnosis. Methods This work describes a novel architecture and algorithm designed to efficiently diagnose OSAS via the use of smart phones. In our algorithm, features are extracted from the data, specifically blood oxygen saturation as represented by SpO2. These features are used by a support vector machine (SVM) based strategy to create a classification model. The resultant SVM classification model can then be employed to diagnose OSAS. To allow remote diagnosis, we have combined a simple monitoring system with our algorithm. The system allows physiological data to be obtained from a smart phone, the data to be uploaded to the cloud for processing, and finally population of a diagnostic report sent back to the smart phone in real-time. Results Our initial evaluation of this algorithm utilizing actual patient data finds its sensitivity, accuracy, and specificity to be 87.6%, 90.2%, and 94.1%, respectively. Discussion Our architecture can monitor human physiological readings in real time and give early warning of abnormal physiological parameters. Moreover, after our evaluation, we find 5G technology offers higher bandwidth with lower delays ensuring more effective monitoring. In addition, we evaluate our algorithm utilizing real-world data; the proposed approach has high accuracy, sensitivity, and specific, demonstrating that our approach is very promising. Conclusions Experimental results on the apnea data in University College Dublin (UCD) Database have proven the efficiency and effectiveness of our methodology. This work is a pilot project and still under development. There is no clinical validation and no support. In addition, the Internet of Things (IoT) architecture enables real-time monitoring of human physiological parameters, combined with diagnostic algorithms to provide early warning of abnormal data.

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“…In large part, this manuscript constitutes an extension of our previously published [14] initial description of our system. That said, this report describes upgrades to our platform including the addition of an infra-red transducer that can determine when someone is using the system, and also describes our system software in significantly greater detail.…”

Section: Introductionmentioning

confidence: 99%

Muscle fatigue detection and treatment system driven by internet of things

et al. 2019

BMC Med Inform Decis Mak

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BackgroundInternet of things is fast becoming the norm in everyday life, and integrating the Internet into medical treatment, which is increasing day by day, is of high utility to both clinical doctors and patients. While there are a number of different health-related problems encountered in daily life, muscle fatigue is a common problem encountered by many.MethodsTo facilitate muscle fatigue detection, a pulse width modulation (PWM) and ESP8266-based fatigue detection and recovery system is introduced in this paper to help alleviate muscle fatigue. The ESP8266 is employed as the main controller and communicator, and PWM technology is employed to achieve adaptive muscle recovery. Muscle fatigue can be detected by surface electromyography signals and monitored in real-time via a wireless network.ResultsWith the help of the proposed system, human muscle fatigue status can be monitored in real-time, and the recovery vibration motor status can be optimized according to muscle activity state.DiscussionEnvironmental factors had little effect on the response time and accuracy of the system, and the response time was stable between 1 and 2 s. As indicated by the consistent change of digital value, muscle fatigue was clearly diminished using this system.ConclusionsExperiments show that environmental factors have little effect on the response time and accuracy of the system. The response time is stably between 1 and 2 s, and, as indicated by the consistent change of digital value, our systems clearly diminishes muscle fatigue. Additionally, the experimental results show that the proposed system requires minimal power and is both sensitive and stable.

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A PWM-Based Muscle Fatigue Detection and Recovery System

Cited by 9 publications

References 4 publications

OSA Patient Monitoring System Based on the Internet of Things Framework

OSA Patient Monitoring System Based on the Internet of Things Framework

Development of a support vector machine learning and smart phone Internet of Things-based architecture for real-time sleep apnea diagnosis

Muscle fatigue detection and treatment system driven by internet of things

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