Intelligent healthcare data segregation using fog computing with internet of things and machine learning

Kishor, Amit; Chakraborty, Chinmay; Jeberson, Wilson

doi:10.1504/ijesms.2021.115533

Cited by 35 publications

(20 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To overcome the shortcomings of existing knee injury prediction algorithms, an intelligent knee injury prediction method is given. Experiments have shown that this technique can increase both the accuracy of knee injury prediction and its precondition in football players [ 26 ]. This study focused on evaluating the risk prediction in football using artificial intelligence technique.…”

Section: Introductionmentioning

confidence: 99%

The Application of Artificial Intelligence in Football Risk Prediction

Qiao

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Football is the most popular sports in the World, with an estimated global following of 4.0 billion fans worldwide. Football draws attention from people of various age groups. The result of the game only decides the performance of the team and individual players. The player has to train smarter to avoid a career-ending injury. Sports have also entered into the new era of artificial intelligence as any industry. Artificial intelligence (AI) in football acts like a teammate to the players and also plays the role of an assistant coach. The coach uses artificial intelligence and incorporates it into the traditional way of training. The Football Associations have already implemented sensors to collect data in the form of technologies such as Video Assistant Referee and Goal Line Technology. Additionally, the quality of the players and the coaches is improved with smart technological implementation. This technology itself incorporates the utilization of smart technologies for data acquisition using sensor networks and an intelligent data analysis. The proposed algorithm is compared with the fuzzy logic model (FLM) and found that it is 7.2% of higher risk predication by the proposed model than the existing.

show abstract

Section: Introductionmentioning

confidence: 99%

The Application of Artificial Intelligence in Football Risk Prediction

Qiao

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Knee joint injury is one of the most common injuries but very crucial for players to play any game [ 6 ]. Football players are unable to perform due to knee injuries during training and competition [ 7 ]. Normal training and participation in the competition seriously hindered the normal play and promotion of the football players' competitive level.…”

Section: Introductionmentioning

confidence: 99%

Healthcare Data-Based Prediction Algorithm for Potential Knee Joint Injury of Football Players

Yu¹,

Ye²

2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

It is important to predict the potential harm to the knee joint in order to prevent football players from inflicting numerous injuries to the knee during activity. Numerous professionals have been drawn to this subject, and many viable prediction systems have been developed. Prediction of potential knee joint injury is critical to effectively avoid knee joint injury during exercise. The current prediction algorithms are mainly implemented through expert interviews, medical reports, and historical documents. The algorithms have problems with low prediction accuracy or precision values. There is a need to understand more knee injury factors and improve the prediction accuracy; hence, the intelligent prediction algorithm for potential injury of knee joints of football players is proposed in this paper. Firstly, the characteristics of the knee joint injury and the injury factors of the football players are gathered and analyzed. Then, the damage is predicted by the similarity measurement. The experimental results show that the proposed algorithm has higher prediction accuracy and shorter time. According to the findings of a survey that collected healthcare data, several key factors contribute to football knee injuries. To a degree, this algorithm can predict the likelihood of a football player’s knee injury.

show abstract

“…With this method, if the image intensity is less than a predetermined constant, the image will be converted to black, and if the image intensity is greater than that predetermined constant, it will be converted to white. The values of the pixels that make up an image are what are utilized to divide it into separate areas, such as a particle region, which contains the things that are being examined, and a background region [28,30]. There are two aspects to consider when configuring threshold values: the first is determining whether the user wants to override the default thresholds or configure component-specific thresholds; the second is defining the thresholds for every measure of the test, with the type of thresholds used for each measure depending on which thresholds are most appropriate for that particular measure.…”

Section: Threshold Processing Methodsmentioning

confidence: 99%

“…Signal recovery: perform single‐branch recovery on the decomposed coefficients [27–31], that is, use a 3 to restore the low‐frequency signal d 3 of the third level, and use

cD3

to restore the high‐frequency signal of the third level. Since the high‐frequency signal of the second layer restores d 2 to

cD2

, and the high‐frequency signal of the first layer restores d 1 to

cD1

, the three‐layer decomposition of the S signal can be expressed by formula (8)

\begin{equation}S = a3 + d3 + d2 + d1\end{equation}

…”

Section: Methodsmentioning

confidence: 99%

Heart rate monitoring of physical fitness training load based on wavelet transform

Zhang¹,

Sharma

Samori

2022

The Journal of Engineering

View full text Add to dashboard Cite

The detection of fluctuations is a crucial step in the process of isolating heart rate oscillation signals. This is necessary to circumvent the issue wherein a variety of auditory signals will invariably interfere with the collection of centre velocity signals during physical activity, which has a significant impact on the detection efficiency of automated testing. To obtain heart rate fluctuation signals that are more precise, the paper proposes a method of R‐wave identification that is based on heartbeat conversion while the athlete is exercising. The proposed model relies heavily on the technique of wave analysis to filter out the background noise of the heartbeat when the athlete exercises and isolates the R‐wave signal of the heartbeat from the background noise. The proposed algorithm is evaluated in terms of noise interference that removes approximately 90% of noise, the heart rate data which is obtained after the noise is normally distributed, the average detection rate of the R‐wave algorithm is 98.65%. It has been demonstrated that the detection accuracy of the proposed algorithm is sufficient to meet the requirements of generating heart rate oscillatory signals.

show abstract

Intelligent healthcare data segregation using fog computing with internet of things and machine learning

Cited by 35 publications

References 0 publications

The Application of Artificial Intelligence in Football Risk Prediction

The Application of Artificial Intelligence in Football Risk Prediction

Healthcare Data-Based Prediction Algorithm for Potential Knee Joint Injury of Football Players

Heart rate monitoring of physical fitness training load based on wavelet transform

Contact Info

Product

Resources

About