In this paper, we have recently created self-driving cars and self-parking systems in human-friendly cars that can provide high safety and high convenience functions by recognizing the internal and external situations of automobiles in real time by incorporating next-generation electronics, informat...
In this paper, we have recently created self-driving cars and self-parking systems in human-friendly cars that can provide high safety and high convenience functions by recognizing the internal and external situations of automobiles in real time by incorporating next-generation electronics, information communication, and function control technologies. And with the development of connected cars, the ITS (Intelligent Transportation Systems) market is expected to grow rapidly. Intelligent Transportation System (ITS) is an intelligent transportation system that incorporates technologies such as electronics, information, communication, and control into the transportation system, and aims to implement a next-generation transportation system suitable for the information society. By combining the technologies of connected cars and Internet of Things with software features and operating systems, future cars will serve as a service platform to connect the surrounding infrastructure on their own. This study creates a research methodology based on the Enhanced Security Model in Self-Driving Cars model. As for the types of attacks, Availability Attack, Man in the Middle Attack, Imperial Password Use, and Use Inclusive Access Control attack defense methodology are used. Along with the commercialization of 5G, various service models using advanced technologies such as autonomous vehicles, traffic information sharing systems using IoT, and AI-based mobility services are also appearing, and the growth of smart transportation is accelerating. Therefore, research was conducted to defend against hacking based on vulnerabilities of smart cars based on artificial intelligence blockchain.
In this paper, we have recently created self-driving cars and self-parking systems in human-friendly cars that can provide high safety and high convenience functions by recognizing the internal and external situations of automobiles in real time by incorporating next-generation electronics, information communication, and function control technologies. And with the development of connected cars, the ITS (Intelligent Transportation Systems) market is expected to grow rapidly. Intelligent Transportation System (ITS) is an intelligent transportation system that incorporates technologies such as electronics, information, communication, and control into the transportation system, and aims to implement a next-generation transportation system suitable for the information society. By combining the technologies of connected cars and Internet of Things with software features and operating systems, future cars will serve as a service platform to connect the surrounding infrastructure on their own. This study creates a research methodology based on the Enhanced Security Model in Self-Driving Cars model. As for the types of attacks, Availability Attack, Man in the Middle Attack, Imperial Password Use, and Use Inclusive Access Control attack defense methodology are used. Along with the commercialization of 5G, various service models using advanced technologies such as autonomous vehicles, traffic information sharing systems using IoT, and AI-based mobility services are also appearing, and the growth of smart transportation is accelerating. Therefore, research was conducted to defend against hacking based on vulnerabilities of smart cars based on artificial intelligence blockchain.
2. Performance evaluation and analysis of the trained neural network system by comparing the prediction result with the real ECG signal.
The objective of our proposed idea is to improve PPG heart rate calculation while also avoiding the implementation of any expensive signal processing algorithm on the wearable device itself. This system will examine several sensors data to predict its corresponding ECG value for the purpose of extracting more precise heart rate data.
대상 데이터
Keras library is used for implementing neural network in ECG value predictor. Four hidden layers with 150 neurons each are used in this system. Since ECG value predictor is actually a neural network model for running a regression task, there is only one output neuron which is the corresponding ECG value.
As stated in Section 1, ECG value prediction in this system was evaluated by using dataset provided by [17]. In this dataset, 19 records from eight participants are provided. Participants' ages ranged from 22-32 years (mean 26.
이론/모형
In order to analyze the agreement between the original or ground truth ECG measurement and the prediction result, bland-altman plot [23] was used.
성능/효과
By only observing Fig. 7, we cannot really tell the peak value since the two signals overlap one another. Therefore, in Fig.
Therefore, in Fig. 8, we show the closeness of the ground truth ECG measurement and the prediction result on test data in separate plot so that we can observe every value in more detail. If we refer back to our Bland-Altman plot result, we can conclude that our mean difference and limits of agreement values will unlikely cause serious problem in calculating heart rate as it will not significantly affect the prediction of QRS wave and it also often happen during the other cardiac activities or, indicated by the value ranged from ±250mV and ±(cid:3398)250mV in this experiment.
Although this means that by predicting ECG value from PPG value, we will loss the other information, as shown by this error, it is sufficient to use it for calculating heart rate since heart rate calculation is usually derived from ECG's QRS complex, or to be pre- cise, the number of QRS complex within certain period, which basically indicates ventricular contraction
Based on the result shown above, ECG value predictor can successfully predict the ECG value based on its corresponding PPG, accelerometer and gyroscope value. This result may further assist PPG sensor on the wearable device to estimate more precise heart rate information, including but not limited to, heart rate value without the need to implement any expensive computation on the device itself.
ECG sensor can provide more precise heart rate calculation than PPG sensor as PPG sensor is heavily influenced by motion artefact; however, due to its simplicity, PPG sensor is still widely used for smart devices. Therefore, in this paper, we proposed a neural network-based system that can use PPG, accelerometer and gyroscope sensors data to predict the prediction accuracy to close to ECG value.
where 𝑚 denotes the slope of the line and 𝑏 denotes the 𝑦-intercept. For regression experiment, the ideal best-fitted line equation would have 𝑚 and 𝑏 equal to 1 and 0 respectively as it would result in 𝑦(cid:3404)𝑥, meaning there was no error in the prediction result. The best-fitted line equations for our experiment result on train and test data are 𝑦(cid:3404)0.
If we refer back to our Bland-Altman plot result, we can conclude that our mean difference and limits of agreement values will unlikely cause serious problem in calculating heart rate as it will not significantly affect the prediction of QRS wave and it also often happen during the other cardiac activities or, indicated by the value ranged from ±250mV and ±(cid:3398)250mV in this experiment
Moreover, it should be noticed that both data have the maximum value of ±2000mV and the minimum value of ±(cid:3398)2000mV
35 respectively, where 𝑥 indicates the original or ground truth ECG measurement and 𝑦 denotes the prediction result. Moreover, the correlation coefficient (𝑟) between the ground truth ECG measurement and the prediction result on training data is 0.95 (𝑝(cid:3407)0.00001) while the experiment on test data yields a correlation coefficient of 0.904 (𝑝(cid:3407)0.00001). These results indicate that our prediction on both training and test dataset are strongly correlated with the ground truth value.
The results have shown that our proposed ECG value predictor can precisely predict the corresponding ECG value from a combination of PPG, accelerometer and gyroscope values. Our deep learning model is also implemented on the cloud, meaning there is no need to run any complex algorithm on the PPG device itself.
This work is to collect more dataset that at least, including size and weight from several different people in order to conduct further research on predicting ECG value across different peo- ple. The alternative way to this solution is to develop an algorithm that can calculate the average perfusion of the blood and either adaptively select the neural network model that can suit the results better or add this result to the neural network input features.
The average of the difference (𝑑̅) between our ECG prediction value and the ground truth ECG measurement is 2.29 while 199.12 and (cid:3398)194.54 are the limits of agreement (±1.96 standard deviation of the difference)
For regression experiment, the ideal best-fitted line equation would have 𝑚 and 𝑏 equal to 1 and 0 respectively as it would result in 𝑦(cid:3404)𝑥, meaning there was no error in the prediction result. The best-fitted line equations for our experiment result on train and test data are 𝑦(cid:3404)0.94𝑥(cid:3398)0.96 and 𝑦(cid:3404)0.83𝑥(cid:3398)2.35 respectively, where 𝑥 indicates the original or ground truth ECG measurement and 𝑦 denotes the prediction result. Moreover, the correlation coefficient (𝑟) between the ground truth ECG measurement and the prediction result on training data is 0.
We have tested the performance of our proposed system by using dataset provided by [17]. The results have shown that our proposed ECG value predictor can precisely predict the corresponding ECG value from a combination of PPG, accelerometer and gyroscope values. Our deep learning model is also implemented on the cloud, meaning there is no need to run any complex algorithm on the PPG device itself.
If we refer back to our Bland-Altman plot result, we can conclude that our mean difference and limits of agreement values will unlikely cause serious problem in calculating heart rate as it will not significantly affect the prediction of QRS wave and it also often happen during the other cardiac activities or, indicated by the value ranged from ±250mV and ±(cid:3398)250mV in this experiment. Therefore, this should bring us to our ultimate conclusion that our ECG value prediction can replace the original or ground truth ECG measurement.
00001). These results indicate that our prediction on both training and test dataset are strongly correlated with the ground truth value.
후속연구
ECG sensor can provide more precise heart rate calculation than PPG sensor as PPG sensor is heavily influenced by motion artefact; however, due to its simplicity, PPG sensor is still widely used for smart devices. Therefore, in this paper, we proposed a neural network-based system that can use PPG, accelerometer and gyroscope sensors data to predict the prediction accuracy to close to ECG value.
This means that even though two different persons have the exact same electrical activities in their heart at the same time, the perfusion of blood in their wrist at that time is not necessarily the same. This problem can, in fact, be solved by adding more features such as weight and height or even blood volume estimation as our neural network in- put; however, the lack of available public dataset that related to this prevents us to prove this hypothesis.
Based on the result shown above, ECG value predictor can successfully predict the ECG value based on its corresponding PPG, accelerometer and gyroscope value. This result may further assist PPG sensor on the wearable device to estimate more precise heart rate information, including but not limited to, heart rate value without the need to implement any expensive computation on the device itself. It goes without saying that our proposed neural network and cloud computing-based heart rate calculation from PPG data will improve the efficiency of many existing systems that based on heart rate calculation and internet of things (IoT) such as that of [24].
There is one important area for future work. This work is to collect more dataset that at least, including size and weight from several different people in order to conduct further research on predicting ECG value across different peo- ple. The alternative way to this solution is to develop an algorithm that can calculate the average perfusion of the blood and either adaptively select the neural network model that can suit the results better or add this result to the neural network input features.
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