## 4 Discussion  
This research proposed a new idea that uses static human body data (input of the model) to predict the slope (output of the model) through the machine learning model. Then, we can use the Equation (1) to easily figure out the parameters of the cycling equipment required to reach the THR. We use wattage here only because most smart cycling equipment uses it as the input parameter (although there are other examples, such as the speed of treadmills and rowing machine). If MPH is going to be an input parameter for most smart cycling equipment in the future, then the slope we need might be in terms of heart rate and MPH. For wattage prediction, we focused on verifying our ideas and the model's concepts. The purpose of this was not only to determine the final specific regression Equation (2), because in our main scenario (intelligent health system), a high-performance computer coupled with network technology can efficiently use and optimize the model, without the need of specific regression formulas. Our study showed that the slope could be accurately predicted through the static data we had, and that the predicted results could be further optimized through the key information obtained from the questionnaire, so as to expand the applicable scope of the model. After the model training is completed, using slope can help exercisers reach the target intensity accurately without the need for smart wearable devices. In daily use, the exerciser only needs to enter the predicted wattage into the smart cycling equipment to reach the target intensity. Of course, we certainly encourage people who are able to utilize wearable devices to use them because, in this way, we can obtain more in-exercise data. These data also do not require exercisers to observe and record. The device automatically uploads data when it is connected to the network. These data can be utilized to continuously optimize our models to provide better services. Our method is relatively complex. It requires specific static body data monitoring equipment, rather than directly and continuously monitoring heart rate to reach exercise intensity. However, our method has specific usage scenarios because, among Chinese exercisers who use cycling equipment, only some people use exercise prescriptions. They obtained their prescriptions from professional departments, so they could easily obtain the static body data when they visit. Then, they can use our method without buying this equipment (body composition analyzer, an ultrasonic bone density meter, and other equipment). Other exercisers who did not utilize exercise prescriptions exercised more casually, lacked exercise knowledge, or even did not know what THR is. Even if they have a smart watch, they may be unable to accurately and scientifically use it to reach the THR. For those exercisers who have the ability to use a wearable device to reach the intensity without our method, they are more likely to suffer sports injuries during exercise, especially when they implement prescriptions for the first time because exercisers need to continue to pay attention to the changes in the heart rate data of the wearable device and whether the data remains stable throughout the exercise. When exercisers  
Figure 3: The slope obtained by the prediction and the slope measured in the experiment. Training set on the left, test set on the right. engage in exercises of moderate intensity or above, a lack of concentration can easily to cause sport injuries. On the other hand, many exercisers are not even sure if they can maintain their THR heart rate and complete the recommended exercise time. In this case, the exerciser may feel palpitation, chest tightness, cramps, and so on during the exercise, which also may cause sport injuries. When the training set was initially set to 35 samples, the adjusted R\({}^{2}\) was 0.63, while the adjusted R\({}^{2}\) was 0.71 after excluding high error samples from the questionnaires or experimental data.