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Heart rate prediction based on cycling cadence using feedforward neural network
Mutijarsa K.a, Ichwan M.b, Utami D.B.b
a School of Electrical Engineering and Information Technology, Institut Teknologi Bandung, Bandung, Indonesia
b Information Technology, Institut Teknologi Nasional, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2016 IEEE.It is important to monitor heart rate during cycling. By monitoring heart rate during cycling, cyclists can control the cycling session such as cycling cadence to determine the intensity of exercise. By controlling the intensity of cycling, cyclists can avoid the risks of over training and heart attack. Exercise intensity can be measured by heart rate of cyclist. The heart rate can be measured by wearable sensor. But there are data that are not recorded by the sensor at a regular time for example, one second, two seconds, etc. So we need a prediction model of heart rate to complete the missing data. The purpose of this study is to create a predictive model for heart rate based on cycling cadence using Feedforward Neural Network. The inputs are heart rate (HRt) and cadence (cadt) on the second. The output is the predictive value of heart rate on the next second (HRt+1). Feedforward Neural Network is used as a mathematical model of the relationship between heart rate and cycling cadence. The prediction model was trained using 10000 data of cyclist number 1 in a cycling session. The test data use dataset of 6 cyclists. Experiments show that the prediction model generates the predictive value of heart rate that is close to the value of heart rate measured by the sensor. The error of training data is 2.43 while the average error of test data is 3.02.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]cadence,cycling,Exercise intensity,Heart rates,Over trainings,Prediction model,Predictive modeling,Predictive values[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]cadence,cycling,Feedforward Neural Network,heart rate,Prediction[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/IC3INA.2016.7863026[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]