Building a Personalized Fitness Recommendation Application based on Sequential Information

Abstract

Now-a-days sports plays a very important role in the life of the human being and it allows to keep him healthy and make him always active. Sport is essential for people to have a healthy mind. However, the practice of a sport can have negative effects on the body and human health if it is practiced incorrectly or if it is not adapted to the body or the human health. This is why, in this paper, we have proposed a recommendations system that allows the selection of the right person to practice the right sport according to several factors such as heart rate, speed and size. The implementation was applied to the FitRec dataset with the help of SPARK tool, and the results show that the proposed method is capable of generating the appropriate training for different groups according to their information, where each group gets the appropriate training. The grouping of this data was done by the k-means method.

Physical Activities Recommender System Based on Sequential Data Use K-Mean Clustering

“…The utilization of sequential data in datasets makes it easier for researchers to develop more sophisticated recommendation systems. (Abdulaziz et al, 2021) They researched to create a personalized recommendation system by utilizing sequential data. The system aims to determine user categories in sports based on heart rate, pace, and altitude parameters during physical activities.…”

“…The following method applied is data pre-processing using data that has been adequately processed. Before the clustering stage, the data pre-processing step is vital to ensure that clustering results can be obtained quickly and prevent errors (Abdulaziz et al, 2021). The first step in data pre-processing is to remove the timestamp column, which allows the use of data in the clustering process, considering that the timestamp column is a DateTime data type.…”

A Novel Hybrid Approach for Detection of Type-2 Diabetes in Women Using Lasso Regression and Artificial Neural Network

“…Novel Hybrid Approach for Detection of Type-2 Diabetes in Women Using Lasso Regression and Artificial Neural Network do not accept missing values. In our experimental dataset, missing values are represented by zero[15]. Using Jupyter Notebook, we retrieved the number of missing data points in glucose, blood pressure, skin thickness, insulin, BMI, and age by replacing zero with NaN.…”