Automatic classification of physical exercises from wearable sensors using small dataset from non-laboratory settings

Chowdhury, Alok Kumar; Farseev, Aleksandr; Chakraborty, Prithwi Raj; Tjondronegoro, Dian; Chandran, Vinod

doi:10.1109/lsc.2017.8268156

Cited by 5 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We found that there is no universal threshold value for classifying heart rate; researchers design their own standards based on their study. Considering recommendations from previous studies, we selected 45 to 130 beats per minute (bpm) as an acceptable range for our experimental simulation (Chakraborty et al, 2015; Chowdhury et al 2017). We tested the performances of linear interpolation, Kalman, spline and Stineman interpolation.…”

Section: Initial Methodologymentioning

confidence: 99%

Preliminary Simulation for Obtaining Accurate Heart Rate Data Using Imputation Methods

Ramnarine-Rieks

Deshmukh

Sang

et al. 2021

Proceedings of the Association for Information Science and Tech

View full text Add to dashboard Cite

Increasingly data acquired from consumer activity trackers are being used by clinicians to monitor and treat patients. There are many reported cases where medical doctors leverage data retrieved from these devices to successfully treat and diagnose patients with heart conditions. In addition, hospitals are testing these devices in clinical trials to support telemedicine. One challenge is sensor susceptibility to distortions, resulting in missingness and impacting on validity. In this study we are exploring optimal methods of imputation through an experimental simulation using heart rate interval data. For this initial methodology we are utilizing Fitbit heart rate data to investigate performances of four imputation methods using 1000 Monte Carlo runs. Normalized errors from simulated missingness experiments were determined by comparing observed and predicted values. Preliminary findings show that linear and Kalman imputation methods are most appropriate when imputing heart rate data.

show abstract

Section: Initial Methodologymentioning

confidence: 99%

Preliminary Simulation for Obtaining Accurate Heart Rate Data Using Imputation Methods

Ramnarine-Rieks

Deshmukh

Sang

et al. 2021

Proceedings of the Association for Information Science and Tech

View full text Add to dashboard Cite

show abstract

“…Recently, there is a growing trend to model a recommendation system using these devices. Chowdhury et al (2018) [20] proposed AdaBoost-based classifier to predict suitable exercise types in real-world contexts based on a limited data for training. First, the system extracts some features from the data such as the distance and heart rate.…”

Section: B Sensor Data Miningmentioning

confidence: 99%

Building a Personalized Fitness Recommendation Application based on Sequential Information

Abdulaziz¹,

Al-motairy²,

Alghamdi³

et al. 2021

IJACSA

View full text Add to dashboard Cite

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.

show abstract

Fitness EDA using Advanced PySpark

Kiran,

Pushpa,

Shilpa

et al. 2024

2024 4th International Conference on Data Engineering and Communication Systems (ICDECS)

View full text Add to dashboard Cite

Automatic classification of physical exercises from wearable sensors using small dataset from non-laboratory settings

Cited by 5 publications

References 12 publications

Preliminary Simulation for Obtaining Accurate Heart Rate Data Using Imputation Methods

Preliminary Simulation for Obtaining Accurate Heart Rate Data Using Imputation Methods

Building a Personalized Fitness Recommendation Application based on Sequential Information

Fitness EDA using Advanced PySpark

Contact Info

Product

Resources

About