首页|COMSATS University Islamabad Researchers Describe Recent Advances in Machine Learning (Active Machine Learning for Heterogeneity Activity Recognition Through Smartwatch Sensors)
COMSATS University Islamabad Researchers Describe Recent Advances in Machine Learning (Active Machine Learning for Heterogeneity Activity Recognition Through Smartwatch Sensors)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
IEEE
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intelligence have been published. According to news reporting originating from Islamabad, Pakistan, by NewsRx correspondents, research stated, “Smartwatches with cutting-edge sensors are becoming commonplace in our daily lives.” Funders for this research include Deanship of Scientific Research At Jouf University; Eu Nextgenera- tioneu Through The Recovery And Resilience Plan For Slovakia. The news journalists obtained a quote from the research from COMSATS University Islamabad: “De- spite their widespread use, it can be challenging to interpret accelerometer and gyroscope data efficiently for Human Activity Recognition (HAR). This study explores active learning integrated with machine learn- ing, intending to maximize the use of smartwatch technology across a range of applications. The previous research on the HAR lacks promising performance, which could make it difficult to make highly accurate recognition. This paper proposes a novel approach to predict human activity from the Heterogeneity Hu- man Activity Recognition (HHAR) dataset that integrates active learning with machine learning models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting (GB) and Light Gradient Boosting Machine (LGBM) classifier to predict hetero- geneous activities accurately. We evaluated our approach to these models on the HHAR dataset that was generated using an accelerometer and gyroscope of smartwatches. The experiments are evaluated on 3 iterations where the results demonstrated that the proposed approach predicts human activities with the highest F1-Score of 99.99%.”
COMSATS University IslamabadIslamabadPakistanAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
IEEE
