Volume 11 - Volume 11
Comparative Assessment of Forest Optimization with Deep Ensembling Technique for Human Activity Recognition based on Data Collected from Wearable Devices
Abstract
Aim: Improving the performance of Human Activity Recognition based on information sensed by
wearable devices. Materials and methods: In this study we have considered two groups namely forest
optimization with sample size of 1408 and deep ensemble techniques with sample size 1408 (Kane,
Phar, and BCPS n.d.). Accuracy was computed for the dataset size of 9673 to recognise six various
human activities (walking, jogging, standing, upstairs, sitting, downstairs). Result: It was observed
that the forest optimization algorithm obtains accuracy as 96% and loss as 14%. Forest optimization
technique appears to have better significance than deep ensemble technique with value of p=0.000.
Conclusion: The result proves that forest optimization approaches with varying seed value have
significant improvement in human activity recognition.
Paper Details
PaperID: 2181
Author's Name: G. Pallavi and Dr.A. Rama
Volume: Volume 11
Issues: Volume 11
Keywords: Machine Learning, Activity Recognition, Time Axis of Signals, Magnitude, Automated Human Activity Recognition System, Entrepreneurship.
Year: 2021
Month: June
Pages: 1215-1227