Comparative Assessment of Forest Optimization with Deep Ensembling Technique for Human Activity Recognition based on Data Collected from Wearable Devices

Authors

  • G. Pallavi
  • Dr.A. Rama

DOI:

https://doi.org/10.47059/revistageintec.v11i4.2181

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.

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Published

2021-07-11

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Section

Articles