Advances in Artificial Intelligence and Machine Learning; Research 1 (1) 49-67 Received 03-06-21; Accepted 06-06-21; Published 12-06-21
Exoskeleton-Based Multimodal Action and Movement Recognition:
Identifying and Developing the Optimal
Boosted Learning Approach
Nirmalya Thakur
THAKURNA@MAIL.UC.EDU
Department of Electrical Engineering and Computer Science,
University of Cincinnati, Cincinnati, OH 45221-0030, USA.
Chia Y. Han
HAN@UCMAIL.UC.EDU
Department of Electrical Engineering and Computer Science,
University of Cincinnati, Cincinnati, OH 45221-0030, USA.
Corresponding Author: Nirmalya Thakur.
Copyright © 2021 Nirmalya Thakur and Chia Y. Han. This is an open access article distributed under the Creative
Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided
the original work is properly cited.
Abstract
This paper makes two scientific contributions to the field of exoskeleton-based action and
movement recognition. First, it presents a novel machine learning and pattern recognition-
based framework that can detect a wide range of actions and movements - walking, walking
upstairs, walking downstairs, sitting, standing, lying, stand to sit, sit to stand, sit to lie, lie
to sit, stand to lie, and lie to stand, with an overall accuracy of 82.63%. Second, it presents
a comprehensive comparative study of different learning approaches - Random Forest,
Artificial Neural Network, Decision Tree, Multiway Decision Tree, Support Vector
Machine, k-NN, Gradient Boosted Trees, Decision Stump, AutoMLP, Linear Regression,
Vector Linear Regression, Random Tree, Naïve Bayes, Naïve Bayes (Kernel), Linear
Discriminant Analysis, Quadratic Discriminant Analysis, and Deep Learning applied to this
framework. The performance of each of these learning approaches was boosted by using the
AdaBoost algorithm, and the Cross Validation approach was used for training and testing.
The results show that in boosted form, the k-NN classifier outperforms all the other boosted
learning approaches and is, therefore, the optimal learning method for this purpose. The
results presented and discussed uphold the importance of this work to contribute towards
augmenting the abilities of exoskeleton-based assisted and independent living of the elderly
in the future of Internet of Things-based living environments, such as Smart Homes. As a
specific use case, we also discuss how the findings of our work are relevant for augmenting
the capabilities of the Hybrid Assistive Limb exoskeleton, a highly functional lower limb
exoskeleton.
Keywords: Exoskeleton, Machine Learning, Pattern Recognition, Human-Computer Inter-
action, Internet of Things, Smart Home, Elderly Population.
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Citation: Nirmalya Thakur and Chia Y. Han. Exoskeleton-Based Multimodal Action and Movement Recognition: Identifying and
Developing the Optimal Boosted Learning Approach. Advances in Artificial Intelligence and Machine Learning. 2021;1(1):4.
