Head-AR: Human Activity Recognition with Head-Mounted IMU Using Weighted Ensemble Learning

2020 ◽  
pp. 153-167
Author(s):  
Hristijan Gjoreski ◽  
Ivana Kiprijanovska ◽  
Simon Stankoski ◽  
Stefan Kalabakov ◽  
John Broulidakis ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition

scholarly journals Multi-label classification based ensemble learning for human activity recognition in smart home

2020 ◽  
Vol 12 ◽  
pp. 100324
Author(s):  
Manan Jethanandani ◽  
Abhishek Sharma ◽  
Thinagaran Perumal ◽  
Jieh-Ren Chang
Keyword(s):  
Ensemble Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Smart Home ◽  
Human Activity Recognition

scholarly journals Efficient Human Activity Recognition Solving the Confusing Activities Via Deep Ensemble Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 75490-75499 ◽  
Author(s):  
Ran Zhu ◽  
Zhuoling Xiao ◽  
Ying Li ◽  
Mingkun Yang ◽  
Yawen Tan ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition

scholarly journals Selective Ensemble Based on Extreme Learning Machine for Sensor-Based Human Activity Recognition

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3468 ◽  
Author(s):  
Tian ◽  
Zhang ◽  
Chen ◽  
Geng ◽  
Wang
Keyword(s):  
Ensemble Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
The Body ◽  
Swarm Optimization ◽  
Diversity Measure ◽  
Base Classifier ◽  
Glowworm Swarm Optimization ◽  
Selective Ensemble

Sensor-based human activity recognition (HAR) has attracted interest both in academic and applied fields, and can be utilized in health-related areas, fitness, sports training, etc. With a view to improving the performance of sensor-based HAR and optimizing the generalizability and diversity of the base classifier of the ensemble system, a novel HAR approach (pairwise diversity measure and glowworm swarm optimization-based selective ensemble learning, DMGSOSEN) that utilizes ensemble learning with differentiated extreme learning machines (ELMs) is proposed in this paper. Firstly, the bootstrap sampling method is utilized to independently train multiple base ELMs which make up the initial base classifier pool. Secondly, the initial pool is pre-pruned by calculating the pairwise diversity measure of each base ELM, which can eliminate similar base ELMs and enhance the performance of HAR system by balancing diversity and accuracy. Then, glowworm swarm optimization (GSO) is utilized to search for the optimal sub-ensemble from the base ELMs after pre-pruning. Finally, majority voting is utilized to combine the results of the selected base ELMs. For the evaluation of our proposed method, we collected a dataset from different locations on the body, including chest, waist, left wrist, left ankle and right arm. The experimental results show that, compared with traditional ensemble algorithms such as Bagging, Adaboost, and other state-of-the-art pruning algorithms, the proposed approach is able to achieve better performance (96.7% accuracy and F1 from wrist) with fewer base classifiers.

scholarly journals A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2307 ◽  
Author(s):  
Shoujiang Xu ◽  
Qingfeng Tang ◽  
Linpeng Jin ◽  
Zhigeng Pan
Keyword(s):  
Ensemble Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Classification Accuracy ◽  
Human Activity Recognition ◽  
Gradient Boosting ◽  
Layer By Layer ◽  
Proposed Model ◽  
Public Datasets ◽  
Gyroscope Sensor

Human activity recognition (HAR) has gained lots of attention in recent years due to its high demand in different domains. In this paper, a novel HAR system based on a cascade ensemble learning (CELearning) model is proposed. Each layer of the proposed model is comprised of Extremely Gradient Boosting Trees (XGBoost), Random Forest, Extremely Randomized Trees (ExtraTrees) and Softmax Regression, and the model goes deeper layer by layer. The initial input vectors sampled from smartphone accelerometer and gyroscope sensor are trained separately by four different classifiers in the first layer, and the probability vectors representing different classes to which each sample belongs are obtained. Both the initial input data and the probability vectors are concatenated together and considered as input to the next layer’s classifiers, and eventually the final prediction is obtained according to the classifiers of the last layer. This system achieved satisfying classification accuracy on two public datasets of HAR based on smartphone accelerometer and gyroscope sensor. The experimental results show that the proposed approach has gained better classification accuracy for HAR compared to existing state-of-the-art methods, and the training process of the model is simple and efficient.

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