TY  - JOUR
T1  - Action Classification on the Berkeley Multimodal Human Action Dataset (MHAD)
AU - Ng, Hu AU - Yap, Timothy Tzen Vun AU - Tong, Hau Lee AU - Ho, Chiung Ching AU - Tan, Lay Kun AU - Eng, Wan Xin AU - Yap, Seng Kuan AU - Soh, Jia Hao 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 3
SP  - 520
EP  - 526
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.520.526
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.520.526
KW  - Human action classification
KW  -multimodal
KW  -feature extraction
KW  -feature selection
KW  -machine learning
AB  - The objective of this study is to classify multimodal human actions of the Berkeley Multimodal
Human Action Database (MHAD). Actions from accelerometer and motion capture modals are utilized in this
study. Features extracted include statistical measures such as minimum, maximum, mean, median, standard
deviation, kurtosis and skewness. Feature extraction level fusion is applied to form a feature vector comprising
two modalities. Feature selection is implemented using Particle Swarm Optimization (PSO) Tabu and Ranker.
Classification is performed with Support Vector Machine (SVM) Random Forest (RF) k-Nearest
Neighbour (k-NN) and Best First Tree (BFT). The classification model that gave the highest accuracy is support
vector machine with radial basis function kernel with a Correct Classification Rate (CCR) of 97.6 % for the
Accelerometer modal (Acc) 99.8% for the Motion capture system modal (Mocap) and 99.8% for the Fusion
Modal (FusioMA). In the feature selection process, ranker selected every single extracted feature (162 features
for Acc and 1161 features for Mocap and 1323 features for FusioMA) and produced an average CCR of 97.4%.
Comparing with PSO (68 features for Acc, 350 features for Mocap and 412 features for FusioMA) it produced
an average CCR of 97.1% and Tabu (54 features for Acc, 199 features for Mocap and 323 features for
FusionMA) produced an average CCR of 97.2%. Although, Ranker gave the best result, the difference in the
average CCR is not significant. Thus, PSO and Tabu may be more suitable in this case as the reduced feature
set can result in computational speedup and reduced complexity. The extracted statistical features are able to
produce high accuracy in classification of multimodal human actions. The feature extraction level fusion to
combine the two modalities performs better than single modality in the classification.
ER  -