Timothy Tzen Vun Yap,

Objectives: The objective of this paper is to classify multimodal human actions of the Berkeley Multimodal Human Action Database (MHAD). Methods/Statistical analysis: 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). Findings: 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. Application/Improvements: 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.

This work aims to develop an information retrieval application based on augmented reality (AR) technologies to enhance visitors' experience in a museum exhibition. The purpose of developing this application is to give visitors of museums a customized interactive experience through a handheld smartphone. The application recognizes objects of interest and retrieve information of such objects for display through feeds from a smartphone's camera in real time and overlays the information over the object. This is achieved with vision-based AR, utilizing 3D object tracking, thus eliminating the use of markers, which could prove unreliable due to obfuscation or damage.

This study reports the classification of subdural and extradural hematomas in brain CT images. The major difference between subdural and extradural hematomas lies in their shapes, therefore eight shape descriptors are proposed to describe the characteristics of the two types of hematoma. The images will first undergo the pre-processing step which consists of two-level contrast enhancement separated by parenchyma extraction processes. Next, k-means clustering is performed to garner all Regions of Interest (ROIs) into one cluster. Prior to classification, shape features are extracted from each ROI. Finally for classification, fuzzy k-Nearest Neighbor (fuzzy k-NN) and Linear Discriminant Analysis (LDA) are employed to classify the regions into subdural hematoma, extradural hematoma or normal regions. Experimental results suggest that fuzzy k-NN produces the optimum accuracy. It manages to achieve over 93% correct classification rate on a set of 109 subdural and 247 extradural hematoma regions, as well as 629 normal regions.

This paper describes the baseline corpus of a new multimodal biometric database, the MMU GASPFA (Gait-Speech-Face) database. The corpus in GASPFA is acquired using commercial off the shelf (COTS) equipment including digital video cameras, digital voice recorder, digital camera, Kinect camera and accelerometer equipped smart phones. The corpus consists of frontal face images from the digital camera, speech utterances recorded using the digital voice recorder, gait videos with their associated data recorded using both the digital video cameras and Kinect camera simultaneously as well as accelerometer readings from the smart phones. A total of 82 participants had their biometric data recorded. MMU GASPFA is able to support both multimodal biometric authentication as well as gait action recognition. This paper describes the acquisition setup and protocols used in MMU GASPFA, as well as the content of the corpus. Baseline results from a subset of the participants are presented for validation purposes.

This work aims to develop an information retrieval application based on augmented reality (AR) technologies to enhance visitors' experience in a museum exhibition. The purpose of developing this application is to give visitors of museums a customized interactive experience through a handheld smartphone. The application recognizes objects of interest and retrieve information of such objects for display through feeds from a smartphone's camera in real time and overlays the information over the object. This is achieved with vision-based AR, utilizing 3D object tracking, thus eliminating the use of markers, which could prove unreliable due to obfuscation or damage.

The objective of this paper is to analyse the gait of subjects with suffering Parkinson's Disease (PD), plus to differentiate their gait from those of normal people. The data isobtained from a medical gait database known as Gaitpdb [1]. In the data set, there are
73 control subjects and 93 subjects with PD. In our study, we first obtained the gait features
using statistical analysis, which include minimum, maximum, median, kurtosis, mean,
skewness, standard deviation and average absolute deviation of the gait signal. Next, selection of the extracted features is performed using PSO search, Tabu search and Ranker. Finally the selected features will undergo classification using BFT, BPANN, k-NN, SVM with Ln kernel, SVM with Poly kernel and SVM with Rbf kernel. From the experimental results,
the proposed model achieved average of 66.43%, 89.97%, 87.00%, 88.47%, 86.80% and87.53% correct classification rates respectively.

The objective of this paper is to analyse the gait of subjects with suffering Parkinson's Disease (PD), plus to differentiate their gait from those of normal people. The data is obtained from a medical gait database known as Gaitpdb [1]. In the data set, there are 73 control subjects and 93 subjects with PD. In our study, we first obtained the gait features using statistical
analysis, which include minimum, maximum, median, kurtosis, mean, skewness, standard deviation and average absolute deviation of the gait signal. Next, selection of the extracted features is performed using PSO search, Tabu search and Ranker. Finally the selected features will undergo
classification using BFT, BPANN, k-NN, SVM with Ln kernel, SVM with Polykernel and SVM with Rbf kernel. From the experimental results, the proposed model achieved average of 66.43%, 89.97%, 87.00%, 88.47%, 86.80% and 87.53% correct classification rates respectively.