Assessing Performance of Aerobic Routines using Background Subtraction and Intersected Image Region

It is recommended for a novice person to engage trained personnel before starting an unfamiliar aerobic or weight routine to gain real-time expert feedbacks. This greatly reduces the risk of injury and maximise physical gains. We present a simple image similarity measure based on intersected image region to assess a subject's performance of an aerobic routine. The method was implemented inside an Augmented Reality (AR) desktop app that employed a single RGB camera to capture still images of the subject as he or she progressed through the routine. The background-subtracted body pose image was compared against the exemplar image (i.e., AR template) at specific intervals. Based on a limited dataset, our pose matching function managed an accuracy of 93.67

Customer’s spontaneous facial expression recognition

In the field of consumer science, customer facial expression is often categorized either as negative or positive. Customer who portrays negative emotion to a specific product mostly means they reject the product while a customer with positive emotion is more likely to purchase the product. To observe customer emotion, many researchers have studied different perspectives and methodologies to obtain high accuracy results. Conventional neural network (CNN) is used to recognize customer spontaneous facial expressions. This paper aims to recognize customer spontaneous expressions while the customer observed certain products. We have developed a customer service system using a CNN that is trained to detect three types of facial expression, i.e. happy, sad, and neutral. Facial features are extracted together with its histogram of gradient and sliding window. The results are then compared with the existing works and it shows an achievement of 82.9% success rate on average

Personalized Instant Messaging UI Design for Elderly

There is a paucity of studies on the consumption of digital content by elderly people utilising smart devices as well as strategies to get elderly people acquainted with smart gadgets. Usability and familiarity with smart devices for senior persons to utilise and get the most out of smart devices and digital content must be prioritised. A cognitive reaction-based intelligent UI suited for senior persons is proposed in this paper, which is based on the user's cognitive performance and demographics. A cognitive response feedback and demography dataset was built by interviewing a group of elderly in Malaysia. The context of the interview is associated with the unique cognitive keywords that may be anticipated by contextual semantic search. In this paper, two classifiers are used, Support Vector Machine (SVM), and Naïve Bayes (NB), and they are compared in terms of classification performances. The classifiers are validated using k-fold cross-validation (10-fold) using unigram TF-IDF and bigram TF-IDF, and the results were presented using accuracy, precision, recall, and F1 scores. Thus, user interface (UI) pre-sets lists will be matched to the user model based on the dataset classification

3D Facial Action Units Recognition for Emotional Expression

units (AUs) when a facial expression is shown by a human face. This paper presents the methods to recognize AU using a distance feature between facial points which activates the muscles. The seven AU involved are AU1, AU4, AU6, AU12, AU15, AU17 and AU25 that characterizes a happy and sad expression. The recognition is performed on each AU according to the rules defined based on the distance of each facial point. The facial distances chosen are computed from twelve salient facial points. Then the facial distances are trained using Support Vector Machine (SVM) and Neural Network (NN). Classification result using SVM is presented with several different SVM kernels while result using NN is presented for each training, validation and testing phase. By using any SVM kernels, it is consistent that AUs that are corresponded to sad expression has a high recognition compared to happy expression. The highest average kernel performance across AUs is 93%, scored by quadratic kernel. Best results for NN across AUs is for AU25 (Lips parted) with lowest CE (0.38%) and 0% incorrect classification

Wide Survey on Online Teaching and Learning During Movement Control Order in Malaysia due to Covid-19 Pandemic

“Prepare for the new norm” is a common saying among Malaysians since the federal government introduced the Movement Control Order on March 18th, 2020. Among the many enforced measures to control the spread of the Covid-19 virus are the stay-at-home ruling and the ban on mass gatherings. These measures force Higher Education providers (HEPs) to drastically change the way Teaching and Learning (T&L) activities are conducted at their institutions. Conventional guided face-to-face (f2f) T&L and assessments are no longer tenable; the only workable solution is converting the remaining course plan to full online mode. Universiti Malaysia Sarawak’s (UNIMAS) students and academics are relatively familiar with the concept of blended learning. UNIMAS had started using Learning Management System two decades ago and the current system, e-Learning Enrichment and Advancement Platform or eLEAP, is actively used by all UNIMAS students. However, most of the courses were designed with blended learning relegated to supporting act status; existing only to complement the guided f2f T&L activities. The Movement Control Order (MCO) requires blended learning to be delivered in substitution mode, which is to replace the f2f sessions instead of merely complementing them. To assess the status of UNIMAS’s academics and students for this scenario, an online survey was conducted from March 22nd till March 31st. A total of 640 academics and 6,871 students had participated in the survey. This paper reports on the survey findings that provide insights on how to mitigate the infrastructure and policy shortcomings as to afford effective blended learning (in substitution mode) delivery in UNIMAS as well as to minimise inequitable education for students from diverse online learning readiness

Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)

Passive biometric identification enables wildlife monitoring with minimal disturbance. Using a motion-activated camera placed at an elevated position and facing downwards, we collected images of sea turtle carapace, each belonging to one of sixteen Chelonia mydas juveniles. We then learned co-variant and robust image descriptors from these images, enabling indexing and retrieval. In this work, we presented several classification results of sea turtle carapaces using the learned image descriptors. We found that a template-based descriptor, i.e., Histogram of Oriented Gradients (HOG) performed exceedingly better during classification than keypoint-based descriptors. For our dataset, a high-dimensional descriptor is a must due to the minimal gradient and color information inside the carapace images. Using HOG, we obtained an average classification accuracy of 65%

Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)

Passive biometric identification enables wildlife monitoring with minimal disturbance. Using a motion-activated camera placed at an elevated position and facing downwards, we collected images of sea turtle carapace, each belonging to one of sixteen Chelonia mydas juveniles. We then learned co-variant and robust image descriptors from these images, enabling indexing and retrieval. In this work, we presented several classification results of sea turtle carapaces using the learned image descriptors. We found that a template-based descriptor, i.e., Histogram of Oriented Gradients (HOG) performed exceedingly better during classification than keypoint-based descriptors. For our dataset, a high-dimensional descriptor is a must due to the minimal gradient and color information inside the carapace images. Using HOG, we obtained an average classification accuracy of 65%

Real-Time Driver’s Monitoring Mobile Application through Head Pose, Drowsiness and Angry Detection

The current driver's monitoring system requires a set-up that includes the usage of a variety of camera equipment behind the steering wheel. It is highly impractical in a real-world environment as the set-up might cause annoyance or inconvenience to the driver. This project proposes a framework of using mobile devices and cloud services to monitor the driver's head pose, detect angry expression and drowsiness, and alerting them with audio feedback. With the help of a phone camera functionality, the driver’s facial expression data can be collected then further analyzed via image processing under the Microsoft Azure platform. A working mobile app is developed, and it can detect the head pose, angry emotion, and drowsy drivers by monitoring their facial expressions. Whenever an angry or drowsy face is detected, pop-up alert messages and audio feedback will be given to the driver. The benefit of this mobile app is it can remind drivers to drive calmly and safely until drivers manage to handle their emotions where anger or drowsy is no longer detected. The performance of the mobile app in classifying anger emotion is achieved at 96.66% while the performance to detect driver’s drowsiness is 82.2%. On average, the head pose detection success rate across the six scenarios presented is 85.67%

Language Modelling for a Low-Resource Language in Sarawak, Malaysia

This paper explores state-of-the-art techniques for creating language models in low-resource setting. It is known that building a good statistical language model requires a large amount of data. Therefore, models that are trained on low-resource language suffer from poor performances. We conducted a study on current language modelling techniques such as n-gram and recurrent neural network (RNN) to observe their outcomes on data from a language in Sarawak, Malaysia. The target language is Iban, a widely spoken language in this region. We have collected news data form an online source to build an Iban text corpus. After normalising the data, we trained trigram and RNN language models and tested on automatic speech recognition data. Based on our results, we observed that the RNN language models did not significantly outperform the trigram language models. A slight improvement on RNN model is seen after the size of the training data was increased. We have also experimented on merging n-gram and RNN language models and we obtained 32.33% improvement after using a trigram-RNN language model