Real-Time Robotic Presentation Skill Scoring Using Multi-Model Analysis and Fuzzy Delphi-Analytic Hierarchy Process.

Existing methods for scoring student presentations predominantly rely on computer-based implementations and do not incorporate a robotic multi-classification model. This limitation can result in potential misclassification issues as these approaches lack active feature learning capabilities due to fixed camera positions. Moreover, these scoring methods often solely focus on facial expressions and neglect other crucial factors, such as eye contact, hand gestures and body movements, thereby leading to potential biases or inaccuracies in scoring. To address these limitations, this study introduces Robotics-based Presentation Skill Scoring (RPSS), which employs a multi-model analysis. RPSS captures and analyses four key presentation parameters in real time, namely facial expressions, eye contact, hand gestures and body movements, and applies the fuzzy Delphi method for criteria selection and the analytic hierarchy process for weighting, thereby enabling decision makers or managers to assign varying weights to each criterion based on its relative importance. RPSS identifies five academic facial expressions and evaluates eye contact to achieve a comprehensive assessment and enhance its scoring accuracy. Specific sub-models are employed for each presentation parameter, namely EfficientNet for facial emotions, DeepEC for eye contact and an integrated Kalman and heuristic approach for hand and body movements. The scores are determined based on predefined rules. RPSS is implemented on a robot, and the results highlight its practical applicability. Each sub-model is rigorously evaluated offline and compared against benchmarks for selection. Real-world evaluations are also conducted by incorporating a novel active learning approach to improve performance by leveraging the robot's mobility. In a comparative evaluation with human tutors, RPSS achieves a remarkable average agreement of 99%, showcasing its effectiveness in assessing students' presentation skills.

Semantic visual simultaneous localization and mapping (SLAM) using deep learning for dynamic scenes.

Simultaneous localization and mapping (SLAM) is a fundamental problem in robotics and computer vision. It involves the task of a robot or an autonomous system navigating an unknown environment, simultaneously creating a map of the surroundings, and accurately estimating its position within that map. While significant progress has been made in SLAM over the years, challenges still need to be addressed. One prominent issue is robustness and accuracy in dynamic environments, which can cause uncertainties and errors in the estimation process. Traditional methods using temporal information to differentiate static and dynamic objects have limitations in accuracy and applicability. Nowadays, many research trends have leaned towards utilizing deep learning-based methods which leverage the capabilities to handle dynamic objects, semantic segmentation, and motion estimation, aiming to improve accuracy and adaptability in complex scenes. This article proposed an approach to enhance monocular visual odometry's robustness and precision in dynamic environments. An enhanced algorithm using the semantic segmentation algorithm DeeplabV3+ is used to identify dynamic objects in the image and then apply the motion consistency check to remove feature points belonging to dynamic objects. The remaining static feature points are then used for feature matching and pose estimation based on ORB-SLAM2 using the Technical University of Munich (TUM) dataset. Experimental results show that our method outperforms traditional visual odometry methods in accuracy and robustness, especially in dynamic environments. By eliminating the influence of moving objects, our method improves the accuracy and robustness of visual odometry in dynamic environments. Compared to the traditional ORB-SLAM2, the results show that the system significantly reduces the absolute trajectory error and the relative pose error in dynamic scenes. Our approach has significantly improved the accuracy and robustness of the SLAM system's pose estimation.

Enhancing data transmission in duct air quality monitoring using mesh network strategy for LoRa.

Duct air quality monitoring (DAQM) is a typical process for building controls, with multiple infections outbreaks reported over time linked with duct system defilement. Various research works have been published with analyses on the air quality inside ducting systems using microcontrollers and low-cost smart sensors instead of conventional meters. However, researchers face problems sending data within limited range and cross-sections inside the duct to the gateway using available wireless technologies, as the transmission is entirely a non-line-of-sight. Therefore, this study developed a new instrument for DAQM to integrate microcontrollers and sensors with a mobile robot using LoRa as the wireless communication medium. The main contribution of this paper is the evaluation of mesh LoRa strategies using our instrument to overcome network disruption problems at the cross-sections and extend the coverage area within the duct environment. A mobile LoRa-based data collection technique is implemented for various data sensors such as DHT22, MQ7, MQ2, MQ135, and DSM50A to identify carbon monoxide, carbon dioxide, smoke, and PM2.5 levels. This study analyzed the efficiency of data transmission and signal strength to cover the air duct environment using several network topologies. The experimental design covered four different scenarios with different configurations in a multi-story building. The network performance evaluations focused on the packet delivery ratio (PDR) and the received signal strength indicator (RSSI). Experimental results in all scenarios showed an improvement in Packet Delivery Ratio (PDR) and significant improvement in the coverage area in the mesh network setup. The results conclude that the transmission efficiency and coverage area are significantly enhanced using the proposed LoRa mesh network and potentially expanded in larger duct environments.

Artificial intelligence maturity model: a systematic literature review.

Organizations in various industries have widely developed the artificial intelligence (AI) maturity model as a systematic approach. This study aims to review state-of-the-art studies related to AI maturity models systematically. It allows a deeper understanding of the methodological issues relevant to maturity models, especially in terms of the objectives, methods employed to develop and validate the models, and the scope and characteristics of maturity model development. Our analysis reveals that most works concentrate on developing maturity models with or without their empirical validation. It shows that the most significant proportion of models were designed for specific domains and purposes. Maturity model development typically uses a bottom-up design approach, and most of the models have a descriptive characteristic. Besides that, maturity grid and continuous representation with five levels are currently trending in maturity model development. Six out of 13 studies (46%) on AI maturity pertain to assess the technology aspect, even in specific domains. It confirms that organizations still require an improvement in their AI capability and in strengthening AI maturity. This review provides an essential contribution to the evolution of organizations using AI to explain the concepts, approaches, and elements of maturity models.

Malay Version of the mHealth App Usability Questionnaire (M-MAUQ): Translation, Adaptation, and Validation Study.

BACKGROUND: Mobile health (mHealth) apps play an important role in delivering education, providing advice on treatment, and monitoring patients' health. Good usability of mHealth apps is essential to achieve the objectives of mHealth apps efficiently. To date, there are questionnaires available to assess the general system usability but not explicitly tailored to precisely assess the usability of mHealth apps. Hence, the mHealth App Usability Questionnaire (MAUQ) was developed with 4 versions according to the type of app (interactive or standalone) and according to the target user (patient or provider). Standalone MAUQ for patients comprises 3 subscales, which are ease of use, interface and satisfaction, and usefulness. OBJECTIVE: This study aimed to translate and validate the English version of MAUQ (standalone for patients) into a Malay version of MAUQ (M-MAUQ) for mHealth app research and usage in future in Malaysia. METHODS: Forward and backward translation and harmonization of M-MAUQ were conducted by Malay native speakers who also spoke English as their second language. The process began with a forward translation by 2 independent translators followed by harmonization to produce an initial translated version of M-MAUQ. Next, the forward translation was continued by another 2 translators who had never seen the original MAUQ. Lastly, harmonization was conducted among the committee members to resolve any ambiguity and inconsistency in the words and sentences of the items derived with the prefinal adapted questionnaire. Subsequently, content and face validations were performed with 10 experts and 10 target users, respectively. Modified kappa statistic was used to determine the interrater agreement among the raters. The reliability of the M-MAUQ was assessed by 51 healthy young adult mobile phone users. Participants needed to install the MyFitnessPal app and use it for 2 days for familiarization before completing the designated task and answer the M-MAUQ. The MyFitnessPal app was selected because it is one among the most popular installed mHealth apps globally available for iPhone and Android users and represents a standalone mHealth app. RESULTS: The content validity index for the relevancy and clarity of M-MAUQ were determined to be 0.983 and 0.944, respectively, which indicated good relevancy and clarity. The face validity index for understandability was 0.961, which indicated that users understood the M-MAUQ. The kappa statistic for every item in M-MAUQ indicated excellent agreement between the raters (κ ranging from 0.76 to 1.09). The Cronbach α for 18 items was .946, which also indicated good reliability in assessing the usability of the mHealth app. CONCLUSIONS: The M-MAUQ fulfilled the validation criteria as it revealed good reliability and validity similar to the original version. M-MAUQ can be used to assess the usability of mHealth apps in Malay in the future.