There is a consensus that serious games have a significant potential as a tool for instruction. However, their effectiveness in terms of learning outcomes is still understudied mainly due to the complexity involved in assessing intangible measures. A systematic approach—based on established principles and guidelines—is necessary to enhance the design of serious games, and many studies lack a rigorous assessment. An important aspect in the evaluation of serious games, like other educational tools, is user performance assessment. This is an important area of exploration because serious games are intended to evaluate the learning progress as well as the outcomes. This also emphasizes the importance of providing appropriate feedback to the player. Moreover, performance assessment enables adaptivity and personalization to meet individual needs in various aspects, such as learning styles, information provision rates, feedback, and so forth. This paper first reviews related literature regarding the educational effectiveness of serious games. It then discusses how to assess the learning impact of serious games and methods for competence and skill assessment. Finally, it suggests two major directions for future research: characterization of the player’s activity and better integration of assessment in games.

This article presents a new cable-driven underactuated robotic gripper, called TWISTER Hand. It is designed for adaptable grasping of objects in different shapes, weights, sizes, and textures. Each finger of the gripper is made of a compliant and continuum mechanism inspired by an origami design. This design is converted into a computer-aided design (CAD) model and 3-D printed using flexible and rigid polymer composite materials. Two CAD modeling methods for this design are compared in terms of structural stiffness and durability in the printed outcomes. For each design, two soft materials are used for preliminary evaluation of the material effect in these properties. The best combination of the model and material is selected to fabricate the three fingers of the robotic gripper. Each finger has a single cable routed along the structure. All three cables are tied and actuated simultaneously using a single servo motor to generate closing and opening motions in the gripper. TWISTER Hand’s adaptable grasping capability is tested using 36 different objects. The robot’s grasping performance under object pose uncertainties is also experimentally tested and analyzed. This compact fully integrated gripper can be attached to a robotic arm for various manipulative tasks.

SUMMARY: This paper describes a new robotic manipulator with three fingers based on an origami twisted tower design. The design specifications, kinematic description, and results from the stiffness and durability tests for the selected origami design are presented. The robotic arm is made of a 10-layer twisted tower, actuated by four cables with pulleys driven by servo motors. Each finger is made of a smaller 11-layer tower and uses a single cable directly attached to a servo motor. The current hardware setup supports vision-based autonomous control and internet-based remote control in real time. For preliminary evaluation of the robot's object manipulation capabilities, arbitrary objects with varying weights, sizes, and shapes (i.e., a shuttlecock, an egg shell, a paper cub, and a cubic block) were selected and the rate of successful grasping and lifting for each object was measured. In addition, an experiment comparing a rigid gripper and the new origami-based manipulator revealed that the origami structure in the fingers absorbs the excessive force applied to the object through force distribution and structural deformation, demonstrating its potential applications for effective manipulation of fragile objects.

Adaptability is a fundamental yet challenging requirement for mobile robot locomotion. This article presents $\alpha$ -WaLTR, a new adaptive wheel-and-leg transformable robot for versatile multiterrain mobility. The robot has four passively transformable wheels, where each wheel consists of a central gear and multiple leg segments with embedded spring suspension for vibration reduction. These wheels enable the robot to traverse various terrains, obstacles, and stairs while retaining the simplicity in primary control and operation principles of conventional wheeled robots. The chassis dimensions and the center of gravity location were determined by a multiobjective design optimization process aimed at minimizing the weight and maximizing the robot's pitch angle for obstacle climbing. Unity-based simulations guided the selection of the design variables associated with the transformable wheels. Following the design process, $\alpha$ -WaLTR with an embedded sensing and control system was developed. Experiments showed that the spring suspension on the wheels effectively reduced the vibrations when operated in the legged mode and verified that the robot's versatile locomotion capabilities were highly consistent with the simulations. The system-level integration with an embedded control system was demonstrated via autonomous stair detection, navigation, and climbing capabilities.

This letter presents a novel robotic mechanism inspired by origami and its fabrication method using three-dimensional (3D) printing. The selected twisted tower design shows unique structural and geometrical properties that enable bending and linear motions, and therefore, can be suitable for various robotic applications. However, fabrication of this origami design requires a complex sequence of paper folding and assembling by hand, which cannot be automated by existing manufacturing technologies. To fabricate this structure via 3D printing, the twisted tower is reconstructed in a computer aided design (CAD) model. The CAD model is then 3D printed using two different materials-a flexible material for creases and a rigid material for surfaces. The printed structure preserves the geometrical properties of the original tower. Associated kinematics and workspace analysis are also presented in this letter. The 3D printed tower is embodied into a robotic arm to physically demonstrate its potential utility.

This paper presents a facial emotion recognition technique using two newly defined geometric features, landmark curvature and vectorized landmark. These features are extracted from facial landmarks associated with individual components of facial muscle movements. The presented method combines support vector machine (SVM) based classification with a genetic algorithm (GA) for a multi-attribute optimization problem of feature and parameter selection. Experimental evaluations were conducted on the extended Cohn-Kanade dataset (CK+) and the Multimedia Understanding Group (MUG) dataset. For 8-class CK+, 7-class CK+, and 7-class MUG, the validation accuracy was 93.57, 95.58, and 96.29%; and the test accuracy resulted in 95.85, 97.59, and 96.56%, respectively. Overall precision, recall, and F1-score were about 0.97, 0.95, and 0.96. For further evaluation, the presented technique was compared with a convolutional neural network (CNN), one of the widely adopted methods for facial emotion recognition. The presented method showed slightly higher test accuracy than CNN for 8-class CK+ (% (SVM) vs. 95.43% (CNN)) and 7-class CK+ (97.59 vs. 97.34), while the CNN slightly outperformed on the 7-class MUG dataset (96.56 vs. 99.62). Compared to CNN-based approaches, this method employs less complicated models and thus shows potential for real-time machine vision applications in automated systems.