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- Exploring Human Action Recognition for Rehabilitation Game ApplicationPublication . Lopes, Júlio Castro; Van-Deste, Isaac; Lopes, Rui PedroThrough computer vision algorithms motion can be computed, which can be a crucial element to be integrated with serious game environments. To evaluate the efficacy of motion detection algorithms, the information of these algorithms can be used to perform Human Action Recognition (HAR). There are several algorithms to perform HAR, although skeleton approaches can be seen as the best way to isolate human motion. To extract the human skeleton representation, the work described in this paper evaluates three distinct methods: OpenPose (2D), YOLO-Pose (2D) and BlazePose (3D). The information translated by the skeleton representations is normalized by lightweight normalization algorithms (for further real-time application). To classify the video sequence and further action identification, a Long Short Term Memory network (LSTM), was used. Using the N-UCLA dataset, the highest F1 score of 0.745 was achieved using OpenPose skeleton extraction (2D), followed by the computation of the angles in each joint, demonstrating that the OpenPose skeleton representation can be the most viable solution for computing human motion in serious games.
- An architecture for capturing and synchronizing heart rate and body motion for stress inferencePublication . Lopes, Júlio Castro; Vieira, João; Van-Deste, Isaac; Lopes, Rui PedroThis paper aims to propose a system for capturing and synchronizing human heart rate (HR) and body motion (BMR) for stress inference. For this purpose, OpenPose skeletonbased method was used, which is capable of analyzing sequential videos, processing them frame by frame, and obtaining an approximation to the human figure composed of 18 key points, roughly corresponding to the joints. It is expected that by combining these two distinct measurements, HR and BMR, a more grounded evaluation of player stress levels while playing a Virtual Reality (VR) game, will be achieved. The experiment was conducted with 5 participants playing 5 different types of games, with different levels of intensity. During the game, the players wore a smartwatch to measure the HR and images were captured to calculate the BMR. Future work will assess this dataset to confirm the stress level in these 5 situations.
- Stress inference in a virtual reality game for rehabilitation with body motion and heart ratePublication . Lopes, Júlio Castro; Van-Deste, Isaac; Vieira, João; Lopes, Rui PedroThis paper proposes an architecture for detecting stress in a player, while playing a Virtual Reality (VR) game, by analyzing the player’s movements as well as the player’s Heart Rate (HR). For this effect, only a camera to analyze players’ Body Motion Rate (BMR) and a smartwatch to capture the HR, were used. As part of the computation of the BMR, computer vision techniques were used to detect the player’s skeleton, computing the difference between frames. A dataset was captured in this paper, while the players tested 5 different scenarios to induce different stress situations. The proposed dataset serves as a proof of concept to validate the relation between HR and BMR. Future work should investigate synthetic data generation techniques to improve dataset diversity and adaptability for Dynamic Difficulty Adjustment (DDA) systems. This research contributes to advancing stress detection in VR, with potential applications in rehabilitation, particularly for conditions such as schizophrenia, promoting improved well-being and stress management in the long run.