Chen‐Hua Yeow

Zherui Li, Raye Chen-Hua Yeow

Real-time intelligent detection and prediction of subjects' behavior particularly their movements or actions is critical in the ward. This approach offers the advantage of reducing in-hospital care costs and improving the efficiency of healthcare workers, which is especially true for scenarios at night or during peak admission periods. Therefore, in this work, we propose using computer vision (CV) and deep learning (DL) methods for detecting subjects and recognizing their actions. We utilize OpenPose as an accurate subject detector for recognizing the positions of human subjects in the video stream. Additionally, we employ AlphAction's Asynchronous Interaction Aggregation (AIA) network to predict the actions of detected subjects. This integrated model, referred to as PoseAction, is proposed. At the same time, the proposed model is further trained to predict 12 common actions in ward areas, such as staggering, chest pain, and falling down, using medical-related video clips from the NTU RGB+D and NTU RGB+D 120 datasets. The results demonstrate that PoseAction achieves the highest classification mAP of 98.72% (IoU@0.5). Additionally, this study develops an online real-time mode for action recognition, which strongly supports the clinical translation of PoseAction. Furthermore, using OpenPose's function for recognizing face key points, we also implement face blurring, which is a practical solution to address the privacy protection concerns of patients and healthcare workers. Nevertheless, the training data for PoseAction is currently limited, particularly in terms of label diversity. Consequently, the subsequent step involves utilizing a more diverse dataset (including general actions) to train the model's parameters for improved generalization.

Wee Kiat Chan, PengWei Wang, Raye Chen-Hua Yeow

Soft robotics has emerged as a promising field with the potential to revolutionize industries such as healthcare and manufacturing. However, designing effective soft robots presents challenges, particularly in managing the complex interplay of material properties, structural design, and control strategies. Traditional design methods are often time-consuming and may not yield optimal designs. In this paper, we explore the use of generative AI to create 3D models of soft actuators. We create a dataset of over 70 text-shape pairings of soft pneumatic robot actuator designs, and adapt a latent diffusion model (SDFusion) to learn the data distribution and generate novel designs from it. By employing transfer learning and data augmentation techniques, we significantly improve the performance of the diffusion model. These findings highlight the potential of generative AI in designing complex soft robotic systems, paving the way for future advancements in the field.

Huixu Dong, Chao-Yu Chen, Chen Qiu et al.

Grasping is an essential capability for most robots in practical applications. Soft robotic grippers are considered as a critical part of robotic grasping and have attracted considerable attention in terms of the advantages of the high compliance and robustness to variance in object geometry; however, they are still limited by the corresponding sensing capabilities and actuation mechanisms. We propose a novel soft gripper that looks like a granary with a compliant snap-through bistable mechanism fabricated by integrated mold technology, achieving sensing and actuation purely mechanically. In particular, the snap-through bistable structure in the proposed gripper allows us to reduce the complexity of the mechanism, control, sensing designs since the grasping and sensing behaviors are completely passive. The grasping behaviors are automatically motivated once the trigger position of the gripper touches an object and applies sufficient force. To grasp objects with various profiles, the proposed granary soft gripper (GSG) is designed to be capable of enveloping, pinching and caging grasps. The gripper consists of a chamber palm, a palm cap and three fingers. First, the design of the gripper is analyzed. Then, after the theoretical model is constructed, finite element (FE) simulations are conducted to verify the built model. Finally, a series of grasping experiments is carried out to assess the snap-through behavior of the proposed gripper on grasping and sensing. The experimental results illustrate that the proposed gripper can manipulate a variety of soft and rigid objects and remain stable even though it undertakes external disturbances.