Human Posture

Human posture research focuses on accurately and efficiently recognizing and controlling human and robotic postures using various sensing and modeling techniques. Current research employs machine learning models, including deep learning, support vector machines, and neural networks, often coupled with radio frequency sensing or Channel State Information (CSI) from Wi-Fi signals, to achieve posture classification. Challenges remain in improving the generalization of these models across different environments and addressing the complexities of musculoskeletal systems, particularly hysteresis in robotic joints. This research is significant for applications in human-robot interaction, assistive robotics, and activity recognition, offering potential for improved healthcare monitoring and more natural human-machine interfaces.