We are developing a training system for figure skating. The system is designed to provide coach instructions to users with no professional equipment or 3D camera. The system includes 2D video analysis, human skeleton tracking, pose detection, and instruction generation. This document focuses on temporal video alignment. Code is available here. System Structure Get the Embedding Space Model To compare and find the differences between the learner's video and the standard motion, we have to temporally align two videos and automatically get the timestamp where the motion starts. Because of the lack of labelled data, we implement a self-supervised representation learning method, which originated from Temporal Cycle-Consistency (TCC) Learning[^TCC]. The method aims to find the temporal correspondence between video pairs and align two similar videos based on the resulting per-frame embeddings. [^TCC]: Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman. Temporal Cycle-Consistency Learning. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Dataset Preparation

The project's primary objective is to combine computer vision with a robotic arm to execute precise pick-and-place operations. This task is carried out utilizing the Universal Robots' UR5e Robot Arm Manipulator, which is equipped with a stationary USB webcam and controlled by MATLAB program. Code is available here. Introduction The UR5e robotic arm in Figure 1 is utilized to perform the tasks.  Figure 1. the UR5e robotic arm The tasks involve the integration of computer vision technology with the UR5e robotic arm to enhance the precision and complexity of pick-and-place operations. Utilizing the top-down perspective from the webcam, this project entails several key objectives. Firstly, we aim to identify the game board's location relative to the robot's base joint. Secondly, we will identify and distinguish between the obstacle pieces and the player piece, using a color-coded system to facilitate the identification, all in reference to the robot's base joint. Subsequently, we will execute the relocation of the obstacle pieces from their current positions to their designated locations, guided by a provided occupancy grid. Finally, the Bug2 algorithm will be employed to navigate the player piece from its source location to the specified target location, thus achieving the desired pick-and-place operations.

Due to rapid aging, dealing with the shortage of healthcare resources and helping the elderly manage their daily life independently and build confidence gradually have been the focus of common concerns of technology and the medical industry. Unlike western countries, most elderly in Taiwan rely on home care. The early changes in the elderly's physical and mental health, however, are difficult for family caregivers to perceive because they lack specialised training in elder care, leading to failure of timely medical treatment. CHARM - Companion Healthcare Aid Robot Manager - is a social companion robot for the elderly suffering from chronic diseases, cognitive impairments, emotional disorders, and the general population. Based on ASUS Zenbo Junior, CHARM provides an autonomous service framework that focuses on the daily-life communication of the elderly. In this framework, we implement the following functions: For the general group, it provides various chat functions, multimedia services, proactive scheduling, etc. Furthermore, it optimises personalised services based on reactions from users. For cognitive health, it provides a brief assessment of levels of cognitive impairment through a natural dialogue to evaluate the risk of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI). For emotional disorders, it provides emotional recognition and suppressing based on natural dialogue to conduct positive emotional guidance. For chronic disease care, it provides an early warning by detecting abnormal events in physiological data. Meanwhile, it equips routine life analysis and medical knowledge database to offer recommendations for health management. This document focuses on the fall warning system and the reminiscence chatting system. Fall Warning System