Sensors, Vol. 22, Pages 6323: Deep Learning-Based Subtask Segmentation of Timed Up-and-Go Test Using RGB-D Cameras

Sensors, Vol. 22, Pages 6323: Deep Learning-Based Subtask Segmentation of Timed Up-and-Go Test Using RGB-D Cameras Sensors doi: 10.3390/s22176323 Authors: Yoon Jeong Choi Yoo Sung Bae Baek Dong Cha Je Ha Ryu The timed up-and-go (TUG) test is an efficient way to evaluate an individual’s basic functional mobility, such as standing up, walking, turning around, and sitting back. The total completion time of the TUG test is a metric indicating an individual’s overall mobility. Moreover, the fine-grained consumption time of the individual subtasks in the TUG test may provide important clinical information, such as elapsed time and speed of each TUG subtask, which may not only assist professionals in clinical interventions but also distinguish the functional recovery of patients. To perform more accurate, efficient, robust, and objective tests, this paper proposes a novel deep learning-based subtask segmentation of the TUG test using a dilated temporal convolutional network with a single RGB-D camera. Evaluation with three different subject groups (healthy young, healthy adult, stroke patients) showed that the proposed method demonstrated better generality and achieved a significantly higher and more robust performance (healthy young = 95.458%, healthy adult = 94.525%, stroke = 93.578%) than the existing rule-based and artificial neural network-based subtask segmentation methods. Additionally, the results indicated that the input from the pe...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research