This paper considers the emergency behavior detection problem inside an elevator. As elevators come in different shapes and emergency behavior data are scarce, we propose a skeleton-based view-invariant framework to tackle the camera view angle variation issue and the data collection issue. The proposed emergency fall detection model only needs to be trained for a target camera, which is deployed in an elevator at a manufacture's lab, from which a large amount of training data can be collected. The deployment of a source camera, which is in a customer-side elevator, hence can be customized and almost no training effort is needed. Our framework works in four stages. First, a 2D RGB input image is taken from the source camera and a 2D human skeleton is obtained by 2D pose estimation (AlphaPose). Second, the 2D skeleton is converted to a 3D human skeleton by 3D pose estimation (3D pose baseline). Third, a pre-trained rotation-translation (RT) transform (Procrustes analysis (PA)) aligns the 3D pose representations to the target camera view. Finally, a dual 3D pose baseline deep neural networks (D3PBDNN) model for human fall detection is proposed to perform the recognition task. We gather a human fall detection dataset inside different elevators from various view angles and validate our proposal. Experimental results successfully attain almost equivalent accuracy to that of a source camera-trained model.