Human beings rely heavily on estimation of poses in order to access their body movements. Human pose estimation methods take advantage of computer vision advances in order to track human body movements in real life applications. This comes from videos which are recorded through available devices. These paradigms provide potential to make human movement measurement more accessible to users. The consumers of pose estimation movements believe that human poses content tend to supplement available videos. This has increased pose estimation software usage to estimate human poses. In order to address this problem, we develop hybrid ensemble-based group pose estimation method to improve human health. This proposed hybrid ensemble-based group pose estimation method aims to detect multi-person poses using modified group pose estimation and modified real time pose estimation. This ensemble allows fusion of performance of stated methods in real time. The input poses from images are fed into individual methods. The pose transformation method helps to identify relevant features for ensemble to perform training effectively. After this, customized pre-trained hybrid ensemble is trained on public benchmarked datasets. This is followed by evaluation with test datasets. The effectiveness and viability of proposed method is established based on comparative analysis of group pose estimation methods and experiments conducted on benchmarked datasets. It provides best optimized results in real-time pose estimation. It makes pose estimation method more robust to occlusion and improves dense regression accuracy. These results have affirmed potential application of this method in several real-time situations with improvement in human health life span.