With the advancement of technology such as video monitoring, Internet-of-things, cloud, and machine learning, Industry 4.0 is working continuously to ensure the security of workers. The workers are equipped with sensors to analyze their activities. In general, the recognition of human activities in cloud-based industrial scenario is leveraged to monitor the safety of the workers. This paper introduced a new optimal clustering method for the activity recognition of workers in industry using cloud based IoT environment. The proposed method uses the temporal and spatial features of human workers in industry. The proposed method is tested on publicly available dataset of different activities maintained into three groups, namely movement, gestures, and object handling, in the context of the medium and small industrial environment. The experimental findings validate that the proposed method achieves 80.2 % , 81.05 % and 80.19 % of average accuracy for movement, gesture, and object handling activities, which clearly outperformed the fuzzy c-means, particle-swarm optimization, and HMM-based activity recognition methods.