Advances in wearable technologies and data collection techniques have made Human Activity Recognition (HAR) a prominent study topic. Increased activity recognition is made possible by the availability of vast volumes of data from several sensors, but there are also many difficulties because of the volume, heterogeneity, and complexity of the processing. The main issues that arise in the nexus of big data and HAR are thoroughly reviewed in this study, which is the result of a survey of several academic publications. The results demonstrate important problems, such as the scarcity of sizable labeled datasets, issues with data quality, the necessity for real-time processing, and the requirement for scalable, energy-efficient solutions. Concerns about security and privacy also surface as crucial issues in the implementation of HAR systems. This study emphasizes how critical it is to solve these issues in order to enhance the accuracy and dependability of HAR systems in the big data era.