Human activity recognition (HAR) is an emerging field in various applications including smart healthcare that leverages data from sensors and wearable devices to detect, analyze, and monitor various human activities automatically. In smart healthcare, HAR is vital for monitoring patient behavior, assisting with rehabilitation, detecting fall incidents, and improving overall health outcomes. To give remote healthcare support with real-time data, machine learning and deep learning algorithms play an active role in recognizing the activities in a sequence. However, in real scenarios, activities may not be in a sequence and it may consist of several other activities to complete a complex activity. Hence, analyzing the whole process with proper monitoring is required for the medical practitioner and caregivers to understand human behavior. The integration of process mining into human activity recognition enhances the ability to analyze and interpret complex human behaviors, leading to better insights and applications across various fields, including healthcare, security, and smart environments. This chapter explores the challenges and solutions of using process mining in HAR.