Supporting healthy growth and cognitive development in infants necessitates accurate recognition and monitoring of developmental milestones during the first year of life. Effective monitoring plays a pivotal role in this process, enabling parents and caregivers to provide personalized support and promote overall well-being. Although Human Activity Recognition (HAR) has been extensively studied, the majority of research has primarily targeted adult populations, leaving infant-specific HAR under-explored. This research advances infant activity recognition through three major contributions: (1) enhancing an existing dataset under the supervision of a pediatric physiotherapist to ensure clinical relevance and accuracy, (2) conducting a comprehensive evaluation of different sequence deep learning models, and (3) investigating the efficacy of ensemble learning through four distinct strategies. Experimental results demonstrate that ensemble learning outperforms individual models, achieving a 4.87% improvement in recall over the best-performing baseline, CNN-LSTM, and a 5.31% improvement over the most stable model, Bi-LSTM. These findings offer promising implications for the design of intelligent infant monitoring systems aimed at early detection of developmental milestones and improved longitudinal assessment.