As the population aged 65 and above increases, falls among these older adults emerge as a significant public health concern, leading to disabil- ities and economic burdens. Preventative strategies and personalized fall risk assessments are essential for mitigating fall risks. Human Activity Recognition in early fall risk detection by monitoring everyday ac- tivities in older adults could assess patients fall risks. However, current literature has overlooked the older adult demographic by only measuring adults younger than 65, under representing the older population. This research specifically focuses on identifying key features from head-mounted Inertial Measurement Unit (IMU) data using machine learning to classify Sit-to-Walk (STW) and Walk-to-Sit (WTS) move- ments, which are commonly associated with high risk of fall. In addition these movements can be essential in monitoring changes in performance to asses fall risk. We analyzed five activities STW, WTS, Sitting, Swing phase, and Others. Using three feature selec- tion methods (Mutual Information Gain, ANOVA, Recursive Feature Elimination) on 116 extracted fea- tures we were able to rank the features and select the top ten. The study then evaluated the accuracy of three classifiers (Logistic Regression, Random For- est, and K-Nearest Neighbor or Support Vector Ma- chine) with these features. Results indicated that the ANOVA and Random Forest classifier combination achieved the highest total accuracy of 95%, with Ran- dom Forest performing exceptionally well in STW and WTS classifications, reaching up to 81% accu- racy. Commonly selected features across all methods included the accelerometer’s maximum x-axis mea- sured and its energy in both time and frequency do- mains. This model’s performance is comparable with existing literature and validates its effectiveness in fall risk detection.