Due to its advantages in numerous industries, including healthcare, sports, rehabilitation, and wearable electronics, gender recognition has garnered a lot of attention in the last ten years. The gender recognition method described in this study uses a wearable sensor device with inertial measurement units to record a variety of activities. The system consists of five sensors that are mounted to the upper and lower bodies while performing seven standing, walking, and climbing exercises that are meant to replicate daily activity. To create a model for gender recognition, we carried out an extensive study based on supervised machine learning. This study identifies a collection of sensor locations and behaviours to better precisely classify gender. Gender classification based on single activity was performed using Random Forest Classifier (RFC) and Support Vector Machines (SVM). Maximum accuracy of 92.06% was gained using Random Forest Classifier for the sensor located at the ankle when walking. Multi-activity based gender classification outperformed former by achieving an accuracy of 94.13% using RFC. This was for the activity combination of Romberg test eyes open, Single leg stance eyes open and Staircase up and down.