This research investigates the detection of gestures using a torso-worn accelerometer sensor. Using the Conflab dataset, we focus on gestures during conversations in mingling scenarios. Due to significant variability in gesture styles among individuals, traditional methods face challenges in building personalized models. Our experiments demonstrate that Transductive Parameter Transfer (TPT), an adaptive transfer learning method, can more effectively model these individual differences in gesturing. To gain insights into individual expressiveness, we classify gestures into three classes: 'no gesture,' 'normal,' and 'large' gestures. TPT performed an average AUC score of 0.84 in binary classification and 0.77 in multiclass classification. These findings highlight the potential of using a single torso-worn accelerometer to understand social behavior in naturalistic settings.