  Smartwatches are increasingly being used to recognize human daily life
activities. These devices may employ different kind of machine learning (ML)
solutions. One of such ML models is Gradient Boosting Machine (GBM) which has
shown an excellent performance in the literature. The GBM can be trained on
available data set before it is deployed on any device. However, this data set
may not represent every kind of human behavior in real life. For example, a ML
model to detect elder and young persons running activity may give different
results because of differences in their activity patterns. This may result in
decrease in the accuracy of activity recognition. Therefore, a transfer
learning based method is proposed in which user-specific performance can be
improved significantly by doing on-device calibration of GBM by just tuning its
parameters without retraining its estimators. Results show that this method can
significantly improve the user-based accuracy for activity recognition.
