Fall is a critical event (mainly for the elderly and dependent subjects) that requires prompt assistance. In the past, many commercial wearable sensors have been used to detect falls, with limitations in terms of cost and usability. Consequently, there is still a growing interest in the scientific community studying issues related to active and healthy aging. The integration of fall detection functionality into commercial smartwatches represents a solution of clear advantage. It is generally related to the application of threshold values to the accelerometer signals acquired by the wearable device but with important limitations in terms of accuracy. More recently, Machine Learning and Deep Learning techniques were widely investigated, but again, adequate classification results were not obtained mainly due to the lack of specific fall datasets and the imbalance within them of events to be classified. The proposed work describes …