Many fall detection systems are being used to provide real-time responses to fall occurrences.Automated fall detection is challenging because it requires very high accuracy to be clinicallyacceptable. Recent research has tried to improve sensitivity while reducing the high rate offalse positives. Nevertheless, there are still limitations in terms of having e±cient learningapproaches and proper datasets to train. To reduce false alarms, one approach is to add morenonfall data as negative samples to train the deep learning model. However, this approachincreases class imbalance in the training set. To tackle this problem, we propose a multi-taskdeep learning approach that divides datasets into multiple training sets for multiple tasks.We prove this approach gives better results than a single-task model trained on all datasets