Freezing of gait is one of the most disturbing and incapacitating symptoms in Parkinson's Disease. This is defined as a sudden block in effective stepping, provoking anxiety, stress and falls. FoG is usually evaluated by means of different questionnaires; however, this method has shown not to be reliable since it is subjective due to its dependence on patients' and caregivers' judgement. Several authors have analysed the usage of MEMS inertial systems to detect FoG with the aim of objective evaluate this symptom. So far, a threshold-based method based on accelerometer's frequency response has been employed in many works; nonetheless, since it has been developed and tested in laboratory conditions, it provides a much lower accuracy at patients' home. This work proposes a new set of features to detect FoG by using accelerometers, which is compared with three previously reported approaches to detect FoG. The different feature sets are trained by means of several machine learning classifiers; furthermore, different window sizes are also evaluated. Results show that the proposed method detects FoG at patients' home with 91.7% and 87.4% of sensitivity and specificity, respectively, enhancing the results of former methods between a 5% and 11% and providing a more balanced rate of true positives and true negatives.