Freezing of Gait (FoG) is one of the most disturbing symptoms in Parkinson’s disease (PD). Current algorithms that detect this symptom depend on frequency features extracted from wearable systems. These algorithms have only been evaluated under laboratory conditions and, in real life, they might present false positives, reducing the reliability of the algorithm. This paper presents the evaluation of 20 PD patients in their homes and the inclusion of a posture algorithm to contextualize FoG detection. This algorithm, in average, improves specificity a 5% while preserves the sensitivity. In some patients, specificity increases by 11.9% maintaining the sensitivity.