Freezing of gait (FOG) is a particular and troublesome symptom occurring in some Parkinsonian patients .This brief, episodic absence or marked reduction of forward progression of the feet despite the intention to walk is associated to a fall risk. Automatized FOG episode detection would allow systematic assessment of patient state and objective evaluation of the clinical effects of treatments. Techniques have been proposed in the literature to identify FOG episodes based on the frequency properties of inertial sensor signals. Our objective here is to adapt and extend these FOG detectors in order to include other associated gait pattern changes, like festination. The proposed approach is based on a single wireless inertial sensor placed on the patient's lower limbs. Based freezing detectors are not sufficient to detect all FOG and festination episodes. Stride length and cadence are valuable inputs to anticipate the occurrence of upcoming FOG events. The investigators have developed a solution based on a minimal number of embedded sensors and detection algorithms for future real-time applications. It is well known that auditory rhythmic stimulation or visuals marks on the ground would improve dramatically gait in patients with FOG. This sensibility to cueing leads some teams to develop some mobility aid devices rhythmic. Our purpose is to develop a wearable assistant which would trigger the most appropriate cueing aid just before occurrence of the freezing episode.