Wireless body area sensor network for posture and gait monitoring of individuals with Parkinson´s disease

This paper presents a wireless body area sensor network that detects and records real-time posture and gait kinematic data from individuals with Parkinson´s disease. The network comprises wearable sensors placed at lower limbs and back of a human body to measure user´s kinematics. The collected data are transmitted wirelessly to a receiver and stored in cloud-based database. The time series kinematic data is interpreted with adaptive fractal analysis (AFA) to differentiate a healthy subject from another with PD. We use frequency analysis to differentiate spontaneous movement from cued movement of clinical evaluations of several persons with Parkinson disease (PD).