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Given the growing number of elderly people and patients diagnosed with Parkinson's disease, monitoring functional activities using wearable wireless sensors can be used to promote the Quality of Life and healthier life styles. We propose a novel and practical solution using three small wearable wireless Functional Activity Monitor (FAM) sensors and a smartphone to store, transmit, analyze and update data. Three sensors, each composed of a tri-axial accelerometer and a tri-axial gyroscope, are attached to the chest and both thighs. A computationally efficient signal processing algorithm is designed to accurately measure tilting angles. A continuous activity recognition algorithm is developed using a decision tree based on time series data and spectrum analysis; this algorithm can identify activities of daily life in three general categories: (1) postures such as standing, sitting, and lying; (2) locomotion such as walking; and (3) transitions such as sit-to-stand and stand-to-sit. The results show an accurate angle measurement compared to the motion capture system Optotrak 3020 and a reliable detection of all activities with sensitivity at least 96.2% compared to video recordings.