he use of inertial measurement units (IMUs) in motion analysis for clinical purpose is relatively recent. However, the use of such system in free environment remains sparse. This is in part due the lack of robust algorithms to handle large volumes of data for performance evaluation and patient diagnosis. The present work examines the ability of using Empirical Mode Decomposition and discrete-time detection of events to automatically detect and segment tasks associated with activities of daily living (ADL) using IMUs. Seven healthy older adults (73± 4 years old) performed ADL tasks in a simulated apartment during trials of different durations (3, 4, and 5-min). They wore a suit (Synertial-IGS180) comprised of 17-IMUs positioned strategically on body segments to capture full body motion. After a systematic process examining time series of each sensor, it was determined that 6-IMUs were sufficient to detect the 9 tasks at hand (such as walking, sit to stand, stand to sit, reaching to the ground to pick or to put down objects on the floor, step an obstacle and turning). The proposed method automatically identified the proper set of template waveforms associated to ADL tasks based on kinematic data acquired from the selected IMUs. The ground truth on timing of tasks was established by visual segmentation of recordings using the system's software. Despite the variation in the occurrences of the performed tasks (freely moving), the proposed algorithm exhibited high global accuracy under unscripted conditions of motion, for both Se. and Sp. of 97% (Nevents=1999), using a few features and without learning process. This work will eventually allow for the assessment of mobility performance within the segmented signals; specifically how well the person is moving in his/her environment.