With the recent advances in Internet of Things, smarthomes containing a variety of sensors are expected to monitor and react to the activities of daily living (ADL) of the residents with the goal of improving convenience, comfort, and safety. However, a single human activity may trigger multiple sensors, and each sensor is triggered by multiple activities. Also, the very human nature of interleaving activities along with the presence of multiple inhabitants makes ADL detection a complex problem. This paper tackles the problem with a data driven de-multiplexing approach termed TSFS (Temporal-Sensor-Frequency-Stitch) that disentangles each individual activity from the sensor stream and thus simplifies the ADL recognition problem. TSFS leverages temporal(TT), sensory(SS), and frequency(FF) information to discriminate between two consecutive/parallel activities, followed by a stitch(SS) to enhance segmentation. The multi-faceted approach helps us in correctly identifying segments; extensive evaluation on 10 public datasets reveals that TSFS is better by 185 percent when compared to a simple temporal segmentation approach. The activity detection accuracy yields within 93 percent of the best possible (an oracle) approach.