  Despite the advent of wearable devices and the proliferation of smartphones,
there still is no ideal platform that can continuously sense and precisely
collect all available contextual information. Ideally, mobile sensing data
collection approaches should deal with uncertainty and data loss originating
from software and hardware restrictions. We have conducted life logging data
collection experiments from 35 users and created a rich dataset (9.26 million
records) to represent the real-world deployment issues of mobile sensing
systems. We create a novel set of algorithms to identify human behavioral
motifs while considering the uncertainty of collected data objects. Our work
benefits from combinations of sensors available on a device and identifies
behavioral patterns with a temporal granularity similar to human time
perception. Employing a combination of sensors rather than focusing on only one
sensor can handle uncertainty by neglecting sensor data that is not available
and focusing instead on available data. Moreover, by experimenting on two real,
large datasets, we demonstrate that using a sliding window significantly
improves the scalability of our algorithms, which can be used by applications
for small devices, such as smartphones and wearables.
