Cooperative Discovery of Personal Places from Location Traces

Opportunistic Discovery of Personal Places Using Multi-Source Sensor Data

“…(1) approaches that rely entirely on GPS data for localization [1], [5], [6], [21], [22], [23] and (2) approaches that use other sources of data, such as Wi-Fi routers, cell towers, or Bluetooth beacons in addition to GPS data [3], [8], [9], [12], [24], [25].…”

“…Additionally, Wi-Fi has a relatively large transmission range, which makes Wi-Fi-based approaches less reliable when deciding if a user remained at the same place or not. In [9], [12], we introduced a new approach, where we first use a hierarchy of 1-hop and 2-hop Bluetooth fingerprint matching techniques, followed by Wi-Fi fingerprint matching, in order to discover a group of co-located users of a particular user. Then we borrow the location points from the co-located users to fill the gaps of the target user.…”

“…This can be achieved by carefully augmenting the location traces to reduce or completely remove these data gaps, e.g., by using other sources of information to determine if two small clusters should be merged together or not. Researchers have proposed various methods for merging several small clusters into fewer larger clusters using various types of "secondary data", e.g., using techniques such as fingerprint matching [10], [11] or location data sharing among colocated users [9], [12]. However, these approaches have various limitations, e.g., fingerprint maps may be incomplete or outdated and traces of co-located users (for location sharing) may not always be available.…”

“…For example, modern mobile devices can provide various types of data describing device status and usage, such as the current battery charge level, charging status, and screen/display mode. Various types of physiological data (e.g., heart rate and calorie burn) and behavioral data (e.g., different types of physical activity, step count, and sleep status) can also be obtained from different types of mobile sensors embedded in modern wearables (e.g., devices such as Fitbit, Apple Watch, and Microsoft Band) [8], [12], [13]. Therefore, the approach proposed in this paper proposes to augment a user's location traces using several other types of data, specifically: step counts (wearable), sleep data (wearable), and battery charging status (smartphone).…”

Design Factors of Longitudinal Smartphone-based Health Surveys

“…These contexts include location, motion, proximity to landmarks, environmental conditions, and time of day. These data can be obtained using the phone's built-in sensors [57,59] and help compensate for data collection inaccuracies and biases, such as recall bias, memory limitations, and inadequate compliance that comes from self-reports [53]. Smartphones also make it easier to change survey design on-the-fly, e.g., to adapt future survey questions based on previous responses, subject characteristics, and subject preferences.…”