Inferring High-Level Behavior from Low-Level Sensors

By Donald J. Patterson, Lin Liao, Dieter Fox, Henry A. Kautz. Ubiquitous Computing, 2003

618 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ae8f9768ffcda5bb055e78b94dd5cc281c0f7bb5

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Citations in the corpus

Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior (2008, methodology): “used a Bayesian model of a mobile user conditioned on the mode of transportation to predict the users future location [20].”
Understanding transportation modes based on GPS data for web applications (2010, neutral): “5.2 User Behavior Recognition Based on Sensor Data Parkka et al. [2006] and Ermes et al. [2006] aim to recognize human activity, such as walking and running, using the data collected by more than 20 kinds of wearable sensors on a person.”
Mobility Detection Using Everyday GSM Traces (2006, methodology): “take a learning approach based on particle-filters to detect modes of transportation [23].”
Mining Individual Life Pattern Based on Location History (2009, background): “In the meantime, some techniques have been proposed to discovery higher level knowledge from individual GPS data [15], including detecting significant locations of an individual, predicting the movement destination [6], [11], recognize individual mobility [14], etc.”
Sensor-Based Abnormal Human-Activity Detection (2008, background): “In the past decade, various model-based approaches such as hidden Markov models (HMMs) [14] and dynamic Bayesian networks (DBNs) [15], [20], [31] have been proposed to model and recognize users’ activities.”
Enabling Technologies for Green Internet of Things (2017, background): “many cases, inferring from past data can be adequate [94] and is an area that can lead to significant energy savings for sensor”
A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, methodology): “[104] first divide trajectories into 10-m segments and then leverage CRF (conditional random field) model to map-match segments onto road networks.”
iBAT: detecting anomalous taxi trajectories from GPS traces (2011, methodology): “Gathering and analyzing the large-scale GPS traces have provided us a great opportunity to reveal the hidden “facts” about the city dynamics and human behaviors, enabling diverse innovative applications [22, 13, 18, 30, 26, 21, 23, 28, 24].”
Tracking and Modeling of Human Activity Using Laser Rangefinders (2010, methodology): “Patterson et al. [36] used this approach to model activity patterns at a city level.”
Place Lab: Device Positioning Using Radio Beacons in the Wild (2005, neutral): “[9].”
Towards improving trust in context-aware systems by displaying system confidence (2005, methodology): “Maybe the most advanced systems like the Lumiere project [10], the Lookout project [11] or the Activity Compass [22] are based on techniques such as Bayesian modeling and inference, utility, and decision theory.”
A survey of computational location privacy (2009, methodology): “[52] used GPS traces to infer, in real time, a moving person’s mode of transportation (i.”
Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things (2020, methodology): “[26] presented a Bayesian model with traveler’s moving data drawn from the GPS sensor stream to learn travelers’ transportation mode and their”
Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application (2008, methodology): “We employ GPS location estimates as input to a mobility mode classifier [22][23].”
PEIR, the personal environmental impact report, as a platform for participatory sensing systems research (2009, methodology): “The work of [16, 17] uses GPS data with external knowledge about bus routes and bus stops to infer and predict a user’s transportation mode such as walking, driving, or taking a bus, and in [18, 19] models of a user’s activities and places from traces of GPS data and locations of restaurant, stores,”