Proactive Instructions for Furniture Assembly
UbiComp '02 Proceedings of the 4th international conference on Ubiquitous Computing
Video Surveillance and Human Activity Recognition for Anti-Terrorism and Force Protection
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
ContextPhone: A Prototyping Platform for Context-Aware Mobile Applications
IEEE Pervasive Computing
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mobile Networks and Applications
Wearable Activity Tracking in Car Manufacturing
IEEE Pervasive Computing
Activity Recognition for the Smart Hospital
IEEE Intelligent Systems
Daily Routine Classification from Mobile Phone Data
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
What did you do today?: discovering daily routines from large-scale mobile data
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A Human Activity Aware Learning Mobile Music Player
Proceedings of the 2007 conference on Advances in Ambient Intelligence
A context-aware music recommendation system using fuzzy bayesian networks with utility theory
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Predicting human behaviour from selected mobile phone data points
Proceedings of the 12th ACM international conference on Ubiquitous computing
Review: Situation identification techniques in pervasive computing: A review
Pervasive and Mobile Computing
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Activity inference attempts to identify what a person is doing at a given point in time from a series of observations. Since the 1980s, the task has developed into a fruitful research field and is now considered a key step in the design of many human-centred systems. For activity inference, wearable and mobile devices are unique opportunities to sense a user's context unobtrusively throughout the day. Unfortunately, the limited battery life of these platforms does not always allow continuous activity logging. In this paper, we present a novel technique to fill in gaps in activity logs by exploiting both short- and long-range dependencies in human behaviour. Inference is performed by sequence alignment using scoring parameters learnt from training data in a probabilistic framework. Experiments on the Reality Mining dataset show significant improvements over baseline results even with reduced training and long gaps in data.