C4.5: programs for machine learning
C4.5: programs for machine learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
Affective diary: designing for bodily expressiveness and self-reflection
CHI '06 Extended Abstracts on Human Factors in Computing Systems
InSense: Interest-Based Life Logging
IEEE MultiMedia
Mobile Networks and Applications
Activity sensing in the wild: a field trial of ubifit garden
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
AAMPL: accelerometer augmented mobile phone localization
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
Proceedings of the 6th ACM conference on Embedded network sensor systems
BALANCE: towards a usable pervasive wellness application with accurate activity inference
Proceedings of the 10th workshop on Mobile Computing Systems and Applications
Mapping User Needs to Smartphone Services for Persons with Chronic Disease
ICOST '09 Proceedings of the 7th International Conference on Smart Homes and Health Telematics: Ambient Assistive Health and Wellness Management in the Heart of the City
Combating obesity trends in teenagers through persuasive mobile technology
ACM SIGACCESS Accessibility and Computing
IMCE '09 Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
A survey of mobile phone sensing
IEEE Communications Magazine
Activity Monitoring Using a Smart Phone's Accelerometer with Hierarchical Classification
IE '10 Proceedings of the 2010 Sixth International Conference on Intelligent Environments
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
LifeMap: A Smartphone-Based Context Provider for Location-Based Services
IEEE Pervasive Computing
Empowerment through seamfulness: smart phones in everyday life
Personal and Ubiquitous Computing
CONTEXT'11 Proceedings of the 7th international and interdisciplinary conference on Modeling and using context
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Jog falls: a pervasive healthcare platform for diabetes management
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
IEEE Transactions on Information Technology in Biomedicine
Design and validation of a light inference system to support embedded context reasoning
Personal and Ubiquitous Computing
Personal and Ubiquitous Computing
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Automated activity recognition enables a wide variety of applications related to child and elderly care, disease diagnosis and treatment, personal health or sports training, for which it is key to seamlessly determine and log the user's motion. This work focuses on exploring the use of smartphones to perform activity recognition without interfering in the user's lifestyle. Thus, we study how to build an activity recognition system to be continuously executed in a mobile device in background mode. The system relies on device's sensing, processing and storing capabilities to estimate significant movements/postures (walking at different paces--slow, normal, rush, running, sitting, standing). In order to evaluate the combinations of sensors, features and algorithms, an activity dataset of 16 individuals has been gathered. The performance of a set of lightweight classifiers (Naïve Bayes, Decision Table and Decision Tree) working on different sensor data has been fully evaluated and optimized in terms of accuracy, computational cost and memory fingerprint. Results have pointed out that a priori information on the relative position of the mobile device with respect to the user's body enhances the estimation accuracy. Results show that computational low-cost Decision Tables using the best set of features among mean and variance and considering all the sensors (acceleration, gravity, linear acceleration, magnetometer, gyroscope) may be enough to get an activity estimation accuracy of around 88 % (78 % is the accuracy of the Naïve Bayes algorithm with the same characteristics used as a baseline). To demonstrate its applicability, the activity recognition system has been used to enable a mobile application to promote active lifestyles.