Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
A long-term evaluation of sensing modalities for activity recognition
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection
EWSN'08 Proceedings of the 5th European conference on Wireless sensor networks
Using Dempster-Shafer theory of evidence for situation inference
EuroSSC'09 Proceedings of the 4th European conference on Smart sensing and context
What is happening now? Detection of activities of daily living from simple visual features
Personal and Ubiquitous Computing
Using a live-in laboratory for ubiquitous computing research
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Object-based activity recognition with heterogeneous sensors on wrist
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
Journal of Ambient Intelligence and Smart Environments - Design and Deployment of Intelligent Environments
Hi-index | 0.00 |
Smart homes have a user centered design that makes human activity as the most important type of context to adapt the environment according to people's needs. Sensor systems that include a variety of ambient, vision based, and wearable sensors are used to collect and transmit data to reasoning algorithms to recognize human activities at different levels of abstraction. Despite various types of action primitives are extracted from sensor data and used with state of the art classification algorithms there is little understanding of how these action primitives affect high level activity recognition. In this paper we utilize action primitives that can be extracted from data collected by sensors worn on human body and embedded in different objects and environments to identify how various types of action primitives influence the performance of high level activity recognition systems. Our experiments showed that wearable sensors in combination with object sensors clearly play a crucial role in recognizing high level activities and it is indispensable to use wearable sensors in smart homes to improve the performance of activity recognition systems.