Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moving Target Classification and Tracking from Real-time Video
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
What Shall We Teach Our Pants?
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
Analyzing features for activity recognition
Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
eWatch: A Wearable Sensor and Notification Platform
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
A Robust Video Object Tracking by Using Active Contours
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
ACM Computing Surveys (CSUR)
The Mobile Sensing Platform: An Embedded Activity Recognition System
IEEE Pervasive Computing
Ageing in a networked society: social inclusion and mental stimulation
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Activity Recognition for Everyday Life on Mobile Phones
UAHCI '09 Proceedings of the 5th International on ConferenceUniversal Access in Human-Computer Interaction. Part II: Intelligent and Ubiquitous Interaction Environments
Location and activity recognition using ewatch: a wearable sensor platform
Ambient Intelligence in Everyday Life
Motion- and location-based online human daily activity recognition
Pervasive and Mobile Computing
Fall-detection simulator for accelerometers with in-hardware preprocessing
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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Patient monitoring by camera surveillance within the private surrounding suffers on privacy issues as well as high requirements on sensor technology and infrastructure. Because of those limitations there is not much knowledge on user activities and behavior in the home environment. In this paper we introduce a novel concept of local activity monitoring using a very slim infrastructure. The required technical environment consists only of a Wi-Fi-Webcam and a mobile phone. With a bi-modal sensor fusion approach we improve the optical activity monitoring by inclusion of electromechanical movement sensors data taken from a mobile phone. The fusion of data from cameras and mobile acceleration sensors allow for a comprehensive, non obtrusive observation of people which can be used for behavior analysis, reactive assistance and support, and natural interfaces especially for the elderly.