Integrating recognition and reasoning in smart environments

Trends and Future Directions, 2012

This chapter introduces a framework for enabling context-aware behaviors in smart environment applications, with a special emphasis on smart homes and similar scenarios. In particular, an ontologybased architecture is described that allows system designers to specify non-trivial situations the system must be able to detect on the basis of available sensory data. Relevant situations may include activities and events that could be prolonged over long periods of time. Therefore, the ontology encodes temporal operators that, once applied to sensory information, allow the recognition and efficient correlation of different human activities and other events whose temporal relationships are contextually important. Special emphasis is devoted to actual representation and recognition of temporally distributed situations. The proof of the concept is validated through a thoroughly described example of system usage.

Journal on Multimodal …, 2008

2000

Core to the development of an intelligent environment is the understanding of where, when, and for how long, actions take place. Our work addresses the process of discovering patterns of be- haviour which may reflect preferences of a user on the use of devices. A solution to this problem is essential for the automatization of en- vironments like Smart Homes

2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698), 2003

This paper proposes a novel architecture for multisensor data fusion in the context of Ambient Intelligence (AmI). The proposed system integrates an heterogeneous network of sensors with CCD cameras and computational units working together in a LAN. Activities of humans interacting in the monitored area are detected and classified by combining sensors data output with a neural method.

IEEE Transactions on Human-Machine Systems, 2013

Automated monitoring and the recognition of activities of daily living (ADLs) is a key challenge in ambient-assisted living (AAL) for the assistance of the elderly. Within this context, a formal approach may provide a means to fill the gap between the low-level observations acquired by sensing devices and the highlevel concepts that are required for the recognition of human activities. We describe a system named ARA (Automated Recognizer of ADLs) that exploits propositional temporal logic and model checking to support automated real-time recognition of ADLs within a smart environment. The logic is shown to be expressive enough for the specification of realistic patterns of ADLs in terms of basic actions detected by a sensorized environment. The online model checking engine is shown to be capable of processing a stream of detected actions in real time. The effectiveness and viability of the approach are evaluated within the context of a smart kitchen, where different types of ADLs are repeatedly performed. Index Terms-Activities of Daily Living (ADL), activity recognition, Ambient Assisted Living (AAL), model checking, real-time processing, temporal logic. I. INTRODUCTION T HE demographics of the global population are changing, with an increasing number of both the elderly and people suffering from long-term chronic conditions [32]. Independence, well-being, and health status of these people largely depend on their autonomy in the accomplishment of basic actions of daily self-care. This level of self care is often evaluated with reference to the execution capabilities of standard Activities of Daily Living (ADLs) [24], which address self-care procedures such as feeding, transferring, and washing [38]. Automated activity recognition [2], [40] applied to the specific scenarios of ADLs has the potential to play a pivotal role in supporting the quality of life and improving living experience. To this end, a growing number of pervasive technologies, Manuscript

2013 IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 2013 IEEE 10th International Conference on Autonomic and Trusted Computing, 2013

Smart spaces are everyday environments augmented with computing technologies that enhance human experience and activity performance. Continuous recognition of the presence of people, their identity, location, movement and activity patterns in real-time is a key challenge to address if smart spaces are to be envisioned as personalized and adaptive spaces. This paper introduces the multiple technologies available for human sensing and identification, discussing their advantages and disadvantages. In particular, Kitchen As-A-Pal is described as a smart space with real-time human sensing capabilities using computer vision by fusing fisher face recognition and skeletal tracking approaches. A wall-mounted Kinect is used for both single occupant and multi-occupant settings in kitchen As-A-Pal. The fused approach gives human identity recognition accuracy of 91.75% precision and 66% recall values for single occupant setting with good smart space coverage. Challenges do exist for human identity recognition in multi-occupant settings.

Smart environments rely on artificial intelligence techniques to make sense of the sensor data that is collected in the environment and to use the information for data analysis, prediction, and event automation. In this paper we discuss an important smart environment technology -resident activity recognition. This technology is beneficial for health monitoring of a smart environment resident but accurate recognition is difficult for real-world situations. We describe our approach to activity recognition and discuss how incorporating temporal reasoning improves the accuracy of our algorithms. We validate our algorithm on real sensor data collecting in our smart apartment testbed.

The pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. A primary challenge that needs to be tackled to meet this need is the ability to recognize and track functional activities that people perform in their own homes and everyday settings. In this paper we look at approaches to perform real-time recognition of Activities of Daily Living. We enhance other related research efforts to develop approaches that are effective when activities are interrupted and interleaved. To evaluate the accuracy of our recognition algorithms we assess them using real data collected from participants performing activities in our on-campus smart apartment testbed.