Extending event-driven experiments for human activity for an assistive environment

ABSTRACT

Many different aspects go into the generation of the data and methods needed to recognize human activity within an ambient assistive living environment. The Heracleia @Home apartment has been configured to include both wireless sensor networks as well as wired sensors of multiple types to capture information about subjects in the living space. The responses of these sensors are then analyzed to create key episodic events that occur at each time and place. Once these sensors are equipped to recognize a set of events, the data can then be processed by applying a Hidden Markov Model approach and by an adaptation of the Baum-Welch algorithm to identify different human activities within the assistive living environment. An application has been created to help manage and track the different sensors placed within the environment.

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Index Terms

• Computer systems organization

  • Embedded and cyber-physical systems

    • Sensors and actuators

• Computing methodologies

  • Artificial intelligence

    • Natural language processing

      • Speech recognition

• Hardware

  • Communication hardware, interfaces and storage

• Networks

  • Network types

    • Mobile networks

    • Wireless access networks

• Social and professional topics

  • Professional topics

    • Computing profession

      • Assistive technologies

  • User characteristics

    • People with disabilities