Activity recognition using semi-Markov models on real world smart home datasets

Activity recognition using semi-Markov models on real world smart home datasets

Accurately recognizing human activities from sensor data recorded in a smart home setting is a challenging task. Typically, probabilistic models such as the hidden Markov model (HMM) or conditional random fields (CRF) are used to map the observed sensor data onto the hidden activity states. A weakness of these models, however, is that the type of distribution used to model state durations is fi...

Simulation of Smart Home Activity Datasets

A globally ageing population is resulting in an increased prevalence of chronic conditions which affect older adults. Such conditions require long-term care and management to maximize quality of life, placing an increasing strain on healthcare resources. Intelligent environments such as smart homes facilitate long-term monitoring of activities in the home through the use of sensor technology. A...

Non-stationary Hidden Semi Markov Models in Activity Recognition

Activity recognition is a process by which the ongoing observed behavior of an agent is tracked and mapped to a given model, explaining the behavior and accounting for hidden or unobservable state (e.g., goals or beliefs of the observed agents). Various methods for activity recognition exist. A popular family of such methods rely on Hidden Markov Models HMMs and variants for recognition. These ...

Using Statistico-Relational Model for Activity Recognition in Smart Home

This paper presents the use of a model which mixes logical knowledge and statistical inference to recognize Activities of Daily Living (ADL) from sensors in a smart home. This model called Markov Logic Network (MLN) has different implementations and we propose to compare three of them, from the widely used Alchemy to the new generic framework DeepDive. Finally, we discuss the interest these sof...

Speech recognition using semi-hidden Markov models of multiple features