Dietwig Jos Clement Lowet

Factored four way conditional restricted Boltzmann machines for activity recognition

This paper proposes a new learning algorithm for human activity recognition.Its name is factored four way conditional restricted Boltzmann machine (FFW-CRBM).FFW-CRBMs are capable of simultaneous regression and classification.FFW-CRBMs came together with their own training procedure.The training procedure name is sequential Markov chain contrastive divergence. This paper introduces a new learning algorithm for human activity recognition capable of simultaneous regression and classification. Building upon conditional restricted Boltzmann machines (CRBMs), Factored four way conditional restricted Boltzmann machines (FFW-CRBMs) incorporate a new label layer and four-way interactions among the neurons from the different layers. The additional layer gives the classification nodes a similar strong multiplicative effect compared to the other layers, and avoids that the classification neurons are overwhelmed by the (much larger set of) other neurons. This makes FFW-CRBMs capable of performing activity recognition, prediction and self auto evaluation of classification within one unified framework. As a second contribution, sequential Markov chain contrastive divergence (SMcCD) is introduced. SMcCD modifies Contrastive Divergence to compensate for the extra complexity of FFW-CRBMs during training. Two sets of experiments one on benchmark datasets and one a robotic platform for smart companions show the effectiveness of FFW-CRBMs.

Sensing, actuation triggering and decision making for service robots deployed in smart homes

The Florence project develops a robot for elderly that provides multiple Ambient Assisted Living (AAL) and Lifestyle services with a consistent user-interface. The project success is measured by the acceptance of these services by the user group. Enabling such service robotics in (smart) homes requires a robust and flexible platform to collect, enhance and distribute sensory information; and to manipulate the actuators in the environment. A key characteristic of such an environment is that sensors and actuators are not always available, are distributed, and are mobile (due to e.g. the robot and phone mobility). This dynamicity requires a loose coupling between services and sensors/actuators. The paper describes the design principles and high level architecture of the Florence platform that hides the distribution, availability and mobility aspects from the services, and sketches some challenges that lie ahead.