Music Maker – A Camera-based Music Making Tool for Physical Rehabilitation

The therapeutic effects of playing music are being recognized increasingly in the field of rehabilitation medicine. People with physical disabilities, however, often do not have the motor dexterity needed to play an instrument. We developed a camera-based human-computer interface called "Music Maker" to provide such people with a means to make music by performing therapeutic exercises. Music Maker uses computer vision techniques to convert the movements of a patient's body part, for example, a finger, hand, or foot, into musical and visual feedback using the open software platform EyesWeb. It can be adjusted to a patient's particular therapeutic needs and provides quantitative tools for monitoring the recovery process and assessing therapeutic outcomes. We tested the potential of Music Maker as a rehabilitation tool with six subjects who responded to or created music in various movement exercises. In these proof-of-concept experiments, Music Maker has performed reliably and shown its promise as a therapeutic device.

Extending snBench to Support a Graphical Programming Interface for a Sensor Network Tasking Language (STEP)

The purpose of this project is the creation of a graphical "programming" interface for a sensor network tasking language called STEP. The graphical interface allows the user to specify a program execution graphically from an extensible pallet of functionalities and save the results as a properly formatted STEP file. Moreover, the software is able to load a file in STEP format and convert it into the corresponding graphical representation. During both phases a type-checker is running on the background to ensure that both the graphical representation and the STEP file are syntactically correct. This project has been motivated by the Sensorium project at Boston University. In this technical report we present the basic features of the software, the process that has been followed during the design and implementation. Finally, we describe the approach used to test and validate our software.

Grid Cell Hexagonal Patterns Formed by Fast Self-Organized Learning within Entorhinal Cortex

Grid cells in the dorsal segment of the medial entorhinal cortex (dMEC) show remarkable hexagonal activity patterns, at multiple spatial scales, during spatial navigation. How these hexagonal patterns arise has excited intense interest. It has previously been shown how a selforganizing map can convert firing patterns across entorhinal grid cells into hippocampal place cells that are capable of representing much larger spatial scales. Can grid cell firing fields also arise during navigation through learning within a self-organizing map? A neural model is proposed that converts path integration signals into hexagonal grid cell patterns of multiple scales. This GRID model creates only grid cell patterns with the observed hexagonal structure, predicts how these hexagonal patterns can be learned from experience, and can process biologically plausible neural input and output signals during navigation. These results support a unified computational framework for explaining how entorhinal-hippocampal interactions support spatial navigation.