PoNG understands the importance of data collection, and has been improving tech to push the boundaries of behavioral data collection.
We engage the commercially-available and programmable FitLights to perform a Go/No-Go game-like task that participants can play with their hands or feet to measure accuracy and reaction time. The disc-shaped lights can be arranged in a foam floor mat made so that the subjects can stand in the center and easily reach the lights in front of and to the side of them. They can also be attached to a desk surface for hand reaching tasks.
Markerless Motion Capture
Our two eyes let us see the world in three dimensions by comparing different views at the same time. Similarly, camera based motion capture lets a computer build up a three dimensional image by recording the same scene from multiple angles at the same time. Most human motion capture technologies simplify the task by tracking a few dozen small ping-pong balls attached to an individual’s joints, elbows, knees, feet, hands, and so on. However, some individuals are sensitive to wearing extraneous sensors on their body, particularly young children with developmental differences with whom collecting motion capture data for repetitive behaviors, for example, can do the most good. Instead, with markerless motion capture we use multiple cameras to reconstruct body position from camera data alone and can observe movement and repetitive behavior in a wide range of children and adults.
Equilibrium Balance Game
We use the BTracks Balance Tracking System Board to obtain center of balance measures which are integrated into a custom-built game run by the Unity game engine. Players step on the balance board and shift their weight to the left or right to roll a ball (represented on the computer screen) through a series of obstacles. The game itself outputs data such as duration of the task, number of collisions, objects collected, and the level difficulty. Through this, we can measure how long the user remained engaged with the task and, via the video recording, match the data with heart-rate, repetitive behaviors, and signs of perseverance.
To help us understand not just the quantity of object interaction but how the subjects interacted with them, we embedded small sensors that collect accelerometer (speed and force) and gyroscope (position and angle) data into commercially available 4” foam blocks with zippered velour covers to build Motion-sensing blocks. The chips wirelessly send the 3D position, velocity, and acceleration information to a local computer while kids participate in block building tasks.
Tap Glove was created to measure the gross and fine motor tasks/activities while automating data collection in order to identify movement differences in various populations. The custom glove is made of soft, flexible cotton and equipped with conductive cloth on the fingertips (where your fingerprints would be). When a fingertip is touched to the thumb, an electrical circuit is closed, recording the time and frequency of a “tap.” Using a suite of synchronized sensing hardware with cognitive tasks, we have collected pilot data to illustrate the movement differences during tapping tasks between typically developing children and those with autism spectrum disorder.
Check out Recent Projects for the technical details.