‘This work builds on a number of previous pieces using spatial and environmental sensing to re-present collected environmental data in a dynamic and immersive way.
This piece is centred around a pendulum with a DIY electronic weight containing a three-axis accelerometer and a digital light sensor, tracking directional motion and ambient light levels. When the pendulum is swung, three streams of audio, video and light will be activated according to the speed, angle and orientation of the pendulum. The audio and video channels are three sound (indoor, outdoor and electromagnetic) and video (timelapse footage of transport, foot traffic and cloud movement in the sky) recordings taken from around newcastle, and the LED lighting responds very directly to X, Y and Z axes by creating corresponding Red, Green and Blue values.
By moving the pendulum weight these situations can be interacted with in a fleeting, exploratory manner. Without any interaction the installation will lie dormant, waiting for energy to be added by a passer by.
There is a fourth channel of video, audio and light which can be activated by shining a strong light directly onto the light-sensitive part of the weight (Phone LED torches work particularly well).’
This interactive piece was produced for Square One, and the video shows both some raw video footage from the installation as well as footage of myself and others interacting with the ‘pendulum’. It proved a popular installation, with people really enjoying interacting with it in a very direct way. I purposely designed the installation to be as responsive and durable as possible, which led to bursts of colour coming from the installation space throughout the event with people shaking, swinging and spinning the pendulum around.
The pendulum was composed of an Arduino Nano interfacing with a MPU-6050 accelerometer and BH1750 light sensor, transmitting the data wirelessly using a nRF24l01 radio transceiver, which was picked up by another nRF24l01 in the form of a custom-built Arduino ‘radio shield’ and the data was read and parsed in Max MSP, which handled sound, light and video.
Photos and event by Josh Borom and Matt Pickering