A Wii-controlled safety device for electric chainsaws
AbstractForestry continues to represent one of the most hazardous economic sectors of human activity, and historically, the operation of chainsaws has mainly been restricted to professional lumberjacks. In recent years, because of low cost, chainsaws have become popular among unprofessionals, e.g. for cutting firewood and trimming trees. Serious or lethal lesions due to the use of chainsaws or electric chainsaws are often observed by traumatologists or forensic pathologists. Such serious accidents often occur during occupational activities and are essentially due to kickback or uncorrected use of the tool, or when the operator falls down losing the control of the implement. A new device in order to stop a cutting chain was developed and adapted to an electric chainsaw. The device is based on a Wiimote controller (Nintendo™), including two accelerometers and two gyroscopes for detecting rotation and inclination. A Bluetooth wireless technology is used to transfer data to a portable computer. The data collected about linear and angular acceleration are filtered by an algorithm, based on the Euclid norm, capable to distinguishing between normal movements and dangerous chainsaw movements. The result show a good answer to device and when happen a dangerous situation an alarm signal is sent back to the implement in order to stop the cutting chain. The device show a correct behavior in tested dangerous situations and is envisaged to extend to combustion engine chainsaws, as well as to other portable equipment used in agriculture and forestry operations and for this objectives were patented.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2013 R. Gubiani, G. Pergher, S.R.S. Cividino, R. Lombardo, F. Blanchini
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.