Back to articles
Volume: 33 | Article ID: art00006
Vision-based Machine Learning Worker Assistance System for People with Disabilities on Low-Cost Hardware
  DOI :  10.2352/ISSN.2470-1173.2021.6.IRIACV-311  Published OnlineJanuary 2021

Working in protected workshops places supervisor workers in a work field with concurrent targets. On the one side, the workers with disabilities require a safe space to meet special requirements and on the other side, customers expect comparable time and quality standards than in the normal industry while maintaining cost pressure. We propose a technical solution to support the supervisors with the quality control. We developed a flexible assistance system for people with disabilities working in protected workshops that is based on a Raspberry Pi4 and uses cameras for perception. It is appliable for packaging and picking processes and is supported by additional step by step guidance to reach as many protected workshops as possible. The system tries to support supervisors in quality control and provide information if any action is required to free time for interpersonal matters. An automatic pick-by-light system is included which uses hand recognition. To ensure good speed we used image processing and verified the detections with a machine learning approach for robustness against lighting conditions. In this paper we present the system, which is available open source, itself with its features and the development of the machine learning algorithm.

Subject Areas :
Views 48
Downloads 14
 articleview.views 48
 articleview.downloads 14
  Cite this article 

Micha Christ, Christian Jauch, Julia Denecke, Saskia J. Wiedenroth, "Vision-based Machine Learning Worker Assistance System for People with Disabilities on Low-Cost Hardwarein Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision,  2021,  pp 311-1 - 311-7,

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2021
Electronic Imaging
Society for Imaging Science and Technology
IS&T 7003 Kilworth Lane Springfield, VA 22151 USA