Improving Learning Tasks for Mentally Handicapped People Using AmI Environments Based on Cyber-Physical Systems

Abstract

A prototype to improve learning tasks for mentally handicapped people is shown in this research paper using ambient intelligence techniques and based on cyber-physical systems. e whole system is composed of a worktable, a cyber-glove (both with several RFID and NFC detection zones), and an AmI software application for modeling and workflow guidance. A case study was carried out by the authors where sixteen mentally handicapped people and 3 trainers were involved in the experiment. e experiment consisted in the execution of several memorization tasks of movements of objects using the approach presented in this paper. e results obtained were very interesting, indicating that this kind of solutions are feasible and allow the learning of complex tasks to some types of mentally handicapped people. In addition, at the end of the paper are presented some lessons learned after performing the experimentation.

Improving Learning Tasks for Mentally Handicapped People Using AmI Environments Based on Cyber-Physical Systems

“…is research paper is an extended version of our previous work [6] presented in 10th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2016). Our approach is called LAoCA, and it is composed mainly of three elements: a worktable, a cyberglobe, and a software for modeling and executing workflows.…”

“…is is the case, for example, of the applications involving people with special needs. is paper is an extended version of our previous work [6], which proposed an enhanced ambient intelligence (AmI) system based on cyber-physical systems for tracking the tasks being performed by people who are mentally handicapped during their learning process. e CPS presented in this research work include a cyberglove, a worktable, and an AmI software for creating and controlling workflows, whose tasks involve actions about the cyber-glove and the worktable.…”

Recognizing human activities in Industry 4.0 scenarios through an analysis-modeling- recognition algorithm and context labels

“…Thus, for these craft industrial scenarios, heterogenous pervasive sensing platforms are investigated as a possible valid alternative [9,11]. In these platforms, although cameras may be included, we typically find low-cost sensors such as accelerometers and RFID tags and readers integrated into wearables, Bluetooth beacon devices for indoor positioning, or passive infrared sensors to control the workers movement [12,13]. In those scenarios, besides, the number of sensing nodes is huge [14].…”