A System for Multimodal Assistive Domotics and Augmentative and Alternative Communication

In this paper, we present an new assistive system called SMAD (System for Multimodal Assistive Domotics). Through this SMAD, a user with motor disabilities is able to control home devices from a wheelchair by means of biological signals captured on muscles (sEMG) and eyes (EOG/VOG). Our SMAD focuses on the following end-user features: ease of setup and use, low cost and customizability. The functional design of the interface has been driven by a user-centered performance analysis. Moreover, the system has been conceived to be adaptable to the user's degree of disability. The physical communication between wheelchair and devices on the environment employs radio-frequency (RF), infrared (IR) and Bluetooth (BT). Two scenarios are presented, using respectively facial gestures and eyes movements through Emotiv EPOC and Eye Tribe Tracker. Experimental results show fulfilling performance for the first scenario. The second scenario is an evolution of the first one, in which preliminary results indicate greater usability of the control interface; however the system must undergo testing by a larger number of people for better accuracy performance. During offline and online sessions, users succeeded in employing our SMAD to operate home devices, simulating common needs of people with disabilities, with accuracy above 90% and information transmission rate of up to 53 bits/min. The final goal of our SMAD is to provide the end-user with a practical daily assistive platform, as close as possible to a product that works out of the box.

[1]  Melanie Fried-Oken,et al.  Access Interface Strategies , 2012, Assistive technology : the official journal of RESNA.

[2]  Miad Faezipour,et al.  Eye Tracking and Head Movement Detection: A State-of-Art Survey , 2013, IEEE Journal of Translational Engineering in Health and Medicine.

[3]  Li-Yeh Chuang,et al.  A mobile communication aid system for persons with physical disabilities , 2008, Math. Comput. Model..

[4]  Hani Hagras,et al.  A neural network agent based approach to activity detection in AmI environments , 2005 .

[5]  Albert M. Cook,et al.  Assistive Technologies: Principles and Practice , 1995 .

[6]  Laura Farinetti,et al.  Eye Tracking Impact on Quality-of-Life of ALS Patients , 2008, ICCHP.

[7]  Dinesh Kumar,et al.  Devices for Mobility and Manipulation for People with Reduced Abilities , 2014 .

[8]  C. Shewan,et al.  Augmentative and Alternative Communication , 2020, Encyclopedia of Education and Information Technologies.

[9]  Michael Gleeson,et al.  Project Domus: Designing Effective Smart Home Systems , 2009 .

[10]  Luca Mainardi,et al.  Performance measurement for brain–computer or brain–machine interfaces: a tutorial , 2014, Journal of neural engineering.

[11]  Eva Cerezo,et al.  AraBoard: A Multiplatform Alternative and Augmentative Communication Tool , 2013, DSAI.

[12]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[13]  Naohiro Ishii,et al.  Augmentative and alternative communication with digital assistant for autistic children , 2012, 2012 IEEE International Conference on Emerging Signal Processing Applications.

[14]  A. Bujnowski,et al.  Eye mouse for disabled , 2008, 2008 Conference on Human System Interactions.

[15]  Tai-hoon Kim,et al.  Applications, Systems and Methods in Smart Home Technology: A Review , 2010 .

[16]  J. Lou,et al.  Purposes of AAC device use for persons with ALS as reported by caregivers , 2006, Augmentative and alternative communication.

[17]  Janice Light,et al.  Interaction involving individuals using augmentative and alternative communication systems: State of the art and future directions , 1988 .

[18]  Florina Ungureanu,et al.  Eye tracking based communication system for patient with major neoro-locomotor disabilites , 2011, 15th International Conference on System Theory, Control and Computing.