Signal Processing Methods for Improvement of Environmental Perception of Persons with Deafblindness

Environmental perception is a functional area that is severely limited in persons with deafblindness (DB) who belong a category of people with severe disabilities. Monitor is a vibratory aid developed with the aim to improve environmental perception of persons with DB. The aid consists of a mobile phone with an application connected to a microphone and vibrator. Monitor picks up the sounds produced by events by microphone, processes the sound using an algorithm programmed as an application in the mobile phone and then presents the signal via the vibrator to the persons with DB to be sensed and interpreted. In previous laboratory studies, four algorithms (AM, AMMC, TR, and TRHA) were developed based on modulating, and transposing principles. The algorithms were tested by persons with normal hearing/hearing impairment and selected as good candidates to improve vibratory identification of environmental sounds. In this on-going the algorithms are tested by 13 persons with congenital D and five persons with DB using Monitor in a realistic environment, living room, kitchen or office. Forty five recorded environmental sounds were used as test stimuli. The subjects tested the algorithms two times, Test and Retest each including a test session initiated by a training session. The four algorithms were tested in four days at Test and four days at Retest in total eight test days. Each test day began with a training session where a sound was presented as vibrations to be sensed by the person with the aim to remember its pattern and identity. The 45 sounds were grouped in four groups where an specific algorithm was chosen to process an specific sound group in a specific day. At the test session a sound was presented and the person was given 5 randomly chosen sound alternatives to choose the one as represented sound. The algorithms were different for different sound groups during four different test days so all algorithms were used to process all sounds. The algorithms were tested a second time, Retest, in same way as in Test. The mean value of identification of environmental sounds varied between 74.6% and 84.0% at Test and between 86.9% and 90.4% at Retest. The identification results at Retest were significantly improved (p<0.01) for all algorithms after a relatively short time of training indicating a good learning effect. At Test the algorithm AM was significantly better than the algorithms AMMC and TRHA (p< 0.01) and the algorithm TR was better than TRHA (p<0.01). The algorithms AM, AMMC, and TR were selected as good candidates to be implemented in the Monitor to improve environmental perception.