title: Evaluation of an ambient noise insensitive hum-based powered wheelchair controller.
abstract: OBJECTIVE
  A recently-developed assistive technology nicknamed "the Hummer" was investigated as a potential powered wheelchair controller for individuals with severe and multiple disabilities. System performance in a noisy environment was compared to that obtained with a commercial automatic speech recognition (ASR) system.
  
  
  METHODS
  A bi-hum driving protocol was developed to allow the Hummer to serve as a powered wheelchair controller. Participants performed several virtual wheelchair driving tasks of increasing difficulty using the two systems. Custom-written software recorded task execution time, number of commands issued and wall collisions, speed, and trajectory.
  
  
  RESULTS
  The bi-hum protocol was shown to be non-intuitive and required user training. Overall, the Hummer achieved lower performance relative to ASR. Once users became accustomed to the protocol, the difference in performance between the two systems became insignificant, particularly for the higher-difficulty task.
  
  
  CONCLUSIONS
  The Hummer provides a promising new alternative for powered wheelchair control in everyday environments for individuals with severe and multiple disabilities who are able to hum, particularly for those with severe dysarthria which precludes ASR usage. A more intuitive driving protocol is still needed to reduce user frustration and mitigate user-generated errors; recommendations on how this can be achieved are given herein. [Box: see text].
authors.affiliation: Institut National de la Recherche Scientifique, University of Quebec, Montreal, Quebec. tiago.falk@ieee.org
authors.forename: Tiago H
authors.initials: TH
authors.lastname: Falk
authors.forename: Alex
authors.initials: A
authors.lastname: Andrews
authors.forename: Fanny
authors.initials: F
authors.lastname: Hotzé
authors.forename: Eric
authors.initials: E
authors.lastname: Wan
authors.forename: Tom
authors.initials: T
authors.lastname: Chau
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D005260:Female; D006801:Humans; D008297:Male; D009622:Noise; D049250:Speech Recognition Software; D013647:Task Performance and Analysis; D014732:Vibration; D014827:Vocal Cords; D014910:Wheelchairs