Any human-computer interface requires both a means of transducing information flowing from the person and a way of classifying this information in a form that can be used by an application program. Since several interface devices exploit the head movements of disabled people to control computers, this paper includes a discussion of existing technologies based on head movements. As an alternative to simple techniques based on pointing to classify this information, this paper studies the possibility of using a combination of pointing and movement gestures to control an application program. By using hidden Markov models to classify movements into 'yes', 'no' and spurious gestures, it was possible to control a simple graphics application program. Subsequent analysis showed that the hidden Markov models achieved a 74\% success rate.
%0 Journal Article
%1 Harwin1990
%A Harwin, W. S.
%A Jackson, R. D.
%D 1990
%J J Biomed Eng
%K Adult; Assisted; Cerebral Palsy; Communication Aids for Disabled; Computer Graphics; Female; Gestures; Head; Humans; Kinesics; Markov Chains; Observer Variation; Self-Help Devices; Signal Processing, Computer-; Software; Transducers; User-Computer Interface
%N 3
%P 193--198
%T Analysis of intentional head gestures to assist computer access by physically disabled people.
%V 12
%X Any human-computer interface requires both a means of transducing information flowing from the person and a way of classifying this information in a form that can be used by an application program. Since several interface devices exploit the head movements of disabled people to control computers, this paper includes a discussion of existing technologies based on head movements. As an alternative to simple techniques based on pointing to classify this information, this paper studies the possibility of using a combination of pointing and movement gestures to control an application program. By using hidden Markov models to classify movements into 'yes', 'no' and spurious gestures, it was possible to control a simple graphics application program. Subsequent analysis showed that the hidden Markov models achieved a 74\% success rate.
@article{Harwin1990,
abstract = {Any human-computer interface requires both a means of transducing information flowing from the person and a way of classifying this information in a form that can be used by an application program. Since several interface devices exploit the head movements of disabled people to control computers, this paper includes a discussion of existing technologies based on head movements. As an alternative to simple techniques based on pointing to classify this information, this paper studies the possibility of using a combination of pointing and movement gestures to control an application program. By using hidden Markov models to classify movements into 'yes', 'no' and spurious gestures, it was possible to control a simple graphics application program. Subsequent analysis showed that the hidden Markov models achieved a 74\% success rate.},
added-at = {2014-07-19T20:25:34.000+0200},
author = {Harwin, W. S. and Jackson, R. D.},
biburl = {https://www.bibsonomy.org/bibtex/2defb5cfdc23770be111df3564196e588/ar0berts},
groups = {public},
interhash = {507c029bee0b42b40b6df567edc25454},
intrahash = {defb5cfdc23770be111df3564196e588},
journal = {J Biomed Eng},
keywords = {Adult; Assisted; Cerebral Palsy; Communication Aids for Disabled; Computer Graphics; Female; Gestures; Head; Humans; Kinesics; Markov Chains; Observer Variation; Self-Help Devices; Signal Processing, Computer-; Software; Transducers; User-Computer Interface},
month = May,
number = 3,
pages = {193--198},
pmid = {2140869},
timestamp = {2014-07-19T20:25:34.000+0200},
title = {Analysis of intentional head gestures to assist computer access by physically disabled people.},
username = {ar0berts},
volume = 12,
year = 1990
}