%0 Journal Article %1 Harrington1995 %A Harrington, M. E. %A Daniel, R. W. %A Kyberd, P. J. %D 1995 %J Proc Inst Mech Eng H %K Arm; Athetosis; Calibration; Cerebral Palsy; Fore; Gestures; Humans; Models, Biological; Movement; Signal Processing, Computer-Assisted; User-Computer Interface; arm %N 2 %P 129--134 %T A measurement system for the recognition of arm gestures using accelerometers. %V 209 %X This paper describes a strategy to measure arm movements using accelerometers for the computer recognition of arm gestures. Gesture recognition is being investigated as an alternative method of computer input for people with severe speech and motor impairment; the emphasis is on the needs of people with athetoid cerebral palsy who have difficulties with existing computer input devices. An initial model-based approach to estimate the kinematic motion of the arm from acceleration measurements is given, followed by the chosen measurement scheme. The current system considers the forearm as a rigid body and uses two data streams derived from four linear accelerometers. By treating these signals as outputs from postulated mechanical models, the data are reduced to two series of step inputs that are appropriate for pattern classification and recognition. Although this does not in any way model the arm, the concepts used are based on the type of driving signals expected in the control of arm gestures. Initial experimental results show that the information content of gestures is preserved by this data parameterization.

@article{Harrington1995, abstract = {This paper describes a strategy to measure arm movements using accelerometers for the computer recognition of arm gestures. Gesture recognition is being investigated as an alternative method of computer input for people with severe speech and motor impairment; the emphasis is on the needs of people with athetoid cerebral palsy who have difficulties with existing computer input devices. An initial model-based approach to estimate the kinematic motion of the arm from acceleration measurements is given, followed by the chosen measurement scheme. The current system considers the forearm as a rigid body and uses two data streams derived from four linear accelerometers. By treating these signals as outputs from postulated mechanical models, the data are reduced to two series of step inputs that are appropriate for pattern classification and recognition. Although this does not in any way model the arm, the concepts used are based on the type of driving signals expected in the control of arm gestures. Initial experimental results show that the information content of gestures is preserved by this data parameterization.}, added-at = {2014-07-19T20:24:07.000+0200}, author = {Harrington, M. E. and Daniel, R. W. and Kyberd, P. J.}, biburl = {https://www.bibsonomy.org/bibtex/26c330efb2542dcde8da3529d71f7537d/ar0berts}, groups = {public}, interhash = {943cc12b86cd64260a3319f2c29d9c0c}, intrahash = {6c330efb2542dcde8da3529d71f7537d}, journal = {Proc Inst Mech Eng [H]}, keywords = {Arm; Athetosis; Calibration; Cerebral Palsy; Fore; Gestures; Humans; Models, Biological; Movement; Signal Processing, Computer-Assisted; User-Computer Interface; arm}, number = 2, pages = {129--134}, pmid = {7495428}, timestamp = {2014-07-19T20:24:07.000+0200}, title = {A measurement system for the recognition of arm gestures using accelerometers.}, username = {ar0berts}, volume = 209, year = 1995 }