Monitoring of Upper Limb Prosthesis Activity in Trans-Radial Amputees

  • Mohammad Sobuh
  • Laurence Kenney
  • Phil Tresadern
  • Martin Twiste
  • Sibylle Thies


There has been a shift in rehabilitation medicine from conventional evaluation procedures towards more quantitative approaches. However, up to now, a quantitative evaluation procedure for upper limb prostheses that is applicable outside of the laboratory or clinical environment has not been established. The requirement for such a procedure arises from the findings of a number of recent studies suggesting that unilateral trans-radial amputees do not involve their prosthesis in task performance in real life situations, even if they are able to demonstrate the use of the prosthesis in the clinical environment. This suggests that laboratory, or clinic-based assessments are limited in the information they provide to clinicians or designers of new prostheses. Further, self-report approaches, such as questionnaires or interviews rely on accurate recall and reporting by subjects, an approach that has been shown to be flawed in other rehabilitation and public health domains.

Therefore, this chapter reports a study investigating the feasibility of quantifying the nature and duration of tasks performed with a myoelectric prosthesis by means of an activity monitor. It was hypothesised that by monitoring the prosthesis hand opening and closing it may be possible to identify the manipulation phase. Such information could be used to segment acceleration signals, measured from arm-located accelerometers, which may contain information characterising the task(s) being performed and differentiate it/them from other tasks. The results of this study indicate that, by using a neural network classifier, customised for each user, acceleration signals measured during the manipulation phase of task performance could accurately characterise the task being performed. The implications of these findings and future work are discussed here.


Classification Accuracy Activity Monitoring Acceleration Data Hand Opening Residual Limb 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors gratefully acknowledge the financial support from the University of Jordan and thank the participants in the study. The authors also acknowledge colleagues at the University of Strathclyde for their assistance with figures 4.2– 4.4.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Mohammad Sobuh
  • Laurence Kenney
    • 1
  • Phil Tresadern
  • Martin Twiste
  • Sibylle Thies
  1. 1.Centre for Rehabilitation and Human Performance Research, Brian Blatchford buildingUniversity of SalfordSalfordUK

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