1 / 5

Classifying prosthetic use via accelerometry in persons with transtibial amputations

Classifying prosthetic use via accelerometry in persons with transtibial amputations. Morgan T. Redfield, MSEE; John C. Cagle, BSE; Brian J. Hafner, PhD; Joan E. Sanders, PhD. Aim Use 3-axis accelerometer to characterize activities and body postures in transtibial amputation. Relevance

bary
Download Presentation

Classifying prosthetic use via accelerometry in persons with transtibial amputations

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Classifying prosthetic use via accelerometry in persons with transtibial amputations Morgan T. Redfield, MSEE; John C. Cagle, BSE; Brian J. Hafner, PhD; Joan E. Sanders, PhD

  2. Aim • Use 3-axis accelerometer to characterize activities and body postures in transtibial amputation. • Relevance • How persons with amputation use their prostheses over time may facilitate rehabilitation and enhance understanding of prosthesis functionality. • Existing monitoring and classification systems are often limited, record data over short periods, and/or classify limited activities and body postures.

  3. Method • Accelerometers were mounted on prosthetic pylons of 10 persons with transtibial amputation as they performed preset routine of actions. • Accelerometer data was postprocessed with binary decision tree to: • Identify when prosthesis was being worn. • Classify use as movement, standing, or sitting. • Classifications were compared to visual observation by study researchers.

  4. Results • Classifier achieved average accuracy of 96.6%.

  5. Conclusion • Information provided by this system may: • Help clinicians in fitting prostheses, selecting components, or training patients. • Be useful for automatic feedback control to adjust prosthesis mechanisms based on activity and posture.

More Related