{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/82420"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/82420","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Pattern classification of terrain during amputee walking","abstract":"In this thesis I study the role of extrinsic (sensors placed on the body) versus intrinsic sensing (instruments placed on an artificial limb) and determine a robust set of sensors from physical and reliability constraints for a terrain adaptation in a robotic ankle prosthesis. Further, during this thesis I collect a novel data-set that contains seven able-bodied participants walking over 19 terrain transitions and 7 non-amputees walking over 9 transitions, forming the largest collection of transitions to date using an exhaustive set of sensors: inertial measurement units, gyroscopes, kinematics from motion capture, and electromyography from 16 sites on the lower limbs for non-amputee subjects and 9 sites or amputee subjects. This work extends previous work [3] by using more conditions, a larger subject group, and more sensors on amputees, and includes non-amputees.I present a novel machine learning algorithm that uses sensor data during rapid transitions from pre-foothold to just prior to post-foothold to predict different terrain boundaries. This advances the field of biomechatronics, our understanding of terrain adaptation in people both with and without amputations, contributes to the development of a fully terrain adaptive robotic ankle prosthesis, and improves the quality of life for the physically challenged. Specifically we set out to prove between pre and post-foothold the ankle and knee positions calculated using an IMU attached to an amputees powered prosthetic ankle can discriminate with greater than 99% accuracy between 9 conditions. Our results suggest that myography as a non-volitional sensing modality for terrain adaptive prostheses was not needed.","abstract_html":"In this thesis I study the role of extrinsic (sensors placed on the body) versus intrinsic sensing (instruments placed on an artificial limb) and determine a robust set of sensors from physical and reliability constraints for a terrain adaptation in a robotic ankle prosthesis. Further, during this thesis I collect a novel data-set that contains seven able-bodied participants walking over 19 terrain transitions and 7 non-amputees walking over 9 transitions, forming the largest collection of transitions to date using an exhaustive set of sensors: inertial measurement units, gyroscopes, kinematics from motion capture, and electromyography from 16 sites on the lower limbs for non-amputee subjects and 9 sites or amputee subjects. This work extends previous work [3] by using more conditions, a larger subject group, and more sensors on amputees, and includes non-amputees.I present a novel machine learning algorithm that uses sensor data during rapid transitions from pre-foothold to just prior to post-foothold to predict different terrain boundaries. This advances the field of biomechatronics, our understanding of terrain adaptation in people both with and without amputations, contributes to the development of a fully terrain adaptive robotic ankle prosthesis, and improves the quality of life for the physically challenged. Specifically we set out to prove between pre and post-foothold the ankle and knee positions calculated using an IMU attached to an amputees powered prosthetic ankle can discriminate with greater than 99% accuracy between 9 conditions. Our results suggest that myography as a non-volitional sensing modality for terrain adaptive prostheses was not needed.","abstract_has_math":false,"creators":["Farrell, Matthew Todd"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Program in Media Arts and Sciences (Massachusetts Institute of Technology)","school":null,"contributors":[],"advisors":["Hugh Herr."],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013","date_published":"2013","updated_at":"2026-07-22T22:21:50Z","subjects":["Architecture. Program in Media Arts and Sciences."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 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This advances the field of biomechatronics, our understanding of terrain adaptation in people both with and without amputations, contributes to the development of a fully terrain adaptive robotic ankle prosthesis, and improves the quality of life for the physically challenged. Specifically we set out to prove between pre and post-foothold the ankle and knee positions calculated using an IMU attached to an amputees powered prosthetic ankle can discriminate with greater than 99% accuracy between 9 conditions. 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