Online adaptive neural control of a robotic lower limb prosthesis

J. A. Spanias, A. M. Simon, S. B. Finucane, E. J. Perreault, L. J. Hargrove

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


Objective. The purpose of this study was to develop and evaluate an adaptive intent recognition algorithm that continuously learns to incorporate a lower limb amputee's neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis. Approach. We present a powered lower limb prosthesis that was configured to acquire the user's neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user's intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive intent recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user's model of neural data during ambulation. Main results. Our proposed algorithm accurately and consistently identified the user's intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm - with a 6.66% [3.16%] relative decrease in error rate. Significance. This study demonstrates that our adaptive intent recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user's intent with low error rates.

Original languageEnglish (US)
Article number016015
JournalJournal of Neural Engineering
Issue number1
StatePublished - Feb 2018


  • electromyography
  • lower limb prostheses
  • machine learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Cellular and Molecular Neuroscience

Fingerprint Dive into the research topics of 'Online adaptive neural control of a robotic lower limb prosthesis'. Together they form a unique fingerprint.

Cite this