Ultrasound Sensing Can Improve Continuous Classification of Discrete Ambulation Modes Compared to Surface Electromyography

Kaitlin G. Rabe, Mohammad Hassan Jahanandish, Jacob R. Boehm, Ann Majewicz Fey, Kenneth Hoyt, Nicholas P. Fey

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Clinical translation of 'intelligent' lower-limb assistive technologies relies on robust control interfaces capable of accurately detecting user intent. To date, mechanical sensors and surface electromyography (EMG) have been the primary sensing modalities used to classify ambulation. Ultrasound (US) imaging can be used to detect user-intent by characterizing structural changes of muscle. Our study evaluates wearable US imaging as a new sensing modality for continuous classification of five discrete ambulation modes: level, incline, decline, stair ascent, and stair descent ambulation, and benchmarks performance relative to EMG sensing. Ten able-bodied subjects were equipped with a wearable US scanner and eight unilateral EMG sensors. Time-intensity features were recorded from US images of three thigh muscles. Features from sliding windows of EMG signals were analyzed in two configurations: one including 5 EMG sensors on muscles around the thigh, and another with 3 additional sensors placed on the shank. Linear discriminate analysis was implemented to continuously classify these phase-dependent features of each sensing modality as one of five ambulation modes. US-based sensing statistically improved mean classification accuracy to 99.8% (99.5-100% CI) compared to 8-EMG sensors (85.8%; 84.0-87.6% CI) and 5-EMG sensors (75.3%; 74.5-76.1% CI). Further, separability analyses show the importance of superficial and deep US information for stair classification relative to other modes. These results are the first to demonstrate the ability of US-based sensing to classify discrete ambulation modes, highlighting the potential for improved assistive device control using less widespread, less superficial and higher resolution sensing of skeletal muscle.

Original languageEnglish (US)
Article number9234746
Pages (from-to)1379-1388
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Issue number4
StatePublished - Apr 2021


  • Assistive technology
  • electromyography
  • ultrasound

ASJC Scopus subject areas

  • Biomedical Engineering


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