Human Locomotion Assistance using Two-Dimensional Features Based Adaptive Oscillator

Prudhvi Tej Chinimilli, Susheelkumar C. Subramanian, Sangram Redkar, Thomas Sugar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, an adaptive oscillator method Amplitude Omega Adaptive Oscillator (AωAO), is proposed to provide bilateral hip assistance for human locomotion. A realtime human locomotion recognition algorithm is integrated with AωAO to make it robust for various gait activities. The human locomotion recognition algorithm comprises both low-level (to detect activities) and high-level classifiers to detect transitions between activities. The Support Vector Machine (SVM) and Discrete Hidden Markov Model (DHMM) are used as low-level and high-level classifiers respectively. The human locomotion recognition algorithm is trained using two-dimensional features, Amplitude (A) and Omega (ω), obtained from thigh angle measurements, using a single Inertial Measurement Unit (IMU) on each limb. In AωAO, a pool with four adaptive oscillators (AOs) is used to estimate the filtered thigh angle trajectory. This pool converges to the frequency and phase of the signal, adaptively. To account for amplitude convergence, the amplitude parameters of the oscillator need to be reinitialized based on the human activity, identified by the human locomotion recognition algorithm. In addition to the adaptive oscillators, a Gaussian kernel function based nonlinear filter is employed to predict the future estimates of thigh angles. These predicted estimates, along with the user thigh angles, are used to calculate hip assistive torque in real-time. To verify the efficacy of the proposed approach, experiments were performed, using Hip exoskeleton for Superior Assistance (HeSA), on three healthy subjects. The human locomotion recognition algorithm reported higher classification and prediction accuracy of 95.2% and 94.9 % respectively. Activity Classification, Assistive devices, Human Activity Recognition.

Original languageEnglish (US)
Title of host publication2019 Wearable Robotics Association Conference, WearRAcon 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages92-98
Number of pages7
ISBN (Electronic)9781538680568
DOIs
StatePublished - May 21 2019
Event2019 Wearable Robotics Association Conference, WearRAcon 2019 - Scottsdale, United States
Duration: Mar 25 2019Mar 27 2019

Publication series

Name2019 Wearable Robotics Association Conference, WearRAcon 2019

Conference

Conference2019 Wearable Robotics Association Conference, WearRAcon 2019
CountryUnited States
CityScottsdale
Period3/25/193/27/19

Fingerprint

Locomotion
assistance
Recognition Algorithm
Angle
Classifiers
Units of measurement
Hidden Markov models
Angle measurement
Classifier
Support vector machines
Estimate
Real-time
Torque
Activity Recognition
Gaussian Kernel
Nonlinear Filters
Trajectories
Human
Gaussian Function
Gait

Keywords

  • Activity Classification
  • Assistive devices
  • Human Activity Recognition

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Control and Optimization
  • Human Factors and Ergonomics

Cite this

Chinimilli, P. T., Subramanian, S. C., Redkar, S., & Sugar, T. (2019). Human Locomotion Assistance using Two-Dimensional Features Based Adaptive Oscillator. In 2019 Wearable Robotics Association Conference, WearRAcon 2019 (pp. 92-98). [8719628] (2019 Wearable Robotics Association Conference, WearRAcon 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WEARRACON.2019.8719628

Human Locomotion Assistance using Two-Dimensional Features Based Adaptive Oscillator. / Chinimilli, Prudhvi Tej; Subramanian, Susheelkumar C.; Redkar, Sangram; Sugar, Thomas.

2019 Wearable Robotics Association Conference, WearRAcon 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 92-98 8719628 (2019 Wearable Robotics Association Conference, WearRAcon 2019).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chinimilli, PT, Subramanian, SC, Redkar, S & Sugar, T 2019, Human Locomotion Assistance using Two-Dimensional Features Based Adaptive Oscillator. in 2019 Wearable Robotics Association Conference, WearRAcon 2019., 8719628, 2019 Wearable Robotics Association Conference, WearRAcon 2019, Institute of Electrical and Electronics Engineers Inc., pp. 92-98, 2019 Wearable Robotics Association Conference, WearRAcon 2019, Scottsdale, United States, 3/25/19. https://doi.org/10.1109/WEARRACON.2019.8719628
Chinimilli PT, Subramanian SC, Redkar S, Sugar T. Human Locomotion Assistance using Two-Dimensional Features Based Adaptive Oscillator. In 2019 Wearable Robotics Association Conference, WearRAcon 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 92-98. 8719628. (2019 Wearable Robotics Association Conference, WearRAcon 2019). https://doi.org/10.1109/WEARRACON.2019.8719628
Chinimilli, Prudhvi Tej ; Subramanian, Susheelkumar C. ; Redkar, Sangram ; Sugar, Thomas. / Human Locomotion Assistance using Two-Dimensional Features Based Adaptive Oscillator. 2019 Wearable Robotics Association Conference, WearRAcon 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 92-98 (2019 Wearable Robotics Association Conference, WearRAcon 2019).
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