Literature DB >> 25879962

Development of an Environment-Aware Locomotion Mode Recognition System for Powered Lower Limb Prostheses.

Ming Liu, Ding Wang, He Helen Huang.   

Abstract

This paper aimed to develop and evaluate an environment-aware locomotion mode recognition system for volitional control of powered artificial legs. A portable terrain recognition (TR) module, consisting of an inertia measurement unit and a laser distance meter, was built to identify the type of terrain in front of the wearer while walking. A decision tree was used to classify the terrain types and provide either coarse or refined information about the walking environment. Then, the obtained environmental information was modeled as a priori probability and was integrated with a neuromuscular-mechanical-fusion-based locomotion mode (LM) recognition system. The designed TR module and environmental-aware LM recognition system was evaluated separately on able-bodied subjects and a transfemoral amputee online. The results showed that the TR module provided high quality environmental information: TR accuracy is above 98% and terrain transitions are detected over 500 ms before the time required to switch the prosthesis control mode. This enabled smooth locomotion mode transitions for the wearers. The obtained environmental information further improved the performance of LM recognition system, regardless of whether coarse or refined information was used. In addition, the environment-aware LM recognition system produced reliable online performance when the TR output was relatively noisy, which indicated the potential of this system to operate in unconstructed environment. This paper demonstrated that environmental information should be considered for operating wearable lower limb robotic devices, such as prosthetics and orthotics.

Mesh:

Year:  2015        PMID: 25879962     DOI: 10.1109/TNSRE.2015.2420539

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  15 in total

1.  Delaying Ambulation Mode Transition Decisions Improves Accuracy of a Flexible Control System for Powered Knee-Ankle Prosthesis.

Authors:  Ann M Simon; Kimberly A Ingraham; John A Spanias; Aaron J Young; Suzanne B Finucane; Elizabeth G Halsne; Levi J Hargrove
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2016-09-22       Impact factor: 3.802

2.  Stand-Up, Squat, Lunge, and Walk With a Robotic Knee and Ankle Prosthesis Under Shared Neural Control.

Authors:  Grace Hunt; Sarah Hood; Tommaso Lenzi
Journal:  IEEE Open J Eng Med Biol       Date:  2021-08-11

3.  Powered knee and ankle prosthesis with indirect volitional swing control enables level-ground walking and crossing over obstacles.

Authors:  Joel Mendez; Sarah Hood; Andy Gunnel; Tommaso Lenzi
Journal:  Sci Robot       Date:  2020-07-22

4.  Investigation of Timing to Switch Control Mode in Powered Knee Prostheses during Task Transitions.

Authors:  Fan Zhang; Ming Liu; He Huang
Journal:  PLoS One       Date:  2015-07-21       Impact factor: 3.240

5.  Whole Body Awareness for Controlling a Robotic Transfemoral Prosthesis.

Authors:  Andrea Parri; Elena Martini; Joost Geeroms; Louis Flynn; Guido Pasquini; Simona Crea; Raffaele Molino Lova; Dirk Lefeber; Roman Kamnik; Marko Munih; Nicola Vitiello
Journal:  Front Neurorobot       Date:  2017-05-30       Impact factor: 2.650

Review 6.  Relying on more sense for enhancing lower limb prostheses control: a review.

Authors:  Michael Tschiedel; Michael Friedrich Russold; Eugenijus Kaniusas
Journal:  J Neuroeng Rehabil       Date:  2020-07-17       Impact factor: 4.262

7.  Terrain Feature Estimation Method for a Lower Limb Exoskeleton Using Kinematic Analysis and Center of Pressure.

Authors:  Myounghoon Shim; Jong In Han; Ho Seon Choi; Seong Min Ha; Jung-Hoon Kim; Yoon Su Baek
Journal:  Sensors (Basel)       Date:  2019-10-12       Impact factor: 3.576

Review 8.  Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices-A Systematic Review.

Authors:  Floriant Labarrière; Elizabeth Thomas; Laurine Calistri; Virgil Optasanu; Mathieu Gueugnon; Paul Ornetti; Davy Laroche
Journal:  Sensors (Basel)       Date:  2020-11-06       Impact factor: 3.576

Review 9.  A Survey of Teleceptive Sensing for Wearable Assistive Robotic Devices.

Authors:  Nili E Krausz; Levi J Hargrove
Journal:  Sensors (Basel)       Date:  2019-11-28       Impact factor: 3.576

10.  Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices.

Authors:  Nili E Krausz; Blair H Hu; Levi J Hargrove
Journal:  Sensors (Basel)       Date:  2019-11-08       Impact factor: 3.576

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