This paper builds on prior investigations of the electromyogram (EMG) control of a single degree-of-freedom (DOF) transfemoral prosthetic limb, but augmented with mechanical haptic feedback of prosthetic limb state. Preliminary studies were conducted where quasi-static and vibratory cutaneous haptic feedback was provided to subjects performing nonweight-bearing motion tracking tasks with the EMG controlled transfemoral prosthesis. The results of these studies showed that the subjects exhibited improved tracking performance when following pseudo-random step commands under EMG control augmented with static and vibratory haptic feedback cues. The work to be discussed in this paper augments the EMG control architecture to foster improved co-contraction of the instrumented antagonist muscle pair. Using the modified EMG control architecture, experimental studies were conducted to investigate the efficacy of two haptic feedback modalities in conveying information pertinent to single-DOF nonweight-bearing sinusoidal motion tracking tasks. The two haptic feedback modalities investigated were quasi-static pressure feedback provided with pneumatic actuation and vibratory feedback provided by a vibrotactile motor array. Able-bodied test subjects were asked to control the prosthetic knee to follow sinusoidal trajectories with and without visual and haptic feedback. Experimental results show that EMG-control performance in tracking sinusoidal trajectories significantly improves in visually devoid environments when haptic feedback in the form of error-based and pacemaking stimulation patterns are presented to the user.
- Dynamic Systems and Control Division
The Effects of Cutaneous Haptic Feedback on EMG-Based Motion Control of a Transfemoral Prosthesis
- Views Icon Views
- Share Icon Share
- Search Site
Canino, JM, & Fite, KB. "The Effects of Cutaneous Haptic Feedback on EMG-Based Motion Control of a Transfemoral Prosthesis." Proceedings of the ASME 2016 Dynamic Systems and Control Conference. Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation. Minneapolis, Minnesota, USA. October 12–14, 2016. V001T06A004. ASME. https://doi.org/10.1115/DSCC2016-9778
Download citation file: