Real-time embedded frame work for sEMG skeletal muscle force estimation and LQG control algorithms for smart upper extremity prostheses

Chandrasekhar Potluri, Madhavi Anugolu, D. Subbaram Naidu, Marco P. Schoen, Steve C. Chiu

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


This paper presents a real-time embedded framework for finger force control of upper extremity prostheses. The proposed system is based on the hypothesis that models describing the finger force and surface Electromyographic (sEMG) signal relationships of healthy subjects can be applied to amputees. An identification/estimation scheme is applied to the collected sEMG and finger force signals in order to infer dynamical models relating the two. A LQG control law is proposed based on this estimation scheme in order to control finger forces of upper extremity prostheses. For the force estimation, filtered sEMG signals from a sensor array and finger force data of a healthy subject are acquired. Real-time estimation and control are implemented using a PIC32MX360F512L microcontroller. In this paper, a novel fusion technique, the Optimized Linear Model Fusion Algorithm (OLMFA) is developed for estimating the skeletal muscle force from the sEMG sensor array in real-time. The sEMG signal is rectified and filtered using a Half-Gaussian filter, and fed to the OLMFA based Multiple Input Single Output (MISO) force model. This MISO system provides the estimated finger force as reference input to the upper extremity prostheses in real-time. A LQG controller is designed to control the finger force of the prostheses utilizing the force estimate from the OLMFA as a reference. Both the OLMFA and the LQG control scheme are prototyped on the embedded framework for testing of the real-time performance. The proposed embedded framework features rate partitioning and UART interface for performance validation and troubleshooting. The OLMFA based force estimation yields a real-time performance of 85.6% mean correlation and 20.4% mean relative error with a standard deviation of ±1.6 and ±1.5 respectively for 18 test subject's k-fold cross validation data. The LQG control algorithm yields a real-time performance of 91.6% mean correlation and 9.2% mean relative error with a standard deviation of ±1.4 and ±1.3 respectively.

Original languageEnglish (US)
Pages (from-to)67-81
Number of pages15
JournalEngineering Applications of Artificial Intelligence
StatePublished - Nov 2015

Bibliographical note

Funding Information:
This research was sponsored by the US Department of the Army , under the number W81XWH-10-1-0128 , awarded and administered by the U.S. Army Medical Research Acquisition Activity, 820 Chandler Street, Fort Detrick MD 21702-5014. The information does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred. For purposes of this article, information includes news releases, articles, manuscripts, brochures, advertisements, still and motion pictures, speeches, trade association proceedings, etc. Further, the technical help from Dr. Alba Perez, Dr. Haydie Lecorbeiller and Achyut Venkataramu in proof reading the manuscript is greatly appreciated.

Publisher Copyright:
© 2015 Elsevier Ltd.


  • Electromyogram(EMG)
  • Finger force control
  • Half-Gaussian
  • LQG control scheme
  • Real-time embedded framework
  • Sensor data fusion


Dive into the research topics of 'Real-time embedded frame work for sEMG skeletal muscle force estimation and LQG control algorithms for smart upper extremity prostheses'. Together they form a unique fingerprint.

Cite this