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Research Papers

Synergy-Based Gaussian Process Estimation of Ankle Angle and Torque: Conceptualization for High Level Controlling of Active Robotic Foot Prostheses/Orthoses

[+] Author and Article Information
Mahdy Eslamy

Advanced Service Robots (ASR) Laboratory,
Department of Mechatronics Engineering,
Faculty of New Sciences and Technologies,
University of Tehran,
P.O. Box 1439957131,
Tehran 1439957131, Iran

Khalil Alipour

Advanced Service Robots (ASR) Laboratory,
Department of Mechatronics Engineering,
Faculty of New Sciences and Technologies,
University of Tehran,
P.O. Box 1439957131,
Tehran 1439957131, Iran
e-mail: k.alipour@ut.ac.ir

1Corresponding author.

Manuscript received March 26, 2018; final manuscript received September 27, 2018; published online November 29, 2018. Assoc. Editor: Guy M. Genin.

J Biomech Eng 141(2), 021002 (Nov 29, 2018) (9 pages) Paper No: BIO-18-1133; doi: 10.1115/1.4041767 History: Received March 26, 2018; Revised September 27, 2018

Human gait is the result of a complex and fascinating cooperation between different joints and segments in the lower extremity. This study aims at investigating the existence of this cooperation or the so-called synergy between the shank motion and the ankle motion. One potential use of this synergy is to develop the high level controllers for active foot prostheses/orthoses. The central point in this paper is to develop a high level controller that is able to continuously map shank kinematics (inputs) to ankle angles and torques (outputs). At the same time, it does not require speed determination, gait percent identification, switching rules, and look-up tables. Furthermore, having those targets in mind, an important part of this study is to determine which input type is required to achieve such targets. This should be fulfilled through using minimum number of inputs. To do this, the Gaussian process (GP) regression has been used to estimate the ankle angles and torques for 11 subjects at three walking speeds (0.5, 1, and 1.5 m/s) based on the shank angular velocity and angle. The results show that it is possible to estimate ankle motion based on the shank motion. It was found that the estimation achieved less quality with only shank angular velocity or angle, whereas the aggregated angular velocity and angle resulted in much higher output estimation quality. In addition, the estimation quality was acceptable for the speeds that there was a training procedure before and when it was tested for the untrained speeds, the estimation quality was not as acceptable as before. The pros and cons of the proposed method are investigated at different scenarios.

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Figures

Grahic Jump Location
Fig. 1

The overall control structure for an active foot prosthesis. A part of the acquired sensory information (X) is used by the high level controller (section a) to estimate ankle angles and torques (or through the geometry of the active foot prosthesis and the spring stiffness, the desired motor positions are estimated). Furthermore, some part of the sensory data (e.g., motor position and/or velocity) is used to create the required command signal through the motor controller (section b) to actuate it. Note that different actuation mechanisms could be used for the active foot prostheses [23,24].

Grahic Jump Location
Fig. 2

The diagrams of (a) shank angle (θsh), (b) shank angular velocity (θ˙sh), (c) ankle angle θa, and (d) ankle torque (Ta in [Nm/(BodyWeight)]) for 0.5, 1 and 1.5 m/s walking. The graphs are the mean of 21 subjects for each speed [38].

Grahic Jump Location
Fig. 3

(related to Sects. 3.1 and 3.2) (a) Related to ankle angle and (b) related to ankle torque. For each speed, the dotted black lines and squares are related to training with θ˙sh, the gray solid lines and squares are related to training with θsh and the solid black lines and squares are related to training with [θ˙sh,θsh]. The squares show the mean (of the eleven test subjects) and the lower and upper bounds show the minimum and maximum for each parameter for walking 0.5, 1, 1.5 m/s.

Grahic Jump Location
Fig. 4

(related to Sect. 3.1) The diagrams of ankle angle with respect to (a) θ˙sh, (b) [θsh], and (c) [θ˙sh,θsh] (diagrams for speed of 0.5 m/s, subject number 11)

Grahic Jump Location
Fig. 5

(related to Sect. 3.2) The estimated and expected ankle angles and torques for two different subjects, input: [θ˙sh,θsh], for three speeds from left to right: walking 0.5, 1, 1.5 m/s. See also Fig. 4 and Table 2.

Grahic Jump Location
Fig. 6

(related to Sect. 3.3) (a) Related to ankle angle and (b) related to ankle torque, training with [θ˙sh,θsh]. For each speed, the solid black lines and squares are related to full training with data from 0.5, 1, and 1.5 m/s and the gray dotted lines and squares are related to training only with data from 0.5 m/s. For 1 and 1.5 m/s, the results for the untrained scenario are shown in comparison to the fully trained scenario. The squares show the mean (of the eleven test subjects) and the lower and upper bounds show the minimum and maximum for each parameter for walking 1 and 1.5 m/s.

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