Robust and Adaptive Lower Limb Prosthesis Stance Control via Extended Kalman Filter-Based Gait Phase Estimation

Thatte, Nitish; Shah, Tanvi; Geyer, Hartmut

doi:10.1109/lra.2019.2924841

Cited by 63 publications

(45 citation statements)

References 20 publications

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“…The first method is to calculate the average duration of several previous gait cycles as the denominator and then calculate the time percent (i.e., gait phase, from 0 to 100%) relative to the average duration in each gait cycle. The second method is designing or utilizing a specific algorithm to estimate the continuous gait phase, such as adaptive oscillator [113] [17]. The third method is based on the polar angle method [114].…”

Section: Continuous Gait Phase Estimationmentioning

confidence: 99%

“…The human's intent recognition, including locomotion mode recognition, gait event detection, and continuous gait phase estimation, is the necessary prerequisite to set a control strategy. Thus, it has attracted a multitude of groups to conduct related studies and got some good results [11][12][13][14][15][16][17][18]. Several research groups have given reviews about the human's intent recognition for a control perspective [19,20].…”

Section: Introductionmentioning

confidence: 99%

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Noninvasive Human-Prosthesis Interfaces for Locomotion Intent Recognition: A Review

Wang

2021

Cyborg Bionic Syst

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The lower-limb robotic prostheses can provide assistance for amputees’ daily activities by restoring the biomechanical functions of missing limb(s). To set proper control strategies and develop the corresponding controller for robotic prosthesis, a prosthesis user’s intent must be acquired in time, which is still a major challenge and has attracted intensive attentions. This work focuses on the robotic prosthesis user’s locomotion intent recognition based on the noninvasive sensing methods from the recognition task perspective (locomotion mode recognition, gait event detection, and continuous gait phase estimation) and reviews the state-of-the-art intent recognition techniques in a lower-limb prosthesis scope. The current research status, including recognition approach, progress, challenges, and future prospects in the human’s intent recognition, has been reviewed. In particular for the recognition approach, the paper analyzes the recent studies and discusses the role of each element in locomotion intent recognition. This work summarizes the existing research results and problems and contributes a general framework for the intent recognition based on lower-limb prosthesis.

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Section: Continuous Gait Phase Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Noninvasive Human-Prosthesis Interfaces for Locomotion Intent Recognition: A Review

Wang

2021

Cyborg Bionic Syst

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“…In contrast, real-time estimates of the continuous phase of gait allow the prosthetic leg to track normative joint kinematic patterns in synchrony with the user's motion [4]- [7]. Researchers have used different methodologies (e.g., central pattern generators [8]- [10], oscillators [11]- [13], machine learning [14], extended Kalman filters [15], and phase variables [4]- [7]) to calculate the phase of the gait cycle in a continuous manner. A phase variable is a mechanical signal that increases monotonically with a steady gait cycle and thus can describe the position of a person's kinematics in the gait cycle at all times [5], [16].…”

Section: Introductionmentioning

confidence: 99%

Parameterizing Human Locomotion Across Quasi-Random Treadmill Perturbations and Inclines

Macaluso

Embry

Villarreal

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Previous work has shown that it is possible to use a mechanical phase variable to accurately quantify the progression through a human gait cycle, even in the presence of disturbances. However, mechanical phase variables are highly dependent on the behavior of the body segment from which they are measured, which can change with the human's task or in response to different disturbances. In this study, we compare kinematic parameterization methods based on time, thigh phase angle, and tibia phase angle with motion capture data obtained from ten able-bodied subjects walking at three inclines while experiencing phase-shifting perturbations from a split-belt instrumented treadmill. The belt, direction, and timings of perturbations were quasi-randomly selected to prevent anticipatory action by the subjects and sample different types of perturbations. Statistical analysis revealed that both phase parameterization methods are superior to time parameterization, with thigh phase angle also being superior to tibia phase angle in most cases.

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“…The neuromuscular controller uses models of muscle dynamics and hypothesized reflexes, but it brings many parameters which are difficult to tune. Another alternative control method is based on the estimation of continuous gait phase [11]. Quintero et al have used an adaptive Kalman filter based on Newton's and Euler's equations of motion to compute real-time Euler angles to conduct continuous-phase control of a powered knee-ankle prosthesis and has achieved some effects [12].…”

Section: Introductionmentioning

confidence: 99%

Online Estimation of Continuous Gait Phase for Robotic Transtibial Prostheses Based on Adaptive Oscillators

Crea

Vitiello

et al. 2020

2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

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Continuous gait phase plays an important role in wearable robot control. This study focuses on the online estimation of continuous gait phase based on robotic transtibial prosthesis signals. First, we adopt the prosthetic foot deformation information to detect the heel strike as the start timing (reset 0 rad) of one gait cycle. Then we conduct the gait phase estimation based on adaptive oscillators using the prosthetic shank angle signal as input. Three transtibial amputees were recruited in this study and they walked on the treadmill at different speeds (slow, normal and fast) and on different ramps (10 • , 5 • , 0 • , -5 • and -10 • ) in the experiment. The root-meansquare error between online estimation result and ground truth gait phase is calculated. The maximum and minimum errors are 0.147 rad and 0.058 rad, and the corresponding ratios in one gait cycle are 2.34% and 0.92%. This study achieves good performance and provides an effective method to estimate the continuous gait phase, which will instruct robotic transtibial prosthesis control.

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Robust and Adaptive Lower Limb Prosthesis Stance Control via Extended Kalman Filter-Based Gait Phase Estimation

Cited by 63 publications

References 20 publications

Noninvasive Human-Prosthesis Interfaces for Locomotion Intent Recognition: A Review

Noninvasive Human-Prosthesis Interfaces for Locomotion Intent Recognition: A Review

Parameterizing Human Locomotion Across Quasi-Random Treadmill Perturbations and Inclines

Online Estimation of Continuous Gait Phase for Robotic Transtibial Prostheses Based on Adaptive Oscillators

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