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Zhu Z, Liu L, Zhang W, Jiang C, Wang X, Li J. Design and motion control of exoskeleton robot for paralyzed lower limb rehabilitation. Front Neurosci 2024; 18:1355052. [PMID: 38456145 PMCID: PMC10918848 DOI: 10.3389/fnins.2024.1355052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 02/05/2024] [Indexed: 03/09/2024] Open
Abstract
Introduction Patients suffering from limb movement disorders require more complete rehabilitation treatment, and there is a huge demand for rehabilitation exoskeleton robots. Flexible and reliable motion control of exoskeleton robots is very important for patient rehabilitation. Methods This paper proposes a novel exoskeleton robotic system for lower limb rehabilitation. The designed lower limb rehabilitation exoskeleton robot mechanism is mainly composed of the hip joint mechanism, the knee joint mechanism and the ankle joint mechanism. The forces and motion of the exoskeleton robot were analyzed in detail to determine its design parameters. The robot control system was developed to implement closed-loop position control and trajectory planning control of each joint mechanism. Results Multiple experiments and tests were carried out to verify robot's performance and practicality. In the robot angular response experiments, the joint mechanism could quickly adjust to different desired angles, including 15°, 30°, 45°, and 60°. In the trajectory tracking experiments, the exoskeleton robot could complete tracking movements of typical actions such as walking, standing up, sitting down, go upstairs and go downstairs, with a maximum tracking error of ±5°. Robotic wearing tests on normal people were performed to verify the assistive effects of the lower limb rehabilitation exoskeleton at different stages. Discussion The experimental results indicated that the exoskeleton robot has excellent reliability and practicality. The application of this exoskeleton robotic system will help paralyzed patients perform some daily movements and sports.
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Affiliation(s)
- Zhiyong Zhu
- College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China
- School of Mechanical Engineering, Southeast University, Nanjing, China
| | - Lingyan Liu
- School of Mechanical Engineering, Southeast University, Nanjing, China
| | - Wenbin Zhang
- College of Computer Science and Software Engineering, Hohai University, Nanjing, Jiangsu, China
| | - Cong Jiang
- School of Mechanical Engineering, Southeast University, Nanjing, China
| | - Xingsong Wang
- School of Mechanical Engineering, Southeast University, Nanjing, China
| | - Jie Li
- College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China
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Montes-Pérez JA, Thomas GC, Gregg RD. Effects of Personalization on Gait-State Tracking Performance Using Extended Kalman Filters. PROCEEDINGS OF THE ... IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS. IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS 2023; 2023:6068-6074. [PMID: 38130337 PMCID: PMC10732269 DOI: 10.1109/iros55552.2023.10342498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Emerging partial-assistance exoskeletons can enhance able-bodied performance and aid people with pathological gait or age-related immobility. However, every person walks differently, which makes it difficult to directly compute assistance torques from joint kinematics. Gait-state estimation-based controllers use phase (normalized stride time) and task variables (e.g., stride length and ground inclination) to parameterize the joint torques. Using kinematic models that depend on the gait-state, prior work has used an Extended Kalman filter (EKF) to estimate the gait-state online. However, this EKF suffered from kinematic errors since it used a subject-independent measurement model, and it is still unknown how personalization of this measurement model would reduce gait-state tracking error. This paper quantifies how much gait-state tracking improvement a personalized measurement model can have over a subject-independent measurement model when using an EKF-based gait-state estimator. Since the EKF performance depends on the measurement model covariance matrix, we tested on multiple different tuning parameters. Across reasonable values of tuning parameters that resulted in good performance, personalization improved estimation error on average by 8.5 ± 13.8% for phase (mean ± standard deviation), 27.2 ± 8.1% for stride length, and 10.5 ± 13.5% for ground inclination. These findings support the hypothesis that personalization of the measurement model significantly improves gait-state estimation performance in EKF based gait-state tracking (P ≪ 0.05 ), which could ultimately enable reliable responses to faster human gait changes.
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Affiliation(s)
| | | | - Robert D Gregg
- Department of Robotics, University of Michigan, Ann Arbor, MI 48109 USA
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Best TK, Welker CG, Rouse EJ, Gregg RD. Data-Driven Variable Impedance Control of a Powered Knee-Ankle Prosthesis for Adaptive Speed and Incline Walking. IEEE T ROBOT 2023; 39:2151-2169. [PMID: 37304232 PMCID: PMC10249435 DOI: 10.1109/tro.2022.3226887] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2023]
Abstract
Most impedance-based walking controllers for powered knee-ankle prostheses use a finite state machine with dozens of user-specific parameters that require manual tuning by technical experts. These parameters are only appropriate near the task (e.g., walking speed and incline) at which they were tuned, necessitating many different parameter sets for variable-task walking. In contrast, this paper presents a data-driven, phase-based controller for variable-task walking that uses continuously-variable impedance control during stance and kinematic control during swing to enable biomimetic locomotion. After generating a data-driven model of variable joint impedance with convex optimization, we implement a novel task-invariant phase variable and real-time estimates of speed and incline to enable autonomous task adaptation. Experiments with above-knee amputee participants (N=2) show that our data-driven controller 1) features highly-linear phase estimates and accurate task estimates, 2) produces biomimetic kinematic and kinetic trends as task varies, leading to low errors relative to able-bodied references, and 3) produces biomimetic joint work and cadence trends as task varies. We show that the presented controller meets and often exceeds the performance of a benchmark finite state machine controller for our two participants, without requiring manual impedance tuning.
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Affiliation(s)
- T Kevin Best
- Department of Electrical Engineering and Computer Science and the Robotics Institute, University of Michigan, Ann Arbor, MI 48109
| | - Cara Gonzalez Welker
- Department of Electrical Engineering and Computer Science and the Robotics Institute, University of Michigan, Ann Arbor, MI 48109
| | - Elliott J Rouse
- Department of Mechanical Engineering and the Robotics Institute, University of Michigan, Ann Arbor, MI 48109
| | - Robert D Gregg
- Department of Electrical Engineering and Computer Science and the Robotics Institute, University of Michigan, Ann Arbor, MI 48109
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Medrano RL, Thomas GC, Keais CG, Rouse EJ, Gregg RD. Real-Time Gait Phase and Task Estimation for Controlling a Powered Ankle Exoskeleton on Extremely Uneven Terrain. IEEE T ROBOT 2023; 39:2170-2182. [PMID: 37304231 PMCID: PMC10249462 DOI: 10.1109/tro.2023.3235584] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Positive biomechanical outcomes have been reported with lower-limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This paper presents a controller for an ankle exoskeleton that uses a data-driven kinematic model to continuously estimate the phase, phase rate, stride length, and ground incline states during locomotion, which enables the real-time adaptation of torque assistance to match human torques observed in a multi-activity database of 10 able-bodied subjects. We demonstrate in live experiments with a new cohort of 10 able-bodied participants that the controller yields phase estimates comparable to the state of the art, while also estimating task variables with similar accuracy to recent machine learning approaches. The implemented controller successfully adapts its assistance in response to changing phase and task variables, both during controlled treadmill trials (N=10, phase RMSE: 4.8 ± 2.4%) and a real-world stress test with extremely uneven terrain (N=1, phase RMSE: 4.8 ± 2.7%).
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Affiliation(s)
| | | | - Connor G Keais
- Department of Robotics, University of Michigan, Ann Arbor, MI 48109
| | - Elliott J Rouse
- Department of Mechanical Engineering; Department of Robotics, University of Michigan, Ann Arbor, MI 48109
| | - Robert D Gregg
- Department of Robotics, University of Michigan, Ann Arbor, MI 48109
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Reznick E, Welker CG, Gregg RD. Predicting Individualized Joint Kinematics Over Continuous Variations of Walking, Running, and Stair Climbing. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 3:211-217. [PMID: 36819935 PMCID: PMC9928215 DOI: 10.1109/ojemb.2023.3234431] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 11/23/2022] [Accepted: 12/29/2022] [Indexed: 06/15/2024] Open
Abstract
Goal: Accounting for gait individuality is important to positive outcomes with wearable robots, but manually tuning multi-activity models is time-consuming and not viable in a clinic. Generalizations can possibly be made to predict gait individuality in unobserved conditions. Methods: Kinematic individuality-how one person's joint angles differ from the group-is quantified for every subject, joint, ambulation mode (walking, running, stair ascent, and stair descent), and intramodal task (speed, incline) in an open-access dataset with 10 able-bodied subjects. Four N-way ANOVAs test how prediction methods affect the fit to experimental data between and within ambulation modes. We test whether walking individuality (measured at a single speed on level ground) carries across modes, or whether a mode-specific prediction (based on a single task for each mode) is significantly more effective. Results: Kinematic individualization improves fit across joint and task if we consider each mode separately. Across all modes, tasks, and joints, modal individualization improved the fit in 81% of trials, improving the fit on average by 4.3[Formula: see text] across the gait cycle. This was statistically significant at all joints for walking and running, and half the joints for stair ascent/descent. Conclusions: For walking and running, kinematic individuality can be easily generalized within mode, but the trends are mixed on stairs depending on joint.
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Affiliation(s)
- Emma Reznick
- Department of RoboticsUniversity of MichiganAnn ArborMI48109USA
| | - Cara Gonzalez Welker
- Department of Mechanical EngineeringUniversity of Colorado BoulderBoulderCO80309USA
| | - Robert D. Gregg
- Department of RoboticsUniversity of MichiganAnn ArborMI48109USA
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Horn JC, Gregg RD. Nonholonomic Virtual Constraints for Control of Powered Prostheses Across Walking Speeds. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY : A PUBLICATION OF THE IEEE CONTROL SYSTEMS SOCIETY 2022; 30:2062-2071. [PMID: 35990403 PMCID: PMC9390073 DOI: 10.1109/tcst.2021.3133823] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
This paper presents a method to design a nonholonomic virtual constraint (NHVC) controller that produces multiple distinct stance-phase trajectories for corresponding walking speeds. NHVCs encode velocity-dependent joint trajectories via momenta conjugate to the unactuated degree(s)-of-freedom of the system. We recently introduced a method for designing NHVCs that allow for stable bipedal robotic walking across variable terrain slopes. This work extends the notion of NHVCs for application to variable-cadence powered prostheses. Using the segmental conjugate momentum for the prosthesis, an optimization problem is used to design a single stance-phase NHVC for three distinct walking speed trajectories (slow, normal, and fast). This stance-phase controller is implemented with a holonomic swing phase controller on a powered knee-ankle prosthesis, and experiments are conducted with an able-bodied user walking in steady and non-steady velocity conditions. The control scheme is capable of representing 1) multiple, task-dependent reference trajectories, and 2) walking gait variance due to both temporal and kinematic changes in user motion.
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Affiliation(s)
- Jonathan C Horn
- Department of Mechanical Engineering and Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75080, USA
| | - Robert D Gregg
- Department of Electrical Engineering and Computer Science and the Robotics Institute, University of Michigan, Ann Arbor, MI 48109 USA
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Cheng S, Bolívar-Nieto E, Welker CG, Gregg RD. Modeling the Transitional Kinematics Between Variable-Incline Walking and Stair Climbing. IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS 2022; 4:840-851. [PMID: 35991942 PMCID: PMC9386740 DOI: 10.1109/tmrb.2022.3185405] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Although emerging powered prostheses can enable people with lower-limb amputation to walk and climb stairs over different task conditions (e.g., speeds and inclines), the control architecture typically uses a finite-state machine to switch between activity-specific controllers. Because these controllers focus on steady-state locomotion, powered prostheses abruptly switch between controllers during gait transitions rather than continuously adjusting leg biomechanics in synchrony with the users. This paper introduces a new framework for powered prosthesis control by modeling the lower-limb joint kinematics over a continuum of variable-incline walking and stair climbing, including steady-state and transitional gaits. Steady-state models for walking and stair climbing represent joint kinematics as continuous functions of gait phase, forward speed, and incline. Transition models interpolate kinematics as convex combinations of the two steady-state models, with an additional term to account for kinematics that fall outside their convex hull. The coefficients of this convex combination denote the similarity of the transitional kinematics to each steady-state mode, providing insight into how able-bodied individuals continuously transition between ambulation modes. Cross-validation demonstrates that the model predictions of untrained kinematics have errors within the range of physiological variability for all joints. Simulation results demonstrate the model's robustness to incline estimation and mode classification errors.
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Affiliation(s)
- Shihao Cheng
- Department of Mechanical Engineering and the Robotics Institute, University of Michigan, Ann Arbor, MI, 48109 USA
| | - Edgar Bolívar-Nieto
- Department of Electrical and Computer Engineering and the Robotics Institute, University of Michigan, Ann Arbor, MI, 48109 USA
| | - Cara Gonzalez Welker
- Department of Electrical and Computer Engineering and the Robotics Institute, University of Michigan, Ann Arbor, MI, 48109 USA
| | - Robert D Gregg
- Department of Electrical and Computer Engineering and the Robotics Institute, University of Michigan, Ann Arbor, MI, 48109 USA
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Medrano RL, Thomas GC, Rouse EJ, Gregg RD. Analysis of the Bayesian Gait-State Estimation Problem for Lower-Limb Wearable Robot Sensor Configurations. IEEE Robot Autom Lett 2022; 7:7463-7470. [PMID: 35782346 PMCID: PMC9246062 DOI: 10.1109/lra.2022.3183790] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Many exoskeletons today are primarily tested in controlled, steady-state laboratory conditions that are unrealistic representations of their real-world usage in which walking conditions (e.g., speed, slope, and stride length) change constantly. One potential solution is to detect these changing walking conditions online using Bayesian state estimation to deliver assistance that continuously adapts to the wearer's gait. This paper investigates such an approach in silico, aiming to understand 1) which of the various Bayesian filter assumptions best match the problem, and 2) which gait parameters can be feasibly estimated with different combinations of sensors available to different exoskeleton configurations (pelvis, thigh, shank, and/or foot). Our results suggest that the assumptions of the Extended Kalman Filter are well suited to accurately estimate phase, stride frequency, stride length, and ramp inclination with a wide variety of sparse sensor configurations.
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Affiliation(s)
- Roberto Leo Medrano
- Department of Mechanical Engineering and the Robotics Institute, University of Michigan, Ann Arbor, MI 48109 USA
| | - Gray Cortright Thomas
- Department of Electrical Engineering and Computer Science and the Robotics Institute, University of Michigan, Ann Arbor, MI 48109 USA
| | - Elliott J. Rouse
- Department of Mechanical Engineering and the Robotics Institute, University of Michigan, Ann Arbor, MI 48109 USA
| | - Robert D. Gregg
- Department of Electrical Engineering and Computer Science and the Robotics Institute, University of Michigan, Ann Arbor, MI 48109 USA
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9
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Xu D, Zhang Z, Crea S, Vitiello N, Wang Q. Adaptive estimation of continuous gait phase based on capacitive sensors. WEARABLE TECHNOLOGIES 2022; 3:e11. [PMID: 38486906 PMCID: PMC10936350 DOI: 10.1017/wtc.2022.4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 03/25/2022] [Accepted: 03/25/2022] [Indexed: 03/17/2024]
Abstract
Continuous gait phase plays an important role in robotic prosthesis control. In this paper, we have conducted the offline adaptive estimation (at different speeds and on different ramps) of continuous gait phase of robotic transtibial prosthesis based on the adaptive oscillators. We have used the capacitive sensing method to record the deformation of the muscles. Two transtibial amputees joined in this study. Based on the strain signals of the prosthetic foot and the capacitive signals of the residual limb, the maximum and minimum of estimation errors are 0.80 rad and 0.054 rad, respectively, and their corresponding ratios in one gait cycle are 1.27% and 0.86%, respectively. This paper proposes an effective method to estimate the continuous gait phase based on the capacitive signals of the residual muscles, which provides a basis for the continuous control of robotic transtibial prosthesis.
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Affiliation(s)
- Dongfang Xu
- Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing100871, China
- Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, Beijing, China
| | - Zhitong Zhang
- Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing100871, China
- National Key Laboratory of Science and Technology on Micro/Nano Fabrication, Institute of Micro/Nano Electronics, Peking University, Beijing, China
| | - Simona Crea
- BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, Italy
- IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Nicola Vitiello
- BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, Italy
- IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Qining Wang
- Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing100871, China
- Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, Beijing, China
- Institute for Artificial Intelligence, Peking University, Beijing, China
- University of Health and Rehabilitation Sciences, Qingdao, China
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Reznick E, Embry KR, Neuman R, Bolívar-Nieto E, Fey NP, Gregg RD. Lower-limb kinematics and kinetics during continuously varying human locomotion. Sci Data 2021; 8:282. [PMID: 34711856 PMCID: PMC8553836 DOI: 10.1038/s41597-021-01057-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 09/15/2021] [Indexed: 12/03/2022] Open
Abstract
Human locomotion involves continuously variable activities including walking, running, and stair climbing over a range of speeds and inclinations as well as sit-stand, walk-run, and walk-stairs transitions. Understanding the kinematics and kinetics of the lower limbs during continuously varying locomotion is fundamental to developing robotic prostheses and exoskeletons that assist in community ambulation. However, available datasets on human locomotion neglect transitions between activities and/or continuous variations in speed and inclination during these activities. This data paper reports a new dataset that includes the lower-limb kinematics and kinetics of ten able-bodied participants walking at multiple inclines (±0°; 5° and 10°) and speeds (0.8 m/s; 1 m/s; 1.2 m/s), running at multiple speeds (1.8 m/s; 2 m/s; 2.2 m/s and 2.4 m/s), walking and running with constant acceleration (±0.2; 0.5), and stair ascent/descent with multiple stair inclines (20°; 25°; 30° and 35°). This dataset also includes sit-stand transitions, walk-run transitions, and walk-stairs transitions. Data were recorded by a Vicon motion capture system and, for applicable tasks, a Bertec instrumented treadmill.
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Affiliation(s)
- Emma Reznick
- University of Michigan, Robotics Institute, Ann Arbor, MI, 48109, USA
| | - Kyle R Embry
- University of Texas at Dallas, Department of Mechanical Engineering, Richardson, TX, 75080, USA
- Shirley Ryan AbilityLab, Center for Bionic Medicine, Chicago, IL, 60611, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, 60611, USA
| | - Ross Neuman
- University of Texas at Austin, Department of Mechanical Engineering, Austin, TX, 78712, USA
| | - Edgar Bolívar-Nieto
- University of Michigan, Robotics Institute, Ann Arbor, MI, 48109, USA
- University of Michigan, Department of Electrical Engineering and Computer Science, Ann Arbor, MI, 48109, USA
| | - Nicholas P Fey
- University of Texas at Austin, Department of Mechanical Engineering, Austin, TX, 78712, USA
| | - Robert D Gregg
- University of Michigan, Robotics Institute, Ann Arbor, MI, 48109, USA.
- University of Michigan, Department of Electrical Engineering and Computer Science, Ann Arbor, MI, 48109, USA.
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Eslamy M, Schilling AF. Estimation of knee and ankle angles during walking using thigh and shank angles. BIOINSPIRATION & BIOMIMETICS 2021; 16:066012. [PMID: 34492652 DOI: 10.1088/1748-3190/ac245f] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 09/07/2021] [Indexed: 06/13/2023]
Abstract
Estimation of joints' trajectories is commonly used in human gait analysis, and in the development of motion planners and high-level controllers for prosthetics, orthotics, exoskeletons and humanoids. Human locomotion is the result of the cooperation between leg joints and limbs. This suggests the existence of underlying relationships between them which lead to a harmonic gait. In this study we aimed to estimate knee and ankle trajectories using thigh and shank angles. To do so, an estimation approach was developed that continuously mapped the inputs to the outputs, which did not require switching rules, speed estimation, gait percent identification or look-up tables. The estimation algorithm was based on a nonlinear auto-regressive model with exogenous inputs. The method was then combined with wavelets theory, and then the two were used in a neural network. To evaluate the estimation performance, three scenarios were developed which used only one source of inputs (i.e., only shank angles or only thigh angles). First, knee anglesθk(outputs) were estimated using thigh anglesθth(inputs). Second, ankle anglesθa(outputs) were estimated using thigh anglesθsh(inputs), and third, the ankle angles were estimated using shank angles (inputs). The proposed approach was investigated for 22 subjects at different walking speeds and the leave-one-subject-out procedure was used for training and testing the estimation algorithm. Average root mean square errors were 3.9°-5.3° and 2.1°-2.3° for knee and ankle angles, respectively. Average mean absolute errors (MAEs) MAEs were 3.2°-4° and 1.7°-1.8°, and average correlation coefficientsρccwere 0.95-0.98 and 0.94-0.96 for knee and ankle angles, respectively. The limitations and strengths of the proposed approach are discussed in detail and the results are compared with several studies.
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Affiliation(s)
- Mahdy Eslamy
- Applied Rehabilitation Technology ART Lab, Department for Trauma Surgery, Orthopaedics and Plastic Surgery, Universitätsmedizin Göttingen (UMG), 37075, Göttingen, Germany
| | - Arndt F Schilling
- Applied Rehabilitation Technology ART Lab, Department for Trauma Surgery, Orthopaedics and Plastic Surgery, Universitätsmedizin Göttingen (UMG), 37075, Göttingen, Germany
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Cheng S, Bolivar-Nieto E, Gregg RD. Real-Time Activity Recognition With Instantaneous Characteristic Features of Thigh Kinematics. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1827-1837. [PMID: 34428147 PMCID: PMC8446341 DOI: 10.1109/tnsre.2021.3107780] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Current supervised learning or deep learning-based activity recognition classifiers can achieve high accuracy in recognizing locomotion activities. Most available techniques use a high-dimensional space of features, e.g., combinations of EMG, kinematics and kinetics, and transformations over those signals. The associated classification rules are therefore complex; the machine tries to understand the human, but the human does not understand the machine. This paper presents an activity recognition system that uses signals from a thigh-mounted IMU and a force sensitive resistor to classify transitions between sitting, walking, stair ascending, and stair descending. The system uses the thigh's orientation and velocity with foot contact information at specific moments within a given activity as the features to classify transitions to other activities. We call these Instantaneous Characteristic Features (ICFs). Because these ICFs are biomechanically intuitive, they are easy for the user to understand and thus control the activity transitions of wearable robots. We assessed our classification algorithm offline using an existing dataset with 10 able-bodied subjects and online with another 10 able-bodied subjects wearing a real-time system. The offline study analyzed the effect of subject-dependency and ramp inclinations. The real-time classification accuracy was evaluated before and after training the subjects on the ICFs. The real-time system achieved overall pre-subject-training and post-subject-training error rates of 0.59% ± 0.24% and 0.56% ± 0.20%, respectively. We also evaluated the feasibility of our ICFs for amputee ambulation by analyzing a public dataset with the open-source bionic leg. The simplicity of these classification rules demonstrates a new paradigm for activity recognition where the human can understand the machine and vice-versa.
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Best TK, Embry KR, Rouse EJ, Gregg RD. Phase-Variable Control of a Powered Knee-Ankle Prosthesis over Continuously Varying Speeds and Inclines. PROCEEDINGS OF THE ... IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS. IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS 2021; 2021:6182-6189. [PMID: 35251752 DOI: 10.1109/iros51168.2021.9636180] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Most controllers for lower-limb robotic prostheses require individually tuned parameter sets for every combination of speed and incline that the device is designed for. Because ambulation occurs over a continuum of speeds and inclines, this design paradigm requires tuning of a potentially prohibitively large number of parameters. This limitation motivates an alternative control framework that enables walking over a range of speeds and inclines while requiring only a limited number of tunable parameters. In this work, we present the implementation of a continuously varying kinematic controller on a custom powered knee-ankle prosthesis. The controller uses a phase variable derived from the residual thigh angle, along with real-time estimates of ground inclination and walking speed, to compute the appropriate knee and ankle joint angles from a continuous model of able-bodied kinematic data. We modify an existing phase variable architecture to allow for changes in speeds and inclines, quantify the closed-loop accuracy of the speed and incline estimation algorithms for various references, and experimentally validate the controller by observing that it replicates kinematic trends seen in able-bodied gait as speed and incline vary.
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Affiliation(s)
- T Kevin Best
- Department of Electrical Engineering and Computer Science and the Robotics Institute, University of Michigan, Ann Arbor, MI 48109
| | - Kyle R Embry
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, and the Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL 60611
| | - Elliott J Rouse
- Department of Mechanical Engineering and the Robotics Institute, University of Michigan, Ann Arbor, MI 48109
| | - Robert D Gregg
- Department of Electrical Engineering and Computer Science and the Robotics Institute, University of Michigan, Ann Arbor, MI 48109
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