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Cheng S, Laubscher CA, Gregg RD. Automatic Stub Avoidance for a Powered Prosthetic Leg Over Stairs and Obstacles. IEEE Trans Biomed Eng 2024; 71:1499-1510. [PMID: 38060364 PMCID: PMC11035099 DOI: 10.1109/tbme.2023.3340628] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Passive prosthetic legs require undesirable compensations from amputee users to avoid stubbing obstacles and stairsteps. Powered prostheses can reduce those compensations by restoring normative joint biomechanics, but the absence of user proprioception and volitional control combined with the absence of environmental awareness by the prosthesis increases the risk of collisions. This article presents a novel stub avoidance controller that automatically adjusts prosthetic knee/ankle kinematics based on suprasensory measurements of environmental distance from a small, lightweight, low-power, low-cost ultrasonic sensor mounted above the prosthetic ankle. In a case study with two transfemoral amputee participants, this control method reduced the stub rate during stair ascent by 89.95% and demonstrated an 87.50% avoidance rate for crossing different obstacles on level ground. No thigh kinematic compensation was required to achieve these results. These findings demonstrate a practical perception solution for powered prostheses to avoid collisions with stairs and obstacles while restoring normative biomechanics during daily activities.
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Le D, Cheng S, Gregg RD, Ghaffari M. Transfer Learning for Efficient Intent Prediction in Lower-Limb Prosthetics: A Strategy for Limited Datasets. IEEE Robot Autom Lett 2024; 9:4321-4328. [PMID: 39081804 PMCID: PMC11286256 DOI: 10.1109/lra.2024.3379800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Abstract
This paper presents a transfer learning method to enhance locomotion intent prediction in novel transfemoral amputee subjects, particularly in data-sparse scenarios. Transfer learning is done with three pre-trained models trained on separate datasets: transfemoral amputees, able-bodied individuals, and a mixed dataset of both groups. Each model is subsequently fine-tuned using data from a new transfemoral amputee subject. While subject-dependent models, trained and tested using individual user data, can achieve the least error rate, they require extensive training datasets. In contrast, our transfer learning approach yields comparable error rates while requiring significantly less data. This highlights the benefit of using preexisting, pre-trained features when data is scarce. As anticipated, the performance of transfer learning improves as more data from the subject is made available. We also explore the performance of the intent prediction system under various sensor configurations. We identify that a combination of a thigh inertial measurement unit and load cell offers a practical and efficient choice for sensor setup. These findings underscore the potential of transfer learning as a powerful tool for enhancing intent prediction accuracy for new transfemoral amputee subjects, even under data-limited conditions.
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Affiliation(s)
- Duong Le
- College of Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Shihao Cheng
- College of Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Robert D Gregg
- College of Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Maani Ghaffari
- College of Engineering, University of Michigan, Ann Arbor, MI 48109, USA
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Flanagan W, Becraft K, Warren H, Stavrakis AI, Bernthal NM, Hardin TJ, Clites TR. Prosthetic Limb Attachment via Electromagnetic Attraction Through a Closed Skin Envelope. IEEE Trans Biomed Eng 2024; 71:1552-1564. [PMID: 38090864 DOI: 10.1109/tbme.2023.3342652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
OBJECTIVE Current socket-based methods of prosthetic limb attachment are responsible for many of the dominant problems reported by persons with amputation. In this work, we introduce a new paradigm for attachment via electromagnetic attraction between a bone-anchored ferromagnetic implant and an external electromagnet. Our objective was to develop a design framework for electromagnetic attachment, and to evaluate this framework in the context of transfemoral amputation. METHODS We first used inverse dynamics to calculate the forces required to suspend a knee-ankle-foot prosthesis during gait. We then conducted cadaveric dissections to inform implant geometry and design a surgical methodology for covering the implant. We also developed an in silico framework to investigate how electromagnet design affects system performance. Simulations were validated against benchtop testing of a custom-built electromagnet. RESULTS The physical electromagnet matched simulations, with a root-mean-square percentage error of 4.2% between measured and predicted forces. Using this electromagnet, we estimate that suspension of a prosthesis during gait would require 33 W of average power. After 200 and 1000 steps of simulated walking, the temperature at the skin would increase 2.3 °C and 15.4 °C relative to ambient, respectively. CONCLUSION Our design framework produced an implant and electromagnet that could feasibly suspend a knee-ankle-foot prosthesis during short walking bouts. Future work will focus on optimization of this system to reduce heating during longer bouts. SIGNIFICANCE This work demonstrates the initial feasibility of an electromagnetic prosthetic attachment paradigm that has the potential to increase comfort and improve residual limb health for persons with amputation.
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Liu S, Wang Y, Ding F, Alsaedi A, Hayat T. Joint iterative state and parameter estimation for bilinear systems with autoregressive noises via the data filtering. ISA TRANSACTIONS 2024; 147:337-349. [PMID: 38342649 DOI: 10.1016/j.isatra.2024.01.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 01/29/2024] [Accepted: 01/29/2024] [Indexed: 02/13/2024]
Abstract
This paper proposes a novel iterative algorithm for the joint state and parameter estimation of bilinear state-space systems disturbed by colored noise. Estimating the states and parameters of such systems is challenging due to their nonlinearity and greater number of parameters compared to linear systems. Our method is to modify the Kalman filtering appropriately to estimate the unknown states of bilinear systems. Once the unknown states are estimated, we develop the Kalman filtering-based multi-innovation gradient-based iterative (KF-MIGI) algorithm for parameter estimation. To further improve estimation accuracy and cope with colored noises, we introduce a data filtering-based KF-MIGI algorithm that uses an adaptive filter to filter input-output data. Additionally, we compare the gradient-based iterative algorithm and the stochastic gradient algorithm. The effectiveness of the proposed algorithm is demonstrated through numerical examples.
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Affiliation(s)
- Siyu Liu
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, 321004, Jinhua, China; Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China.
| | - Yanjiao Wang
- School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
| | - Feng Ding
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China; School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China.
| | - Ahmed Alsaedi
- Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Tasawar Hayat
- Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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Cortino RJ, Best TK, Gregg RD. Data-Driven Phase-Based Control of a Powered Knee-Ankle Prosthesis for Variable-Incline Stair Ascent and Descent. IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS 2024; 6:175-188. [PMID: 38304755 PMCID: PMC10829527 DOI: 10.1109/tmrb.2023.3328656] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Powered knee-ankle prostheses can offer benefits over conventional passive devices during stair locomotion by providing biomimetic net-positive work and active control of joint angles. However, many modern control approaches for stair ascent and descent are often limited by time-consuming hand-tuning of user/task-specific parameters, predefined trajectories that remove user volition, or heuristic approaches that cannot be applied to both stair ascent and descent. This work presents a phase-based hybrid kinematic and impedance controller (HKIC) that allows for semi-volitional, biomimetic stair ascent and descent at a variety of step heights. We define a unified phase variable for both stair ascent and descent that utilizes lower-limb geometry to adjust to different users and step heights. We extend our prior data-driven impedance model for variable-incline walking, modifying the cost function and constraints to create a continuously-varying impedance parameter model for stair ascent and descent over a continuum of step heights. Experiments with above-knee amputee participants (N=2) validate that our HKIC controller produces biomimetic ascent and descent joint kinematics, kinetics, and work across four step height configurations. We also show improved kinematic performance with our HKIC controller in comparison to a passive microprocessor-controlled device during stair locomotion.
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Affiliation(s)
- Ross J Cortino
- Department of Robotics, University of Michigan, Ann Arbor, MI 48109
| | - T Kevin Best
- 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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Rubin N, Hinson R, Saul K, Hu X, Huang H. Ankle Torque Estimation With Motor Unit Discharges in Residual Muscles Following Lower-Limb Amputation. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4821-4830. [PMID: 38015668 PMCID: PMC10752569 DOI: 10.1109/tnsre.2023.3336543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
There has been increased interest in using residual muscle activity for neural control of powered lower-limb prostheses. However, only surface electromyography (EMG)-based decoders have been investigated. This study aims to investigate the potential of using motor unit (MU)-based decoding methods as an alternative to EMG-based intent recognition for ankle torque estimation. Eight people without amputation (NON) and seven people with amputation (AMP) participated in the experiments. Subjects conducted isometric dorsi- and plantarflexion with their intact limb by tracing desired muscle activity of the tibialis anterior (TA) and gastrocnemius (GA) while ankle torque was recorded. To match phantom limb and intact limb activity, AMP mirrored muscle activation with their residual TA and GA. We compared neuromuscular decoders (linear regression) for ankle joint torque estimation based on 1) EMG amplitude (aEMG), 2) MU firing frequencies representing neural drive (ND), and 3) MU firings convolved with modeled twitch forces (MUDrive). In addition, sensitivity analysis and dimensionality reduction of optimization were performed on the MUDrive method to further improve its practical value. Our results suggest MUDrive significantly outperforms (lower root-mean-square error) EMG and ND methods in muscles of NON, as well as both intact and residual muscles of AMP. Reducing the number of optimized MUDrive parameters degraded performance. Even so, optimization computational time was reduced and MUDrive still outperformed aEMG. Our outcomes indicate integrating MU discharges with modeled biomechanical outputs may provide a more accurate torque control signal than direct EMG control of assistive, lower-limb devices, such as exoskeletons and powered prostheses.
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Naseri A, Lee IC, Huang H, Liu M. Investigating the Association of Quantitative Gait Stability Metrics With User Perception of Gait Interruption Due to Control Faults During Human-Prosthesis Interaction. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4693-4702. [PMID: 37906490 DOI: 10.1109/tnsre.2023.3328877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
This study aims to compare the association of different gait stability metrics with the prosthesis users' perception of their own gait stability. Lack of perceived confidence on the device functionality can influence the gait pattern, level of daily activities, and overall quality of life for individuals with lower limb motor deficits. However, the perception of gait stability is subjective and difficult to acquire online. The quantitative gait stability metrics can be objectively measured and monitored using wearable sensors; however, objective measurements of gait stability associated with human's perception of their own gait stability has rarely been reported. By identifying quantitative measurements that associate with users' perceptions, we can gain a more accurate and comprehensive understanding of an individual's perceived functional outcomes of assistive devices such as prostheses. To achieve our research goal, experiments were conducted to artificially apply internal disturbances in the powered prosthesis while the prosthetic users performed level ground walking. We monitored and compared multiple gait stability metrics and a local measurement to the users' reported perception of their own gait stability. The results showed that the center of pressure progression in the sagittal plane and knee momentum (i.e., residual thigh and prosthesis shank angular momentum about prosthetic knee joint) can potentially estimate the users' perceptions of gait stability when experiencing disturbances. The findings of this study can help improve the development and evaluation of gait stability control algorithms in robotic prosthetic devices.
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Yip M, Salcudean S, Goldberg K, Althoefer K, Menciassi A, Opfermann JD, Krieger A, Swaminathan K, Walsh CJ, Huang HH, Lee IC. Artificial intelligence meets medical robotics. Science 2023; 381:141-146. [PMID: 37440630 DOI: 10.1126/science.adj3312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/15/2023]
Abstract
Artificial intelligence (AI) applications in medical robots are bringing a new era to medicine. Advanced medical robots can perform diagnostic and surgical procedures, aid rehabilitation, and provide symbiotic prosthetics to replace limbs. The technology used in these devices, including computer vision, medical image analysis, haptics, navigation, precise manipulation, and machine learning (ML) , could allow autonomous robots to carry out diagnostic imaging, remote surgery, surgical subtasks, or even entire surgical procedures. Moreover, AI in rehabilitation devices and advanced prosthetics can provide individualized support, as well as improved functionality and mobility (see the figure). The combination of extraordinary advances in robotics, medicine, materials science, and computing could bring safer, more efficient, and more widely available patient care in the future. -Gemma K. Alderton.
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Affiliation(s)
- Michael Yip
- Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Septimiu Salcudean
- Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Ken Goldberg
- Department of Industrial Engineering and Operations Research and Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA
| | - Kaspar Althoefer
- School of Engineering and Materials Science, Queen Mary University of London, London, UK
| | - Arianna Menciassi
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Piazza Martiri della Libertà, Pisa, Italy
| | - Justin D Opfermann
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Axel Krieger
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Krithika Swaminathan
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Conor J Walsh
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - He Helen Huang
- Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC, USA
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - I-Chieh Lee
- Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC, USA
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Simon AM, Finucane SB, Ikeda AJ, Cotton RJ, Hargrove LJ. Powered knee and ankle prosthesis use with a K2 level ambulator: a case report. FRONTIERS IN REHABILITATION SCIENCES 2023; 4:1203545. [PMID: 37387731 PMCID: PMC10300561 DOI: 10.3389/fresc.2023.1203545] [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: 04/10/2023] [Accepted: 05/25/2023] [Indexed: 07/01/2023]
Abstract
Powered prosthetic knees and ankles have the capability of restoring power to the missing joints and potential to provide increased functional mobility to users. Nearly all development with these advanced prostheses is with individuals who are high functioning community level ambulators even though limited community ambulators may also receive great benefit from these devices. We trained a 70 year old male participant with a unilateral transfemoral amputation to use a powered knee and powered ankle prosthesis. He participated in eight hours of therapist led in-lab training (two hours per week for four weeks). Sessions included static and dynamic balance activities for improved stability and comfort with the powered prosthesis and ambulation training on level ground, inclines, and stairs. Assessments were taken with both the powered prosthesis and his prescribed, passive prosthesis post-training. Outcome measures showed similarities in velocity between devices for level-ground walking and ascending a ramp. During ramp descent, the participant had a slightly faster velocity and more symmetrical stance and step times with the powered prosthesis compared to his prescribed prosthesis. For stairs, he was able to climb with reciprocal stepping for both ascent and descent, a stepping strategy he is unable to do with his prescribed prosthesis. More research with limited community ambulators is necessary to understand if further functional improvements are possible with either additional training, longer accommodation periods, and/or changes in powered prosthesis control strategies.
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Affiliation(s)
- Ann M. Simon
- Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, United States
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
| | - Suzanne B. Finucane
- Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, United States
| | - Andrea J. Ikeda
- Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, United States
| | - R. James Cotton
- Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, United States
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
| | - Levi J. Hargrove
- Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, United States
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
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