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Akhetova SM, Roembke R, Adamczyk P. Detecting Toe-Off and Initial Contact in Real-Time With Self-Adapting Thresholds. J Biomech Eng 2024; 146:114502. [PMID: 38949879 DOI: 10.1115/1.4065842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 06/24/2024] [Indexed: 07/03/2024]
Abstract
This research introduces an adaptive control algorithm designed to determine gait phase in real-time using an inertial measurement unit (IMU) affixed to the shank. Focusing on detecting specific gait events, primarily initial contact (IC) and toe-off (TO), the algorithm utilizes dynamic thresholds and ratios that facilitate accurate event determination adaptively across a range of walking speeds. Built-in safety checks further ensure precision and minimize false detections. We validated the algorithm with eight participants walking at varying speeds. The algorithm demonstrated promising results in detecting IC and TO events with mean lead of 8.95 ms and 4.42 ms and detection success rate of 100% and 99.72%, respectively. These results are consistent with benchmarks from established algorithms (Hanlon and Anderson, 2009, "Real-Time Gait Event Detection Using Wearable Sensors," Gait Posture, 30(4), pp. 523-527; Maqbool et al., 2017, "A Real-Time Gait Event Detection for Lower Limb Prosthesis Control and Evaluation," IEEE Trans. Neural Syst. Rehabil. Eng.: Publ. IEEE Eng. Med. Biol. Soc., 25(9), pp. 1500-1509). Moreover, the algorithm's self-adaptive nature ensures it can be used in scenarios of varying movement, offering a promising solution for real-time gait phase detection.
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Affiliation(s)
- Sofya M Akhetova
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Engineering Hall, 2415, 1415 Engineering Drive, Madison, WI 53706
- University of Wisconsin-Madison
| | - Rebecca Roembke
- Department of Mechanical Engineering, University of Wisconsin-Madison, Mechanical Engineering Building, 2107, 1513 University Avenue, Madison, WI 53706
- University of Wisconsin-Madison
| | - Peter Adamczyk
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Engineering Hall, 2415, 1415 Engineering Drive, Madison, WI 53706; Department of Mechanical Engineering, University of Wisconsin-Madison, Mechanical Engineering Building, 2107, 1513 University Avenue, Madison, WI 53706
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He Y, Xu Y, Hai M, Feng Y, Liu P, Chen Z, Duan W. Exoskeleton-Assisted Rehabilitation and Neuroplasticity in Spinal Cord Injury. World Neurosurg 2024; 185:45-54. [PMID: 38320651 DOI: 10.1016/j.wneu.2024.01.167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/29/2024] [Accepted: 01/30/2024] [Indexed: 02/08/2024]
Abstract
Spinal cord injury (SCI) results in neurological deficits below the level of injury, causing motor dysfunction and various severe multisystem complications. Rehabilitative training plays a crucial role in the recovery of individuals with SCI, and exoskeleton serves as an emerging and promising tool for rehabilitation, especially in promoting neuroplasticity and alleviating SCI-related complications. This article reviews the classifications and research progresses of medical exoskeletons designed for SCI patients and describes their performances in practical application separately. Meanwhile, we discuss their mechanisms for enhancing neuroplasticity and functional remodeling, as well as their palliative impacts on secondary complications. The potential trends in exoskeleton design are raised according to current progress and requirements on SCI rehabilitation.
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Affiliation(s)
- Yana He
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China; School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Yuxuan Xu
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China; School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Minghang Hai
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China; School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Yang Feng
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China; School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Penghao Liu
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China; Lab of Spinal Cord Injury and Functional Reconstruction, China International Neuroscience Institute(CHINA-INI), Beijing, China
| | - Zan Chen
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China; Lab of Spinal Cord Injury and Functional Reconstruction, China International Neuroscience Institute(CHINA-INI), Beijing, China
| | - Wanru Duan
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China; Lab of Spinal Cord Injury and Functional Reconstruction, China International Neuroscience Institute(CHINA-INI), Beijing, China.
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Strick JA, Farris RJ, Sawicki JT. A Novel Gait Event Detection Algorithm Using a Thigh-Worn Inertial Measurement Unit and Joint Angle Information. J Biomech Eng 2024; 146:044502. [PMID: 38183222 DOI: 10.1115/1.4064435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 12/27/2023] [Indexed: 01/07/2024]
Abstract
This paper describes the development and evaluation of a novel, threshold-based gait event detection algorithm utilizing only one thigh inertial measurement unit (IMU) and unilateral, sagittal plane hip and knee joint angles. The algorithm was designed to detect heel strike (HS) and toe off (TO) gait events, with the eventual goal of detection in a real-time exoskeletal control system. The data used in the development and evaluation of the algorithm were obtained from two gait databases, each containing synchronized IMU and ground reaction force (GRF) data. All database subjects were healthy individuals walking in either a level-ground, urban environment or a treadmill lab environment. Inertial measurements used were three-dimensional thigh accelerations and three-dimensional thigh angular velocities. Parameters for the TO algorithm were identified on a per-subject basis. The GRF data were utilized to validate the algorithm's timing accuracy and quantify the fidelity of the algorithm, measured by the F1-Score. Across all participants, the algorithm reported a mean timing error of -41±20 ms with an F1-Score of 0.988 for HS. For TO, the algorithm reported a mean timing error of -1.4±21 ms with an F1-Score of 0.991. The results of this evaluation suggest that this algorithm is a promising solution to inertial based gait event detection; however, further refinement and real-time evaluation are required for use in exoskeletal control.
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Affiliation(s)
- Jacob A Strick
- Center for Rotating Machinery Dynamics and Control (RoMaDyC), Washkewicz College of Engineering, Cleveland State University, 2121 Euclid Avenue, Cleveland, OH 44115
| | - Ryan J Farris
- Department of Engineering, Messiah University, One University Avenue, Mechanicsburg, PA 17055
| | - Jerzy T Sawicki
- Center for Rotating Machinery Dynamics and Control (RoMaDyC), Washkewicz College of Engineering, Cleveland State University, 2121 Euclid Avenue, Cleveland, OH 44115
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Uhlenberg L, Derungs A, Amft O. Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation. Front Bioeng Biotechnol 2023; 11:1104000. [PMID: 37122859 PMCID: PMC10132030 DOI: 10.3389/fbioe.2023.1104000] [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: 11/21/2022] [Accepted: 03/29/2023] [Indexed: 05/02/2023] Open
Abstract
We propose a co-simulation framework comprising biomechanical human body models and wearable inertial sensor models to analyse gait events dynamically, depending on inertial sensor type, sensor positioning, and processing algorithms. A total of 960 inertial sensors were virtually attached to the lower extremities of a validated biomechanical model and shoe model. Walking of hemiparetic patients was simulated using motion capture data (kinematic simulation). Accelerations and angular velocities were synthesised according to the inertial sensor models. A comprehensive error analysis of detected gait events versus reference gait events of each simulated sensor position across all segments was performed. For gait event detection, we considered 1-, 2-, and 4-phase gait models. Results of hemiparetic patients showed superior gait event estimation performance for a sensor fusion of angular velocity and acceleration data with lower nMAEs (9%) across all sensor positions compared to error estimation with acceleration data only. Depending on algorithm choice and parameterisation, gait event detection performance increased up to 65%. Our results suggest that user personalisation of IMU placement should be pursued as a first priority for gait phase detection, while sensor position variation may be a secondary adaptation target. When comparing rotatory and translatory error components per body segment, larger interquartile ranges of rotatory errors were observed for all phase models i.e., repositioning the sensor around the body segment axis was more harmful than along the limb axis for gait phase detection. The proposed co-simulation framework is suitable for evaluating different sensor modalities, as well as gait event detection algorithms for different gait phase models. The results of our analysis open a new path for utilising biomechanical human digital twins in wearable system design and performance estimation before physical device prototypes are deployed.
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Affiliation(s)
- Lena Uhlenberg
- Hahn-Schickard, Freiburg, Germany
- Intelligent Embedded Systems Lab, University of Freiburg, Freiburg, Germany
- *Correspondence: Lena Uhlenberg,
| | - Adrian Derungs
- F. Hoffmann–La Roche Ltd, pRED, Roche Innovation Center Basel, Basel, Switzerland
| | - Oliver Amft
- Hahn-Schickard, Freiburg, Germany
- Intelligent Embedded Systems Lab, University of Freiburg, Freiburg, Germany
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Gouda A, Andrysek J. Rules-Based Real-Time Gait Event Detection Algorithm for Lower-Limb Prosthesis Users during Level-Ground and Ramp Walking. SENSORS (BASEL, SWITZERLAND) 2022; 22:8888. [PMID: 36433483 PMCID: PMC9693475 DOI: 10.3390/s22228888] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 11/10/2022] [Accepted: 11/15/2022] [Indexed: 06/16/2023]
Abstract
Real-time gait event detection (GED) using inertial sensors is important for applications such as remote gait assessments, intelligent assistive devices including microprocessor-based prostheses or exoskeletons, and gait training systems. GED algorithms using acceleration and/or angular velocity signals achieve reasonable performance; however, most are not suited for real-time applications involving clinical populations walking in free-living environments. The aim of this study was to develop and evaluate a real-time rules-based GED algorithm with low latency and high accuracy and sensitivity across different walking states and participant groups. The algorithm was evaluated using gait data collected from seven able-bodied (AB) and seven lower-limb prosthesis user (LLPU) participants for three walking states (level-ground walking (LGW), ramp ascent (RA), ramp descent (RD)). The performance (sensitivity and temporal error) was compared to a validated motion capture system. The overall sensitivity was 98.87% for AB and 97.05% and 93.51% for LLPU intact and prosthetic sides, respectively, across all walking states (LGW, RA, RD). The overall temporal error (in milliseconds) for both FS and FO was 10 (0, 20) for AB and 10 (0, 25) and 10 (0, 20) for the LLPU intact and prosthetic sides, respectively, across all walking states. Finally, the overall error (as a percentage of gait cycle) was 0.96 (0, 1.92) for AB and 0.83 (0, 2.08) and 0.83 (0, 1.66) for the LLPU intact and prosthetic sides, respectively, across all walking states. Compared to other studies and algorithms, the herein-developed algorithm concurrently achieves high sensitivity and low temporal error with near real-time detection of gait in both typical and clinical populations walking over a variety of terrains.
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Affiliation(s)
- Aliaa Gouda
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON M4G 1R8, Canada
| | - Jan Andrysek
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON M4G 1R8, Canada
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Liu X, Zhang S, Yao B, Yu Y, Wang Y, Fan J. Gait phase detection based on inertial measurement unit and force-sensitive resistors embedded in a shoe. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2021; 92:084708. [PMID: 34470402 DOI: 10.1063/5.0056893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 08/03/2021] [Indexed: 06/13/2023]
Abstract
This study proposes a system to detect the phases of gait. It consists of an intelligent shoe equipped with an inertial measurement unit (IMU) and force-sensitive resistors (FSRs), and it uses a compound method to recognize gait. The continuous wavelet transform is applied according to accelerations obtained via the IMU to identify heel strike and toe-off events. These events are used to calculate the pressure threshold and proportional factor via the Lopez-Meyer (LM) method by using minimal leave-one-out for training and validation. The LM method can identify the entire sub-phase of the stance of the gait based on ground contact forces measured by using the FSRs and rules of gait event detection. The proposed system was tested on five healthy volunteers who used the intelligent shoe. The results show that it can detect all sub-phases of the gait with an overall accuracy (96%) higher than the LM method. The proportional factor was adaptable to variable body weights, and the reported average errors of competing systems in the literature significantly exceeded the average variation of the proposed system for all phases of gait. The range of errors in the swing phase and sub-phases of stance was also acceptable for application purposes. When the size of the subject's foot was close to that of the intelligent shoe, the error between normative data and phases of gait identified by the detection system was minimal. Furthermore, the proposed system detected abnormalities in the gait circle, and thus, it can be used to monitor the walking activity and measure the motor recovery.
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Affiliation(s)
- Xianwen Liu
- College of Mechanical Engineering and Transportation, China University of Petroleum-Beijing, Changping, China
| | - Shimin Zhang
- College of Mechanical Engineering and Transportation, China University of Petroleum-Beijing, Changping, China
| | - Benchun Yao
- College of Mechanical Engineering and Transportation, China University of Petroleum-Beijing, Changping, China
| | - Yang Yu
- College of Mechanical Engineering and Transportation, China University of Petroleum-Beijing, Changping, China
| | - Yusong Wang
- College of Mechanical Engineering and Transportation, China University of Petroleum-Beijing, Changping, China
| | - Jinchao Fan
- College of Mechanical Engineering and Transportation, China University of Petroleum-Beijing, Changping, China
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Li DX, Zha FB, Long JJ, Liu F, Cao J, Wang YL. Effect of Robot Assisted Gait Training on Motor and Walking Function in Patients with Subacute Stroke: A Random Controlled Study. J Stroke Cerebrovasc Dis 2021; 30:105807. [PMID: 33895428 DOI: 10.1016/j.jstrokecerebrovasdis.2021.105807] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 03/23/2021] [Accepted: 03/30/2021] [Indexed: 10/21/2022] Open
Abstract
BACKGROUND Robot-assisted gait training has been confirmed to have beneficial effect on the rehabilitation of stroke patients. An exoskeleton robot, named BEAR-H1, is designed to help stroke patients with walking disabilities. METHODS 17 subjects in experimental group and 15 subjects in control group completed the study. The experimental group received 30 minutes of BEAR-H1 assisted gait training(BAGT), and the control group received 30 minutes of conventional training, 5 times/week for 4weeks. All subjects were evaluated with 6-minute walk test (6MWT), Fugl-Meyer Assessment for lower extremity (FMA-LE), Functional Ambulatory Classification (FAC), Modified Ashworth Scale (MAS), and gait analysis at baseline and after 4 weeks intervention. RESULTS The improvements of 6MWT, FMA-LE, gait speed, cadence, step length and cycle duration in BAGT group were more noticeable than in the control group. However, there was no difference in the assessment of MAS between two groups. CONCLUSIONS Our results showed that BAGT is an effective intervention to improve the motor and walking ability during 4 weeks training for subacute stroke patients.
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Affiliation(s)
- Dong-Xia Li
- Department of Rehabilitation, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, China.
| | - Fu-Bing Zha
- Department of Rehabilitation, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, China.
| | - Jian-Jun Long
- Department of Rehabilitation, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, China.
| | - Fang Liu
- Department of Rehabilitation, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, China.
| | - Jia Cao
- Department of Rehabilitation, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, China.
| | - Yu-Long Wang
- Department of Rehabilitation, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, China.
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