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de Miguel-Fernández J, Lobo-Prat J, Prinsen E, Font-Llagunes JM, Marchal-Crespo L. Control strategies used in lower limb exoskeletons for gait rehabilitation after brain injury: a systematic review and analysis of clinical effectiveness. J Neuroeng Rehabil 2023; 20:23. [PMID: 36805777 PMCID: PMC9938998 DOI: 10.1186/s12984-023-01144-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/07/2023] [Indexed: 02/21/2023] Open
Abstract
BACKGROUND In the past decade, there has been substantial progress in the development of robotic controllers that specify how lower-limb exoskeletons should interact with brain-injured patients. However, it is still an open question which exoskeleton control strategies can more effectively stimulate motor function recovery. In this review, we aim to complement previous literature surveys on the topic of exoskeleton control for gait rehabilitation by: (1) providing an updated structured framework of current control strategies, (2) analyzing the methodology of clinical validations used in the robotic interventions, and (3) reporting the potential relation between control strategies and clinical outcomes. METHODS Four databases were searched using database-specific search terms from January 2000 to September 2020. We identified 1648 articles, of which 159 were included and evaluated in full-text. We included studies that clinically evaluated the effectiveness of the exoskeleton on impaired participants, and which clearly explained or referenced the implemented control strategy. RESULTS (1) We found that assistive control (100% of exoskeletons) that followed rule-based algorithms (72%) based on ground reaction force thresholds (63%) in conjunction with trajectory-tracking control (97%) were the most implemented control strategies. Only 14% of the exoskeletons implemented adaptive control strategies. (2) Regarding the clinical validations used in the robotic interventions, we found high variability on the experimental protocols and outcome metrics selected. (3) With high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented a combination of trajectory-tracking and compliant control showed the highest clinical effectiveness for acute stroke. However, they also required the longest training time. With high grade of evidence and low number of participants (N = 8), assistive control strategies that followed a threshold-based algorithm with EMG as gait detection metric and control signal provided the highest improvements with the lowest training intensities for subacute stroke. Finally, with high grade of evidence and a moderate number of participants (N = 19), assistive control strategies that implemented adaptive oscillator algorithms together with trajectory-tracking control resulted in the highest improvements with reduced training intensities for individuals with chronic stroke. CONCLUSIONS Despite the efforts to develop novel and more effective controllers for exoskeleton-based gait neurorehabilitation, the current level of evidence on the effectiveness of the different control strategies on clinical outcomes is still low. There is a clear lack of standardization in the experimental protocols leading to high levels of heterogeneity. Standardized comparisons among control strategies analyzing the relation between control parameters and biomechanical metrics will fill this gap to better guide future technical developments. It is still an open question whether controllers that provide an on-line adaptation of the control parameters based on key biomechanical descriptors associated to the patients' specific pathology outperform current control strategies.
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Affiliation(s)
- Jesús de Miguel-Fernández
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Diagonal 647, 08028 Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Santa Rosa 39-57, 08950 Esplugues de Llobregat, Spain
| | | | - Erik Prinsen
- Roessingh Research and Development, Roessinghsbleekweg 33b, 7522AH Enschede, Netherlands
| | - Josep M. Font-Llagunes
- Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Politècnica de Catalunya, Diagonal 647, 08028 Barcelona, Spain
- Institut de Recerca Sant Joan de Déu, Santa Rosa 39-57, 08950 Esplugues de Llobregat, Spain
| | - Laura Marchal-Crespo
- Cognitive Robotics Department, Delft University of Technology, Mekelweg 2, 2628 Delft, Netherlands
- Motor Learning and Neurorehabilitation Lab, ARTORG Center for Biomedical Engineering Research, University of Bern, Freiburgstrasse 3, 3010 Bern, Switzerland
- Department of Rehabilitation Medicine, Erasmus MC University Medical Center, Doctor Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
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Measurement, Evaluation, and Control of Active Intelligent Gait Training Systems—Analysis of the Current State of the Art. ELECTRONICS 2022. [DOI: 10.3390/electronics11101633] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Gait recognition and rehabilitation has been a research hotspot in recent years due to its importance to medical care and elderly care. Active intelligent rehabilitation and assistance systems for lower limbs integrates mechanical design, sensing technology, intelligent control, and robotics technology, and is one of the effective ways to resolve the above problems. In this review, crucial technologies and typical prototypes of active intelligent rehabilitation and assistance systems for gait training are introduced. The limitations, challenges, and future directions in terms of gait measurement and intention recognition, gait rehabilitation evaluation, and gait training control strategies are discussed. To address the core problems of the sensing, evaluation and control technology of the active intelligent gait training systems, the possible future research directions are proposed. Firstly, different sensing methods need to be proposed for the decoding of human movement intention. Secondly, the human walking ability evaluation models will be developed by integrating the clinical knowledge and lower limb movement data. Lastly, the personalized gait training strategy for collaborative control of human–machine systems needs to be implemented in the clinical applications.
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Li X, He B, Deng Z, Chen Y, Wang D, Fan Y, Su H, Yu H. A Center of Mass Estimation and Control Strategy for Body-Weight-Support Treadmill Training. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2388-2398. [PMID: 34748495 DOI: 10.1109/tnsre.2021.3126104] [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: 11/10/2022]
Abstract
Walking disorders are common in post-stroke. Body weight support (BWS) systems have been proposed and proven to enhance gait training systems for recovering in individuals with hemiplegia. However, the fixed weight support and walking speed increase the risk of falling and decrease the active participation of the subjects. This paper proposes a strategy to enhance the efficiency of BWS treadmill training. It consists in regulating the height of the BWS system to track the height of the subject's center of mass (CoM), whereby the CoM is estimated through a long-short term memory (LSTM) network and a locomotion recognition system. The LSTM network takes the walking speed, body-height to leg-length ratio, hip and knee joint angles of the hemiplegic subjects' non-paretic side from the locomotion recognition system as input signals and outputs the CoM height to a BWS treadmill training robot. Besides, the hip and knee joints' ranges of motion are increased by 34.54% and 25.64% under the CoM height regulation compared to the constant weight support, respectively. With the CoM height regulation strategy, the stance phase duration of the paretic side is significantly increased by 14.6% of the gait cycle, and the symmetry of the gait is also promoted. The CoM height kinematics by adjustment strategy is in good agreement with the mean values of the 14 non-disabled subjects, which demonstrated that the adjustment strategy improves the stability of CoM height during the training.
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Laidig D, Jocham AJ, Guggenberger B, Adamer K, Fischer M, Seel T. Calibration-Free Gait Assessment by Foot-Worn Inertial Sensors. Front Digit Health 2021; 3:736418. [PMID: 34806077 PMCID: PMC8599134 DOI: 10.3389/fdgth.2021.736418] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 09/24/2021] [Indexed: 02/01/2023] Open
Abstract
Walking is a central activity of daily life, and there is an increasing demand for objective measurement-based gait assessment. In contrast to stationary systems, wearable inertial measurement units (IMUs) have the potential to enable non-restrictive and accurate gait assessment in daily life. We propose a set of algorithms that uses the measurements of two foot-worn IMUs to determine major spatiotemporal gait parameters that are essential for clinical gait assessment: durations of five gait phases for each side as well as stride length, walking speed, and cadence. Compared to many existing methods, the proposed algorithms neither require magnetometers nor a precise mounting of the sensor or dedicated calibration movements. They are therefore suitable for unsupervised use by non-experts in indoor as well as outdoor environments. While previously proposed methods are rarely validated in pathological gait, we evaluate the accuracy of the proposed algorithms on a very broad dataset consisting of 215 trials and three different subject groups walking on a treadmill: healthy subjects (n = 39), walking at three different speeds, as well as orthopedic (n = 62) and neurological (n = 36) patients, walking at a self-selected speed. The results show a very strong correlation of all gait parameters (Pearson's r between 0.83 and 0.99, p < 0.01) between the IMU system and the reference system. The mean absolute difference (MAD) is 1.4 % for the gait phase durations, 1.7 cm for the stride length, 0.04 km/h for the walking speed, and 0.7 steps/min for the cadence. We show that the proposed methods achieve high accuracy not only for a large range of walking speeds but also in pathological gait as it occurs in orthopedic and neurological diseases. In contrast to all previous research, we present calibration-free methods for the estimation of gait phases and spatiotemporal parameters and validate them in a large number of patients with different pathologies. The proposed methods lay the foundation for ubiquitous unsupervised gait assessment in daily-life environments.
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Affiliation(s)
- Daniel Laidig
- Control Systems Group, Technische Universität Berlin, Berlin, Germany
| | - Andreas J. Jocham
- Institute of Physiotherapy, FH JOANNEUM University of Applied Sciences, Graz, Austria
| | - Bernhard Guggenberger
- Institute of Physiotherapy, FH JOANNEUM University of Applied Sciences, Graz, Austria
| | - Klemens Adamer
- Vamed Rehabilitation Center Kitzbuehel, Kitzbuehel, Austria
| | - Michael Fischer
- Vamed Rehabilitation Center Kitzbuehel, Kitzbuehel, Austria
- Ludwig Boltzmann Institute for Rehabilitation Research, Vienna, Austria
- Hannover Medical School MHH, Clinic for Rehabilitation Medicine, Hannover, Germany
| | - Thomas Seel
- Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Nüesch C, Ismailidis P, Koch D, Pagenstert G, Ilchmann T, Eckardt A, Stoffel K, Egloff C, Mündermann A. Assessing Site Specificity of Osteoarthritic Gait Kinematics with Wearable Sensors and Their Association with Patient Reported Outcome Measures (PROMs): Knee versus Hip Osteoarthritis. SENSORS 2021; 21:s21165363. [PMID: 34450828 PMCID: PMC8398113 DOI: 10.3390/s21165363] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/05/2021] [Accepted: 08/06/2021] [Indexed: 12/13/2022]
Abstract
There is a great need for quantitative outcomes reflecting the functional status in patients with knee or hip osteoarthritis (OA) to advance the development and investigation of interventions for OA. The purpose of this study was to determine if gait kinematics specific to the disease—i.e., knee versus hip OA—can be identified using wearable sensors and statistical parametric mapping (SPM) and whether disease-related gait deviations are associated with patient reported outcome measures. 113 participants (N = 29 unilateral knee OA; N = 30 unilateral hip OA; N = 54 age-matched asymptomatic persons) completed gait analysis with wearable sensors and the Knee/Hip Osteoarthritis Outcome Score (KOOS/HOOS). Data were analyzed using SPM. Knee and hip kinematics differed between patients with knee OA and patients with hip OA (up to 14°, p < 0.001 for knee and 8°, p = 0.003 for hip kinematics), and differences from controls were more pronounced in the affected than unaffected leg of patients. The observed deviations in ankle, knee and hip kinematic trajectories from controls were associated with KOOS/HOOS in both groups. Capturing gait kinematics using wearables has a large potential for application as outcome in clinical trials and for monitoring treatment success in patients with knee or hip OA and in large cohorts representing a major advancement in research on musculoskeletal diseases.
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Affiliation(s)
- Corina Nüesch
- Department of Orthopaedics and Traumatology, University Hospital Basel, 4031 Basel, Switzerland; (C.N.); (P.I.); (D.K.); (K.S.); (C.E.)
- Department of Spine Surgery, University Hospital Basel, 4031 Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland
- Department of Clinical Research, University of Basel, 4031 Basel, Switzerland;
| | - Petros Ismailidis
- Department of Orthopaedics and Traumatology, University Hospital Basel, 4031 Basel, Switzerland; (C.N.); (P.I.); (D.K.); (K.S.); (C.E.)
- Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland
- Department of Clinical Research, University of Basel, 4031 Basel, Switzerland;
| | - David Koch
- Department of Orthopaedics and Traumatology, University Hospital Basel, 4031 Basel, Switzerland; (C.N.); (P.I.); (D.K.); (K.S.); (C.E.)
- Department for Sport, Movement and Health, University of Basel, 4052 Basel, Switzerland
| | - Geert Pagenstert
- Department of Clinical Research, University of Basel, 4031 Basel, Switzerland;
- Clarahof Clinic of Orthopaedic Surgery, 4058 Basel, Switzerland
| | - Thomas Ilchmann
- ENDO-Team, Hirslanden Klinik Birshof, 4142 Münchenstein, Switzerland; (T.I.); (A.E.)
| | - Anke Eckardt
- ENDO-Team, Hirslanden Klinik Birshof, 4142 Münchenstein, Switzerland; (T.I.); (A.E.)
| | - Karl Stoffel
- Department of Orthopaedics and Traumatology, University Hospital Basel, 4031 Basel, Switzerland; (C.N.); (P.I.); (D.K.); (K.S.); (C.E.)
| | - Christian Egloff
- Department of Orthopaedics and Traumatology, University Hospital Basel, 4031 Basel, Switzerland; (C.N.); (P.I.); (D.K.); (K.S.); (C.E.)
| | - Annegret Mündermann
- Department of Orthopaedics and Traumatology, University Hospital Basel, 4031 Basel, Switzerland; (C.N.); (P.I.); (D.K.); (K.S.); (C.E.)
- Department of Spine Surgery, University Hospital Basel, 4031 Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland
- Department of Clinical Research, University of Basel, 4031 Basel, Switzerland;
- Correspondence:
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6
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Perez-Ibarra JC, Siqueira AAG, Krebs HI. Identification of Gait Events in Healthy Subjects and With Parkinson's Disease Using Inertial Sensors: An Adaptive Unsupervised Learning Approach. IEEE Trans Neural Syst Rehabil Eng 2020; 28:2933-2943. [PMID: 33237863 DOI: 10.1109/tnsre.2020.3039999] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Automatic identification of gait events is an essential component of the control scheme of assistive robotic devices. Many available techniques suffer limitations for real-time implementations and in guaranteeing high performances when identifying events in subjects with gait impairments. Machine learning algorithms offer a solution by enabling the training of different models to represent the gait patterns of different subjects. Here our aim is twofold: to remove the need for training stages using unsupervised learning, and to modify the parameters according to the changes within a walking trial using adaptive procedures. We developed two adaptive unsupervised algorithms for real-time detection of four gait events, using only signals from two single-IMU foot-mounted wearable devices. We evaluated the algorithms using data collected from five healthy adults and seven subjects with Parkinson's disease (PD) walking overground and on a treadmill. Both algorithms obtained high performance in terms of accuracy ( F1 -score ≥ 0.95 for both groups), and timing agreement using a force-sensitive resistors as reference (mean absolute differences of 66 ± 53 msec for the healthy group, and 58 ± 63 msec for the PD group). The proposed algorithms demonstrated the potential to learn optimal parameters for a particular participant and for detecting gait events without additional sensors, external labeling, or long training stages.
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7
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Seel T, Kok M, McGinnis RS. Inertial Sensors-Applications and Challenges in a Nutshell. SENSORS 2020; 20:s20216221. [PMID: 33142738 PMCID: PMC7662337 DOI: 10.3390/s20216221] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 10/29/2020] [Indexed: 12/26/2022]
Abstract
This editorial provides a concise introduction to the methods and applications of inertial sensors. We briefly describe the main characteristics of inertial sensors and highlight the broad range of applications as well as the methodological challenges. Finally, for the reader’s guidance, we give a succinct overview of the papers included in this special issue.
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Affiliation(s)
- Thomas Seel
- Control Systems Group, Technische Universität Berlin, 10587 Berlin, Germany
- Correspondence:
| | - Manon Kok
- Delft Center for Systems and Control, Delft University of Technology, 2628 CD Delft, The Netherlands;
| | - Ryan S. McGinnis
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT 05405, USA;
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Ye J, Wu H, Wu L, Long J, Zhang Y, Chen G, Wang C, Luo X, Hou Q, Xu Y. An Adaptive Method for Gait Event Detection of Gait Rehabilitation Robots. Front Neurorobot 2020; 14:38. [PMID: 32903323 PMCID: PMC7396541 DOI: 10.3389/fnbot.2020.00038] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Accepted: 05/20/2020] [Indexed: 11/13/2022] Open
Abstract
Accurate gait event detection is necessary for control strategies of gait rehabilitation robots. However, due to personal diversity between individuals, it is a challenge for robots to detect a gait event at various stride frequencies. This paper proposes a novel method for gait event detection of a gait rehabilitation robot using a single inertial sensor mounted on the thigh. A self-adaptive threshold for detecting heel strike is obtained in real time via a linear regression model. Observable thresholds for toe off detection are constant at various stride frequencies. Experiments are conducted based on 20 healthy subjects and six hemiplegic patients wearing a gait rehabilitation robot and walking at various kinds of stride frequencies. The experimental results show that the proposed method can detect heel strike and toe off gait events within an average 2% gait cycle temporal errors and never miss two-gait event detection. Compared to the conventional thresholding method, this work presents a simple and robust application for gait event detection in healthy and hemiplegic subjects by one inertial sensor. The linear regression model can be applicable to different subjects walking at various stride frequencies.
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Affiliation(s)
- Jing Ye
- Shenzhen MileBot Robotics Co., Ltd., Shenzhen, China.,Shenzhen Institute of Geriatrics, Shenzhen, China
| | - Hongde Wu
- Shenzhen MileBot Robotics Co., Ltd., Shenzhen, China
| | - Lishan Wu
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Jianjun Long
- Rehabilitation Center, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Yuling Zhang
- Stroke Biological Recovery Laboratory, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, United States.,School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Gong Chen
- Shenzhen MileBot Robotics Co., Ltd., Shenzhen, China.,Shenzhen Institute of Geriatrics, Shenzhen, China
| | - Chunbao Wang
- Shenzhen Institute of Geriatrics, Shenzhen, China
| | - Xun Luo
- Kerry Rehabilitation Medicine Research Institute, Shenzhen, China.,Shenzhen Sanming Project Group, Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, United States.,Shenzhen Dapeng New District Nan'ao People's Hospital, Shenzhen, China
| | - Qinghua Hou
- Clinical Neuroscience Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Yi Xu
- Department of Rehabilitation Medicine, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
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Schicketmueller A, Lamprecht J, Hofmann M, Sailer M, Rose G. Gait Event Detection for Stroke Patients during Robot-Assisted Gait Training. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3399. [PMID: 32560256 PMCID: PMC7349052 DOI: 10.3390/s20123399] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 06/09/2020] [Accepted: 06/15/2020] [Indexed: 11/16/2022]
Abstract
Functional electrical stimulation and robot-assisted gait training are techniques which are used in a clinical routine to enhance the rehabilitation process of stroke patients. By combining these technologies, therapy effects could be further improved and the rehabilitation process can be supported. In order to combine these technologies, a novel algorithm was developed, which aims to extract gait events based on movement data recorded with inertial measurement units. In perspective, the extracted gait events can be used to trigger functional electrical stimulation during robot-assisted gait training. This approach offers the possibility of equipping a broad range of potential robot-assisted gait trainers with functional electrical stimulation. In particular, the aim of this study was to test the robustness of the previously developed algorithm in a clinical setting with patients who suffered a stroke. A total amount of N = 10 stroke patients participated in the study, with written consent. The patients were assigned to two different robot-assisted gait trainers (Lyra and Lokomat) according to their performance level, resulting in five recording sessions for each gait-trainer. A previously developed algorithm was applied and further optimized in order to extract the gait events. A mean detection rate across all patients of 95.8% ± 7.5% for the Lyra and 98.7% ± 2.6% for the Lokomat was achieved. The mean type 1 error across all patients was 1.0% ± 2.0% for the Lyra and 0.9% ± 2.3% for the Lokomat. As a result, the developed algorithm was robust against patient specific movements, and provided promising results for the further development of a technique that can detect gait events during robot-assisted gait training, with the future aim to trigger functional electrical stimulation.
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Affiliation(s)
- Andreas Schicketmueller
- HASOMED GmbH, Paul-Ecke-Str. 1, 39114 Magdeburg, Germany;
- Institute for Medical Engineering and Research Campus STIMULATE, University of Magdeburg, Universitaetsplatz 2, 39106 Magdeburg, Germany;
| | - Juliane Lamprecht
- MEDIAN Neurological Rehabilitation Center Magdeburg, Gustav-Ricker-Str. 4, 39120 Magdeburg, Germany; (J.L.); (M.S.)
- Institute for Neurorehabilitation, Affiliated Institute of the Otto-von-Guericke University, Gustav-Ricker-Str. 4, 39120 Magdeburg, Germany
| | - Marc Hofmann
- HASOMED GmbH, Paul-Ecke-Str. 1, 39114 Magdeburg, Germany;
| | - Michael Sailer
- MEDIAN Neurological Rehabilitation Center Magdeburg, Gustav-Ricker-Str. 4, 39120 Magdeburg, Germany; (J.L.); (M.S.)
- Institute for Neurorehabilitation, Affiliated Institute of the Otto-von-Guericke University, Gustav-Ricker-Str. 4, 39120 Magdeburg, Germany
- MEDIAN Clinic Flechtingen, Parkstraße, 39345 Flechtingen, Germany
| | - Georg Rose
- Institute for Medical Engineering and Research Campus STIMULATE, University of Magdeburg, Universitaetsplatz 2, 39106 Magdeburg, Germany;
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Towards Wearable-Inertial-Sensor-Based Gait Posture Evaluation for Subjects with Unbalanced Gaits. SENSORS 2020; 20:s20041193. [PMID: 32098239 PMCID: PMC7070249 DOI: 10.3390/s20041193] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 02/14/2020] [Accepted: 02/18/2020] [Indexed: 11/17/2022]
Abstract
Human gait reflects health condition and is widely adopted as a diagnostic basisin clinical practice. This research adopts compact inertial sensor nodes to monitor the functionof human lower limbs, which implies the most fundamental locomotion ability. The proposedwearable gait analysis system captures limb motion and reconstructs 3D models with high accuracy.It can output the kinematic parameters of joint flexion and extension, as well as the displacementdata of human limbs. The experimental results provide strong support for quick access to accuratehuman gait data. This paper aims to provide a clue for how to learn more about gait postureand how wearable gait analysis can enhance clinical outcomes. With an ever-expanding gait database,it is possible to help physiotherapists to quickly discover the causes of abnormal gaits, sports injuryrisks, and chronic pain, and provides guidance for arranging personalized rehabilitation programsfor patients. The proposed framework may eventually become a useful tool for continually monitoringspatio-temporal gait parameters and decision-making in an ambulatory environment.
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Beuchert J, Solowjow F, Trimpe S, Seel T. Overcoming Bandwidth Limitations in Wireless Sensor Networks by Exploitation of Cyclic Signal Patterns: An Event-triggered Learning Approach. SENSORS (BASEL, SWITZERLAND) 2020; 20:E260. [PMID: 31906455 PMCID: PMC6983078 DOI: 10.3390/s20010260] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 12/23/2019] [Accepted: 12/30/2019] [Indexed: 11/25/2022]
Abstract
Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors. This problem can be addressed by reducing the communication load via data compression and event-based communication approaches. The present paper focuses on the class of applications in which the signals exhibit unknown and potentially time-varying cyclic patterns. We review recently proposed event-triggered learning (ETL) methods that identify and exploit these cyclic patterns, we show how these methods can be applied to the nonlinear multivariable dynamics of three-dimensional orientation data, and we propose a novel approach that uses Gaussian process models. In contrast to other approaches, all three ETL methods work in real time and assure a small upper bound on the reconstruction error. The proposed methods are compared to several conventional approaches in experimental data from human subjects walking with a wearable inertial sensor network. They are found to reduce the communication load by 60-70%, which implies that two to three times more sensor nodes could be used at the same bandwidth.
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Affiliation(s)
- Jonas Beuchert
- Department of Engineering Science, University of Oxford, Oxford OX1 2JD, UK
| | - Friedrich Solowjow
- Intelligent Control Systems Group, Max Planck Institute for Intelligent Systems, 70569 Stuttgart, Germany; (F.S.); (S.T.)
| | - Sebastian Trimpe
- Intelligent Control Systems Group, Max Planck Institute for Intelligent Systems, 70569 Stuttgart, Germany; (F.S.); (S.T.)
| | - Thomas Seel
- Control Systems Group, Technische Universität Berlin, 10587 Berlin, Germany;
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