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Astrella A, Iordanov D, De Caro D, Jiménez-Reyes P, Mendiguchia J. Biceps femoris muscle-tendon strain during an entire overground sprint acceleration: a biomechanical explanation for hamstring injuries in the acceleration phase. Sports Biomech 2024:1-14. [PMID: 38739086 DOI: 10.1080/14763141.2024.2353233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 05/02/2024] [Indexed: 05/14/2024]
Abstract
The objectives of this study were to analyse the peak muscle-tendon (MT) strain of the hamstring during an entire acceleration sprint overground and examine their relationship with relative joint angles and segment orientation in the sagittal plane, which are the direct causes of MT strain. Kinematic data were recorded using a 3D inertial motion capture system in 21 male semi-professional soccer players during 40-metre overground sprint. Scaled musculoskeletal models were used to estimate peak MT strain in the hamstring over 16 steps. Biceps femoris long head (BFLH) exhibited the largest peaks in MT strain compared to semitendinosus (ST) and semimembranosus (SM) muscles across all the steps, with its overall strain decreased as the number of steps and maximum speed increased. Hip flexion angle was found to be a strong predictor (p < 0.001) of joint angles, being the orientation of the pelvis in the sagittal plane of the segment with the greatest influence (p < 0.001) on the peak MT strain of BFLH during sprinting. The current study provides a biomechanical explanation for the high proportion of hamstring injuries in the acceleration phase of sprinting.
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Affiliation(s)
- Andrea Astrella
- International Doctoral School, Rey Juan Carlos University, Madrid, Spain
- Department of Muscle Science, RX2 Sports & Health, Madrid, Spain
| | - Daniel Iordanov
- Sports Department, Universidad Politecnica de Madrid, Madrid, Spain
| | - Dario De Caro
- Department of Muscle Science, RX2 Sports & Health, Madrid, Spain
| | | | - Jurdan Mendiguchia
- Department of Muscle Science, RX2 Sports & Health, Madrid, Spain
- Department of Physical Therapy, ZENTRUM Rehab and Performance Center, Barañain, Spain
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2
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David JP, Schick D, Rapp L, Schick J, Glaser M. SensAA-Design and Verification of a Cloud-Based Wearable Biomechanical Data Acquisition System. SENSORS (BASEL, SWITZERLAND) 2024; 24:2405. [PMID: 38676022 PMCID: PMC11053589 DOI: 10.3390/s24082405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 03/29/2024] [Accepted: 04/04/2024] [Indexed: 04/28/2024]
Abstract
Exoskeletons designed to assist patients with activities of daily living are becoming increasingly popular, but still are subject to research. In order to gather requirements for the design of such systems, long-term gait observation of the patients over the course of multiple days in an environment of daily living are required. In this paper a wearable all-in-one data acquisition system for collecting and storing biomechanical data in everyday life is proposed. The system is designed to be cost efficient and easy to use, using off-the-shelf components and a cloud server system for centralized data storage. The measurement accuracy of the system was verified, by measuring the angle of the human knee joint at walking speeds between 3 and 12 km/h in reference to an optical motion analysis system. The acquired data were uploaded to a cloud database via a smartphone application. Verification results showed that the proposed toolchain works as desired. The system reached an RMSE from 2.9° to 8°, which is below that of most comparable systems. The system provides a powerful, scalable platform for collecting and processing biomechanical data, which can help to automize the generation of an extensive database for human kinematics.
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Affiliation(s)
| | | | | | | | - Markus Glaser
- Zentrum für Zuverlässige Mechatronische Systeme (ZMS), Aalen University, 73430 Aalen, Germany
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3
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Horsak B, Eichmann A, Lauer K, Prock K, Krondorfer P, Siragy T, Dumphart B. Concurrent validity of smartphone-based markerless motion capturing to quantify lower-limb joint kinematics in healthy and pathological gait. J Biomech 2023; 159:111801. [PMID: 37738945 DOI: 10.1016/j.jbiomech.2023.111801] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/24/2023] [Accepted: 09/12/2023] [Indexed: 09/24/2023]
Abstract
Markerless motion capturing has the potential to provide a low-cost and accessible alternative to traditional marker-based systems for real-world biomechanical assessment. However, before these systems can be put into practice, we need to rigorously evaluate their accuracy in estimating joint kinematics for various gait patterns. This study evaluated the accuracy of a low-cost, open-source, and smartphone-based markerless motion capture system, namely OpenCap, for measuring 3D joint kinematics in healthy and pathological gait compared to a marker-based system. 21 healthy volunteers were instructed to walk with four different gait patterns: physiological, crouch, circumduction, and equinus gait. Three-dimensional kinematic data were simultaneously recorded using the markerless and a marker-based motion capture system. The root mean square error (RMSE) and the peak error were calculated between every joint kinematic variable obtained by both systems. We found an overall RMSE of 5.8 (SD: 1.8 degrees) and a peak error of 11.3 degrees (SD: 3.9). A repeated measures ANOVA with post hoc tests indicated significant differences in RMSE and peak errors between the four gait patterns (p ¡ 0.05). Physiological gait presented the lowest, crouch and circumduction gait the highest errors. Our findings indicate a roughly comparable accuracy to IMU-based approaches and commercial markerless multi-camera solutions. However, errors are still above clinically desirable thresholds of two to five degrees. While our findings highlight the potential of markerless systems for assessing gait kinematics, they also underpin the need to further improve the underlying deep learning algorithms to make markerless pose estimation a valuable tool in clinical settings.
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Affiliation(s)
- Brian Horsak
- Center for Digital Health and Social Innovation, St. Pölten University of Applied Sciences, Campus-Platz 1, St. Pölten, 3100, Austria; Institute of Health Sciences, St. Pölten University of Applied Sciences, Campus-Platz 1, St. Pölten, 3100, Austria.
| | - Anna Eichmann
- Study Program Gait Analysis and Rehabilitation, St. Pölten University of Applied Sciences, Campus-Platz 1, St. Pölten, 3100, Austria
| | - Kerstin Lauer
- Study Program Gait Analysis and Rehabilitation, St. Pölten University of Applied Sciences, Campus-Platz 1, St. Pölten, 3100, Austria
| | - Kerstin Prock
- Center for Digital Health and Social Innovation, St. Pölten University of Applied Sciences, Campus-Platz 1, St. Pölten, 3100, Austria
| | - Philipp Krondorfer
- Center for Digital Health and Social Innovation, St. Pölten University of Applied Sciences, Campus-Platz 1, St. Pölten, 3100, Austria
| | - Tarique Siragy
- Center for Digital Health and Social Innovation, St. Pölten University of Applied Sciences, Campus-Platz 1, St. Pölten, 3100, Austria
| | - Bernhard Dumphart
- Center for Digital Health and Social Innovation, St. Pölten University of Applied Sciences, Campus-Platz 1, St. Pölten, 3100, Austria; Institute of Health Sciences, St. Pölten University of Applied Sciences, Campus-Platz 1, St. Pölten, 3100, Austria
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4
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Di Raimondo G, Willems M, Killen BA, Havashinezhadian S, Turcot K, Vanwanseele B, Jonkers I. Peak Tibiofemoral Contact Forces Estimated Using IMU-Based Approaches Are Not Significantly Different from Motion Capture-Based Estimations in Patients with Knee Osteoarthritis. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094484. [PMID: 37177688 PMCID: PMC10181595 DOI: 10.3390/s23094484] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/01/2023] [Accepted: 05/03/2023] [Indexed: 05/15/2023]
Abstract
Altered tibiofemoral contact forces represent a risk factor for osteoarthritis onset and progression, making optimization of the knee force distribution a target of treatment strategies. Musculoskeletal model-based simulations are a state-of-the-art method to estimate joint contact forces, but they typically require laboratory-based input and skilled operators. To overcome these limitations, ambulatory methods, relying on inertial measurement units, have been proposed to estimated ground reaction forces and, consequently, knee contact forces out-of-the-lab. This study proposes the use of a full inertial-capture-based musculoskeletal modelling workflow with an underlying probabilistic principal component analysis model trained on 1787 gait cycles in patients with knee osteoarthritis. As validation, five patients with knee osteoarthritis were instrumented with 17 inertial measurement units and 76 opto-reflective markers. Participants performed multiple overground walking trials while motion and inertial capture methods were synchronously recorded. Moderate to strong correlations were found for the inertial capture-based knee contact forces compared to motion capture with root mean square error between 0.15 and 0.40 of body weight. The results show that our workflow can inform and potentially assist clinical practitioners to monitor knee joint loading in physical therapy sessions and eventually assess long-term therapeutic effects in a clinical context.
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Affiliation(s)
- Giacomo Di Raimondo
- Department of Movement Sciences, Katholieke Universiteit Leuven, 3001 Heverlee, Belgium
| | - Miel Willems
- Department of Movement Sciences, Katholieke Universiteit Leuven, 3001 Heverlee, Belgium
| | - Bryce Adrian Killen
- Department of Movement Sciences, Katholieke Universiteit Leuven, 3001 Heverlee, Belgium
| | | | - Katia Turcot
- Department of Kinesiology, Université Laval, Québec, QC G1V 0A6, Canada
| | - Benedicte Vanwanseele
- Department of Movement Sciences, Katholieke Universiteit Leuven, 3001 Heverlee, Belgium
| | - Ilse Jonkers
- Department of Movement Sciences, Katholieke Universiteit Leuven, 3001 Heverlee, Belgium
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Dasgupta A, Sharma R, Mishra C, Nagaraja VH. Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data. Bioengineering (Basel) 2023; 10:bioengineering10050510. [PMID: 37237580 DOI: 10.3390/bioengineering10050510] [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: 03/18/2023] [Revised: 04/16/2023] [Accepted: 04/21/2023] [Indexed: 05/28/2023] Open
Abstract
Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into muscle and joint loading at an in vivo level, aiding clinical decision-making. However, an OMC system is lab-based, expensive, and requires a line of sight. Inertial Motion Capture (IMC) techniques are widely-used alternatives, which are portable, user-friendly, and relatively low-cost, although with lesser accuracy. Irrespective of the choice of motion capture technique, one typically uses an MSK model to obtain the kinematic and kinetic outputs, which is a computationally expensive tool increasingly well approximated by machine learning (ML) methods. Here, an ML approach is presented that maps experimentally recorded IMC input data to the human upper-extremity MSK model outputs computed from ('gold standard') OMC input data. Essentially, this proof-of-concept study aims to predict higher-quality MSK outputs from the much easier-to-obtain IMC data. We use OMC and IMC data simultaneously collected for the same subjects to train different ML architectures that predict OMC-driven MSK outputs from IMC measurements. In particular, we employed various neural network (NN) architectures, such as Feed-Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs) (vanilla, Long Short-Term Memory, and Gated Recurrent Unit) and a comprehensive search for the best-fit model in the hyperparameters space in both subject-exposed (SE) as well as subject-naive (SN) settings. We observed a comparable performance for both FFNN and RNN models, which have a high degree of agreement (ravg,SE,FFNN=0.90±0.19, ravg,SE,RNN=0.89±0.17, ravg,SN,FFNN=0.84±0.23, and ravg,SN,RNN=0.78±0.23) with the desired OMC-driven MSK estimates for held-out test data. The findings demonstrate that mapping IMC inputs to OMC-driven MSK outputs using ML models could be instrumental in transitioning MSK modelling from 'lab to field'.
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Affiliation(s)
- Abhishek Dasgupta
- Doctoral Training Centre, University of Oxford, 1-4 Keble Road, Oxford OX1 3NP, UK
| | - Rahul Sharma
- Laboratory for Computation and Visualization in Mathematics and Mechanics, Institute of Mathematics, Swiss Federal Institute of Technology Lausanne, 1015 Lausanne, Switzerland
| | - Challenger Mishra
- Department of Computer Science & Technology, University of Cambridge, 15 J.J. Thomson Ave., Cambridge CB3 0FD, UK
| | - Vikranth Harthikote Nagaraja
- Natural Interaction Laboratory, Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Old Road Campus Research Building, Oxford OX3 7DQ, UK
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Lloyd DG, Saxby DJ, Pizzolato C, Worsey M, Diamond LE, Palipana D, Bourne M, de Sousa AC, Mannan MMN, Nasseri A, Perevoshchikova N, Maharaj J, Crossley C, Quinn A, Mulholland K, Collings T, Xia Z, Cornish B, Devaprakash D, Lenton G, Barrett RS. Maintaining soldier musculoskeletal health using personalised digital humans, wearables and/or computer vision. J Sci Med Sport 2023:S1440-2440(23)00070-1. [PMID: 37149408 DOI: 10.1016/j.jsams.2023.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 03/27/2023] [Accepted: 04/05/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVES The physical demands of military service place soldiers at risk of musculoskeletal injuries and are major concerns for military capability. This paper outlines the development new training technologies to prevent and manage these injuries. DESIGN Narrative review. METHODS Technologies suitable for integration into next-generation training devices were examined. We considered the capability of technologies to target tissue level mechanics, provide appropriate real-time feedback, and their useability in-the-field. RESULTS Musculoskeletal tissues' health depends on their functional mechanical environment experienced in military activities, training and rehabilitation. These environments result from the interactions between tissue motion, loading, biology, and morphology. Maintaining health of and/or repairing joint tissues requires targeting the "ideal" in vivo tissue mechanics (i.e., loading and strain), which may be enabled by real-time biofeedback. Recent research has shown that these biofeedback technologies are possible by integrating a patient's personalised digital twin and wireless wearable devices. Personalised digital twins are personalised neuromusculoskeletal rigid body and finite element models that work in real-time by code optimisation and artificial intelligence. Model personalisation is crucial in obtaining physically and physiologically valid predictions. CONCLUSIONS Recent work has shown that laboratory-quality biomechanical measurements and modelling can be performed outside the laboratory with a small number of wearable sensors or computer vision methods. The next stage is to combine these technologies into well-designed easy to use products.
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Affiliation(s)
- David G Lloyd
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia.
| | - David J Saxby
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Claudio Pizzolato
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Matthew Worsey
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Laura E Diamond
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Dinesh Palipana
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Medicine, Dentistry and Health, Griffith University, Australia
| | - Matthew Bourne
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Ana Cardoso de Sousa
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Malik Muhammad Naeem Mannan
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Azadeh Nasseri
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Nataliya Perevoshchikova
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Jayishni Maharaj
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Claire Crossley
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Alastair Quinn
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Kyle Mulholland
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Tyler Collings
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Zhengliang Xia
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia
| | - Bradley Cornish
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
| | - Daniel Devaprakash
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; VALD Performance, Australia
| | | | - Rodney S Barrett
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and Advanced Design and Prototyping Technologies Institute, Australia; School of Health Sciences and Social Work, Griffith University, Australia
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Koska D, Oriwol D, Maiwald C. Comparison of statistical models for characterizing continuous differences between two biomechanical measurement systems. J Biomech 2023; 149:111506. [PMID: 36806004 DOI: 10.1016/j.jbiomech.2023.111506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 02/08/2023] [Accepted: 02/13/2023] [Indexed: 02/17/2023]
Abstract
Most biomechanical processes are continuous in nature. Measurement systems record this continuous behavior as curve data, which is often treated inappropriately in validation studies. The current paper compares different statistical models for analyzing the agreement of curves from two measurement systems. All models were evaluated in various error scenarios (simulated and real-world data). Excellent results were obtained using a functional method, with coverage probabilities close to the desired level in all data sets. Pointwise constructed bands had a lower coverage probability, but still contained most of the curve points and may thus be an option in scenarios where assumptions of functional models are violated (e.g., when curves are much noisier than those presented here, or in the presence of drift). Models that account for within-subject variation showed a higher coverage probability and less uncertainty about the variation of band limits. We hope this study, along with the provided research code, will inspire researchers to use methods for curve data more frequently and appropriately.
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Affiliation(s)
- Daniel Koska
- Chemnitz University of Technology, Thüringer Weg 11, 09126 Chemnitz, Germany.
| | - Doris Oriwol
- Chemnitz University of Technology, Thüringer Weg 11, 09126 Chemnitz, Germany
| | - Christian Maiwald
- Chemnitz University of Technology, Thüringer Weg 11, 09126 Chemnitz, Germany
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8
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Demeco A, Frizziero A, Nuresi C, Buccino G, Pisani F, Martini C, Foresti R, Costantino C. Gait Alteration in Individual with Limb Loss: The Role of Inertial Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:1880. [PMID: 36850475 PMCID: PMC9964846 DOI: 10.3390/s23041880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/01/2023] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
Amputation has a big impact on the functioning of patients, with negative effects on locomotion and dexterity. In this context, inertial measurement units represent a useful tool in clinical practice for motion analysis, and in the development of personalized aids to improve a patient's function. To date, there is still a gap of knowledge in the scientific literature on the application of inertial sensors in amputee patients. Thus, the aim of this narrative review was to collect the current knowledge on this topic and stimulate the publication of further research. Pubmed, Embase, Scopus, and Cochrane Library publications were screened until November 2022 to identify eligible studies. Out of 444 results, we selected 26 articles focused on movement analysis, risk of falls, energy expenditure, and the development of sensor-integrated prostheses. The results showed that the use of inertial sensors has the potential to improve the quality of life of patients with prostheses, increasing patient safety through the detection of gait alteration; enhancing the socio-occupational reintegration through the development of highly technologic and personalized prosthesis; and by monitoring the patients during daily life to plan a tailored rehabilitation program.
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Affiliation(s)
- Andrea Demeco
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
| | - Antonio Frizziero
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
| | - Christian Nuresi
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
| | - Giovanni Buccino
- Division of Neuroscience, IRCCS San Raffaele, University Vita-Salute San Raffaele, 20132 Milan, Italy
| | - Francesco Pisani
- Department of Human Neuroscience, University la Sapienza Rome, 00185 Rome, Italy
| | - Chiara Martini
- Department of Diagnostic, Parma University Hospital, 43126 Parma, Italy
| | - Ruben Foresti
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
| | - Cosimo Costantino
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
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Smirnova V, Khamatnurova R, Kharin N, Yaikova E, Baltina T, Sachenkov O. The Automatization of the Gait Analysis by the Vicon Video System: A Pilot Study. SENSORS (BASEL, SWITZERLAND) 2022; 22:7178. [PMID: 36236276 PMCID: PMC9571292 DOI: 10.3390/s22197178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/15/2022] [Accepted: 09/15/2022] [Indexed: 06/16/2023]
Abstract
The quality of modern measuring instruments has a strong influence on the speed of diagnosing diseases of the human musculoskeletal system. The research is focused on automatization of the method of gait analysis. The study involved six healthy subjects. The subjects walk straight. Each subject made several gait types: casual walking and imitation of a non-standard gait, including shuffling, lameness, clubfoot, walking from the heel, rolling from heel to toe, walking with hands in pockets, and catwalk. Each type of gait was recorded three times. For video fixation, the Vicon Nexus system was used. A total of 27 reflective markers were placed on the special anatomical regions. The goniometry methods were used. The walk data were divided by steps and by step phases. Kinematic parameters for estimation were formulated and calculated. An approach for data clusterization is presented. For this purpose, angle data were interpolated and the interpolation coefficients were used for clustering the data. The data were processed and four cluster groups were found. Typical angulograms for cluster groups were presented. For each group, average angles were calculated. A statistically significant difference was found between received cluster groups.
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Affiliation(s)
- Victoriya Smirnova
- Institute of Computational Mathematics and Information Technologies, Kazan Federal University, 420008 Kazan, Russia
- N.I. Lobachevsky Institute of Mathematics and Mechanics, Kazan Federal University, 420008 Kazan, Russia
| | - Regina Khamatnurova
- Interdisciplinary Neuroscience Faculty, Goethe-Universität Frankfurt am Main, 60323 Frankfurt am Main, Germany
| | - Nikita Kharin
- N.I. Lobachevsky Institute of Mathematics and Mechanics, Kazan Federal University, 420008 Kazan, Russia
- Institute of Engineering, Kazan Federal University, 420008 Kazan, Russia
| | - Elena Yaikova
- Neurosurgical Department, Central City Clinical Hospital, 432017 Ulyanovsk, Russia
| | - Tatiana Baltina
- Institute of Fundamental Medicine and Biology, Kazan Federal University, 420008 Kazan, Russia
| | - Oskar Sachenkov
- N.I. Lobachevsky Institute of Mathematics and Mechanics, Kazan Federal University, 420008 Kazan, Russia
- Department Machines Science and Engineering Graphics, Tupolev Kazan National Research Technical University, 420111 Kazan, Russia
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