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Schellenberg E, Visscher RMS, Leu A, Guggiari E, Rabhi-Sidler S. Get-togethers: Guided Peer-Support Groups for Young Carers. Healthcare (Basel) 2024; 12:582. [PMID: 38470693 PMCID: PMC10931138 DOI: 10.3390/healthcare12050582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 02/24/2024] [Accepted: 02/26/2024] [Indexed: 03/14/2024] Open
Abstract
To address Young Carers' (YCs) needs for space and opportunities to reflect and exchange, a guided peer-support programme, the "Get-togethers", was developed in collaboration with YC in Switzerland in 2018. In order to evaluate if the Get-togethers were able to meet their originally set goals of (1) strengthening support among YCs, (2) promoting their life skills, (3) strengthening their social network and (4) promoting the inclusion and participation of YCs, participants of the Get-togethers were asked to complete a short questionnaire about their participation in and experiences with the Get-togethers. We also analysed the standard documentation of 17 Get-togethers held between May 2021 and September 2023. Overall, the Get-togethers were rated positively in almost all areas of the survey and the documentation, indicating that the four originally set objectives of the Get-togethers were (at least largely) achieved. The Get-togethers covered a large part of the needs of YCs, such as emotional support and opportunities to relax and exchange with people in a similar situation, yet they largely failed to reach minor YCs and male YCs. Further support programmes should be developed to address the different needs of different groups of YCs.
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Affiliation(s)
- Eva Schellenberg
- Careum School of Health, Kalaidos University of Applied Sciences, 8006 Zürich, Switzerland; (E.S.); (R.M.S.V.); (E.G.)
| | - Rosa M. S. Visscher
- Careum School of Health, Kalaidos University of Applied Sciences, 8006 Zürich, Switzerland; (E.S.); (R.M.S.V.); (E.G.)
| | - Agnes Leu
- Institute for Biomedical Ethics, Medical Faculty, University of Basel, 4056 Basel, Switzerland;
| | - Elena Guggiari
- Careum School of Health, Kalaidos University of Applied Sciences, 8006 Zürich, Switzerland; (E.S.); (R.M.S.V.); (E.G.)
| | - Sarah Rabhi-Sidler
- Careum School of Health, Kalaidos University of Applied Sciences, 8006 Zürich, Switzerland; (E.S.); (R.M.S.V.); (E.G.)
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Lohss R, Winter R, Göpfert B, Visscher RMS, Sangeux M, Zentai N, Viehweger E. Biomechanical gait parameters change with increasing virtual height in a child with spastic cerebral palsy: A case report. Clin Case Rep 2024; 12:e8548. [PMID: 38440770 PMCID: PMC10909796 DOI: 10.1002/ccr3.8548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 11/02/2023] [Accepted: 01/25/2024] [Indexed: 03/06/2024] Open
Abstract
Virtual height exposure coupled with motion capture is feasible to elicit changes in spatiotemporal, kinematic, and kinetic gait parameters in a child with cerebral palsy and should be considered when investigating gait in real-world-scenarios.
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Affiliation(s)
- Regine Lohss
- Laboratory for Movement AnalysisUniversity Children's Hospital BaselBaselSwitzerland
- Department of Biomedical EngineeringUniversity of BaselBaselSwitzerland
| | - Rebecca Winter
- Laboratory for Movement AnalysisUniversity Children's Hospital BaselBaselSwitzerland
- Department of Biomedical EngineeringUniversity of BaselBaselSwitzerland
| | - Beat Göpfert
- Laboratory for Movement AnalysisUniversity Children's Hospital BaselBaselSwitzerland
- Department of Biomedical EngineeringUniversity of BaselBaselSwitzerland
| | - Rosa M. S. Visscher
- Department of Biomedical EngineeringUniversity of BaselBaselSwitzerland
- Careum School of HealthKalaidos University of Applied SciencesZurichSwitzerland
| | - Morgan Sangeux
- Laboratory for Movement AnalysisUniversity Children's Hospital BaselBaselSwitzerland
- Department of Biomedical EngineeringUniversity of BaselBaselSwitzerland
| | - Norbert Zentai
- Department of Biomedical EngineeringUniversity of BaselBaselSwitzerland
| | - Elke Viehweger
- Laboratory for Movement AnalysisUniversity Children's Hospital BaselBaselSwitzerland
- Department of Biomedical EngineeringUniversity of BaselBaselSwitzerland
- Department of OrthopedicsUniversity Children's Hospital BaselBaselSwitzerland
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Dammeyer C, Nüesch C, Visscher RMS, Kim YK, Ismailidis P, Wittauer M, Stoffel K, Acklin Y, Egloff C, Netzer C, Mündermann A. Classification of inertial sensor-based gait patterns of orthopaedic conditions using machine learning: A pilot study. J Orthop Res 2024. [PMID: 38341759 DOI: 10.1002/jor.25797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 12/21/2023] [Accepted: 01/19/2024] [Indexed: 02/13/2024]
Abstract
Elderly patients often have more than one disease that affects walking behavior. An objective tool to identify which disease is the main cause of functional limitations may aid clinical decision making. Therefore, we investigated whether gait patterns could be used to identify degenerative diseases using machine learning. Data were extracted from a clinical database that included sagittal joint angles and spatiotemporal parameters measured using seven inertial sensors, and anthropometric data of patients with unilateral knee or hip osteoarthritis, lumbar or cervical spinal stenosis, and healthy controls. Various classification models were explored using the MATLAB Classification Learner app, and the optimizable Support Vector Machine was chosen as the best performing model. The accuracy of discrimination between healthy and pathologic gait was 82.3%, indicating that it is possible to distinguish pathological from healthy gait. The accuracy of discrimination between the different degenerative diseases was 51.4%, indicating the similarities in gait patterns between diseases need to be further explored. Overall, the differences between pathologic and healthy gait are distinct enough to classify using a classical machine learning model; however, routinely recorded gait characteristics and anthropometric data are not sufficient for successful discrimination of the degenerative diseases.
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Affiliation(s)
- Constanze Dammeyer
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
- Department of Psychology and Sport Science, University of Bielefeld, Bielefeld, Germany
| | - Corina Nüesch
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
- Department of Clinical Research, University of Basel, Basel, Switzerland
- Department of Spine Surgery, University Hospital Basel, Basel, Switzerland
| | - Rosa M S Visscher
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
- Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
| | - Yong K Kim
- Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
| | - Petros Ismailidis
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
| | - Matthias Wittauer
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
| | - Karl Stoffel
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
| | - Yves Acklin
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
| | - Christian Egloff
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
| | - Cordula Netzer
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
- Department of Clinical Research, University of Basel, Basel, Switzerland
- Department of Spine Surgery, University Hospital Basel, Basel, Switzerland
| | - Annegret Mündermann
- Department of Orthopaedics and Traumatology, University Hospital Basel, Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
- Department of Clinical Research, University of Basel, Basel, Switzerland
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Visscher RMS, Gwerder M, Viehweger E, Taylor WR, Brunner R, Singh NB. Can developmental trajectories in gait variability provide prognostic clues in motor adaptation among children with mild cerebral palsy? A retrospective observational cohort study. Front Hum Neurosci 2023; 17:1205969. [PMID: 37795211 PMCID: PMC10546019 DOI: 10.3389/fnhum.2023.1205969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 09/04/2023] [Indexed: 10/06/2023] Open
Abstract
Aim To investigate whether multiple domains of gait variability change during motor maturation and if this change over time could differentiate children with a typical development (TDC) from those with cerebral palsy (CwCP). Methods This cross-sectional retrospective study included 42 TDC and 129 CwCP, of which 99 and 30 exhibited GMFCS level I and II, respectively. Participants underwent barefoot 3D gait analysis. Age and parameters of gait variability (coefficient of variation of stride-time, stride length, single limb support time, walking speed, and cadence; as well as meanSD for hip flexion, knee flexion, and ankle dorsiflexion) were used to fit linear models, where the slope of the models could differ between groups to test the hypotheses. Results Motor-developmental trajectories of gait variability were able to distinguish between TDC and CwCP for all parameters, except the variability of joint angles. CwCP with GMFCS II also showed significantly higher levels of gait variability compared to those with GMFCS I, these levels were maintained across different ages. Interpretation This study showed the potential of gait variability to identify and detect the motor characteristics of high functioning CwCP. In future, such trajectories could provide functional biomarkers for identifying children with mild movement related disorders and support the management of expectations.
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Affiliation(s)
- Rosa M. S. Visscher
- Department of Health Science & Technology, Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Michelle Gwerder
- Department of Health Science & Technology, Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Elke Viehweger
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
- Laboratory of Movement Analysis, University Children's Hospital Basel (UKBB), Basel, Switzerland
| | - William R. Taylor
- Department of Health Science & Technology, Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
- Singapore-ETH Centre, Future Health Technologies Program, CREATE Campus, Singapore, Singapore
| | - Reinald Brunner
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
- Laboratory of Movement Analysis, University Children's Hospital Basel (UKBB), Basel, Switzerland
| | - Navrag B. Singh
- Department of Health Science & Technology, Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
- Singapore-ETH Centre, Future Health Technologies Program, CREATE Campus, Singapore, Singapore
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Kloeckner J, Visscher RMS, Taylor WR, Viehweger E, De Pieri E. Prediction of ground reaction forces and moments during walking in children with cerebral palsy. Front Hum Neurosci 2023; 17:1127613. [PMID: 36968787 PMCID: PMC10031015 DOI: 10.3389/fnhum.2023.1127613] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 02/13/2023] [Indexed: 03/10/2023] Open
Abstract
IntroductionGait analysis is increasingly used to support clinical decision-making regarding diagnosis and treatment planning for movement disorders. As a key part of gait analysis, inverse dynamics can be applied to estimate internal loading conditions during movement, which is essential for understanding pathological gait patterns. The inverse dynamics calculation uses external kinetic information, normally collected using force plates. However, collection of external ground reaction forces (GRFs) and moments (GRMs) can be challenging, especially in subjects with movement disorders. In recent years, a musculoskeletal modeling-based approach has been developed to predict external kinetics from kinematic data, but its performance has not yet been evaluated for altered locomotor patterns such as toe-walking. Therefore, the goal of this study was to investigate how well this prediction method performs for gait in children with cerebral palsy.MethodsThe method was applied to 25 subjects with various forms of hemiplegic spastic locomotor patterns. Predicted GRFs and GRMs, in addition to associated joint kinetics derived using inverse dynamics, were statistically compared against those based on force plate measurements.ResultsThe results showed that the performance of the predictive method was similar for the affected and unaffected limbs, with Pearson correlation coefficients between predicted and measured GRFs of 0.71–0.96, similar to those previously reported for healthy adults, despite the motor pathology and the inclusion of toes-walkers within our cohort. However, errors were amplified when calculating the resulting joint moments to an extent that could influence clinical interpretation.ConclusionTo conclude, the musculoskeletal modeling-based approach for estimating external kinetics is promising for pathological gait, offering the possibility of estimating GRFs and GRMs without the need for force plate data. However, further development is needed before implementation within clinical settings becomes possible.
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Affiliation(s)
- Julie Kloeckner
- Laboratory for Movement Biomechanics, Department of Health Science and Technology, Institute for Biomechanics, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland
- Department of Biomedical Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Rosa M. S. Visscher
- Laboratory for Movement Biomechanics, Department of Health Science and Technology, Institute for Biomechanics, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - William R. Taylor
- Laboratory for Movement Biomechanics, Department of Health Science and Technology, Institute for Biomechanics, Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland
- *Correspondence: William R. Taylor,
| | - Elke Viehweger
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
- Laboratory for Movement Analysis, University Children’s Hospital Basel (UKBB), Basel, Switzerland
| | - Enrico De Pieri
- Department of Biomedical Engineering, University of Basel, Basel, Switzerland
- Laboratory for Movement Analysis, University Children’s Hospital Basel (UKBB), Basel, Switzerland
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Kim YK, Visscher RMS, Viehweger E, Singh NB, Taylor WR, Vogl F. A deep-learning approach for automatically detecting gait-events based on foot-marker kinematics in children with cerebral palsy-Which markers work best for which gait patterns? PLoS One 2022; 17:e0275878. [PMID: 36227847 PMCID: PMC9562216 DOI: 10.1371/journal.pone.0275878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 09/25/2022] [Indexed: 11/06/2022] Open
Abstract
Neuromotor pathologies often cause motor deficits and deviations from typical locomotion, reducing the quality of life. Clinical gait analysis is used to effectively classify these motor deficits to gain deeper insights into resulting walking behaviours. To allow the ensemble averaging of spatio-temporal metrics across individuals during walking, gait events, such as initial contact (IC) or toe-off (TO), are extracted through either manual annotation based on video data, or through force thresholds using force plates. This study developed a deep-learning long short-term memory (LSTM) approach to detect IC and TO automatically based on foot-marker kinematics of 363 cerebral palsy subjects (age: 11.8 ± 3.2). These foot-marker kinematics, including 3D positions and velocities of the markers located on the hallux (HLX), calcaneus (HEE), distal second metatarsal (TOE), and proximal fifth metatarsal (PMT5), were extracted retrospectively from standard barefoot gait analysis sessions. Different input combinations of these four foot-markers were evaluated across three gait subgroups (IC with the heel, midfoot, or forefoot). For the overall group, our approach detected 89.7% of ICs within 16ms of the true event with a 18.5% false alarm rate. For TOs, only 71.6% of events were detected with a 33.8% false alarm rate. While the TOE|HEE marker combination performed well across all subgroups for IC detection, optimal performance for TO detection required different input markers per subgroup with performance differences of 5-10%. Thus, deep-learning LSTM based detection of IC events using the TOE|HEE markers offers an automated alternative to avoid operator-dependent and laborious manual annotation, as well as the limited step coverage and inability to measure assisted walking for force plate-based detection of IC events.
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Affiliation(s)
- Yong Kuk Kim
- Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
- * E-mail:
| | - Rosa M. S. Visscher
- Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
| | - Elke Viehweger
- Laboratory for Movement Analysis, Department of Orthopedics, University Children’s Hospital Basel, Basel, Switzerland
| | - Navrag B. Singh
- Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
| | - William R. Taylor
- Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
| | - Florian Vogl
- Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
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Visscher RMS, Freslier M, Moissenet F, Sansgiri S, Singh NB, Viehweger E, Taylor WR, Brunner R. Impact of the Marker Set Configuration on the Accuracy of Gait Event Detection in Healthy and Pathological Subjects. Front Hum Neurosci 2021; 15:720699. [PMID: 34588967 PMCID: PMC8475178 DOI: 10.3389/fnhum.2021.720699] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 08/24/2021] [Indexed: 11/29/2022] Open
Abstract
For interpreting outcomes of clinical gait analysis, an accurate estimation of gait events, such as initial contact (IC) and toe-off (TO), is essential. Numerous algorithms to automatically identify timing of gait events have been developed based on various marker set configurations as input. However, a systematic overview of the effect of the marker selection on the accuracy of estimating gait event timing is lacking. Therefore, we aim to evaluate (1) if the marker selection influences the accuracy of kinematic algorithms for estimating gait event timings and (2) what the best marker location is to ensure the highest event timing accuracy across various gait patterns. 104 individuals with cerebral palsy (16.0 ± 8.6 years) and 31 typically developing controls (age 20.6 ± 7.8) performed clinical gait analysis, and were divided into two out of eight groups based on the orientation of their foot, in sagittal and frontal plane at mid-stance. 3D marker trajectories of 11 foot/ankle markers were used to estimate the gait event timings (IC, TO) using five commonly used kinematic algorithms. Heatmaps, for IC and TO timing per group were created showing the median detection error, compared to detection using vertical ground reaction forces, for each marker. Our findings indicate that median detection errors can be kept within 7 ms for IC and 13 ms for TO when optimizing the choice of marker and detection algorithm toward foot orientation in midstance. Our results highlight that the use of markers located on the midfoot is robust for detecting gait events across different gait patterns.
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Affiliation(s)
- Rosa M S Visscher
- Laboratory for Movement Biomechanics, Department of Health Science and Technology, Institute for Biomechanics, ETH Zürich, Zurich, Switzerland.,Biomechanics of Movement Group, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Marie Freslier
- Laboratory for Movement Analysis, Department of Orthopedics, University Children's Hospital Basel, Basel, Switzerland
| | - Florent Moissenet
- Laboratory for Kinesiology, University of Geneva and Geneva University Hospitals, Geneva, Switzerland
| | - Sailee Sansgiri
- Department of Biomedical Engineering, Faculty of Mechanical, Maritime, and Materials Engineering, Delft University of Technology, Delft, Netherlands
| | - Navrag B Singh
- Laboratory for Movement Biomechanics, Department of Health Science and Technology, Institute for Biomechanics, ETH Zürich, Zurich, Switzerland
| | - Elke Viehweger
- Biomechanics of Movement Group, Department of Biomedical Engineering, University of Basel, Basel, Switzerland.,Laboratory for Movement Analysis, Department of Orthopedics, University Children's Hospital Basel, Basel, Switzerland
| | - William R Taylor
- Laboratory for Movement Biomechanics, Department of Health Science and Technology, Institute for Biomechanics, ETH Zürich, Zurich, Switzerland
| | - Reinald Brunner
- Biomechanics of Movement Group, Department of Biomedical Engineering, University of Basel, Basel, Switzerland.,Laboratory for Movement Analysis, Department of Orthopedics, University Children's Hospital Basel, Basel, Switzerland
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Visscher RMS, Sansgiri S, Freslier M, Harlaar J, Brunner R, Taylor WR, Singh NB. Towards validation and standardization of automatic gait event identification algorithms for use in paediatric pathological populations. Gait Posture 2021; 86:64-69. [PMID: 33684617 DOI: 10.1016/j.gaitpost.2021.02.031] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 01/26/2021] [Accepted: 02/26/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND To analyse and interpret gait patterns in pathological paediatric populations, accurate determination of the timing of specific gait events (e.g. initial contract - IC, or toe-off - TO) is essential. As currently used clinical identification methods are generally subjective, time-consuming, or limited to steps with force platform data, several techniques have been proposed based on processing of marker kinematics. However, until now, validation and standardization of these methods for use in diverse gait patterns remains lacking. RESEARCH QUESTIONS 1) What is the accuracy of available kinematics-based identification algorithms in determining the timing of IC and TO for diverse gait signatures? 2) Does automatic identification affect interpretation of spatio-temporal parameters?. METHODS 3D kinematic and kinetic data of 90 children were retrospectively analysed from a clinical gait database. Participants were classified into 3 gait categories: group A (toe-walkers), B (flat IC) and C (heel IC). Five kinematic algorithms (one modified) were implemented for two different foot marker configurations for both IC and TO and compared with clinical (visual and force-plate) identification using Bland-Altman analysis. The best-performing algorithm-marker configuration was used to compute spatio-temporal parameters (STP) of all gait trials. To establish whether the error associated with this configuration would affect clinical interpretation, the bias and limits of agreement were determined and compared against inter-trial variability established using visual identification. RESULTS Sagittal velocity of the heel (Group C) or toe marker configurations (Group A and B) was the most reliable indicator of IC, while the sagittal velocity of the hallux marker configuration performed best for TO. Biases for walking speed, stride time and stride length were within the respective inter-trial variability values. SIGNIFICANCE Automatic identification of gait events was dependent on algorithm-marker configuration, and best results were obtained when optimized towards specific gait patterns. Our data suggest that correct selection of automatic gait event detection approach will ensure that misinterpretation of STPs is avoided.
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Affiliation(s)
- Rosa M S Visscher
- Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zurich, Leopold-Ruzicka-Weg 4, 8093, Zurich, Switzerland.
| | - Sailee Sansgiri
- Department of Biomechanical Engineering, Delft University of Technology, Delft, The Netherlands & Dept. Orthopaedics, Erasmus Medical Center, Rotterdam, The Netherlands.
| | - Marie Freslier
- Laboratory of Movement Analysis, University Children's Hospital Basel (UKBB), University of Basel, Spitalstrasse 33, 4056, Basel, Switzerland.
| | - Jaap Harlaar
- Department of Biomechanical Engineering, Delft University of Technology, Delft, The Netherlands & Dept. Orthopaedics, Erasmus Medical Center, Rotterdam, The Netherlands.
| | - Reinald Brunner
- Laboratory of Movement Analysis, University Children's Hospital Basel (UKBB), University of Basel, Spitalstrasse 33, 4056, Basel, Switzerland.
| | - William R Taylor
- Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zurich, Leopold-Ruzicka-Weg 4, 8093, Zurich, Switzerland.
| | - Navrag B Singh
- Laboratory for Movement Biomechanics, Institute for Biomechanics, ETH Zurich, Leopold-Ruzicka-Weg 4, 8093, Zurich, Switzerland.
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Visscher RMS, Feddermann-Demont N, Romano F, Straumann D, Bertolini G. Artificial intelligence for understanding concussion: Retrospective cluster analysis on the balance and vestibular diagnostic data of concussion patients. PLoS One 2019; 14:e0214525. [PMID: 30939164 PMCID: PMC6445465 DOI: 10.1371/journal.pone.0214525] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 03/14/2019] [Indexed: 12/26/2022] Open
Abstract
OBJECTIVES We propose a bottom-up, machine-learning approach, for the objective vestibular and balance diagnostic data of concussion patients, to provide insight into the differences in patients' phenotypes, independent of existing diagnoses (unsupervised learning). METHODS Diagnostic data from a battery of validated balance and vestibular assessments were extracted from the database of the Swiss Concussion Center. The desired number of clusters within the patient database was estimated using Calinski-Harabasz criteria. Complex (self-organizing map, SOM) and standard (k-means) clustering tools were used, and the formed clusters were compared. RESULTS A total of 96 patients (81.3% male, age (median [IQR]): 25.0[10.8]) who were expected to suffer from sports-related concussion or post-concussive syndrome (52[140] days between diagnostic testing and the concussive episode) were included. The cluster evaluation indicated dividing the data into two groups. Only the SOM gave a stable clustering outcome, dividing the patients in group-1 (n = 38) and group-2 (n = 58). A large significant difference was found for the caloric summary score for the maximal speed of the slow phase, where group-1 scored 30.7% lower than group-2 (27.6[18.2] vs. 51.0[31.0]). Group-1 also scored significantly lower on the sensory organisation test composite score (69.0[22.3] vs. 79.0[10.5]) and higher on the visual acuity (-0.03[0.33] vs. -0.14[0.12]) and dynamic visual acuity (0.38[0.84] vs. 0.20[0.20]) tests. The importance of caloric, SOT and DVA, was supported by the PCA outcomes. Group-1 tended to report headaches, blurred vision and balance problems more frequently than group-2 (>10% difference). CONCLUSION The SOM divided the data into one group with prominent vestibular disorders and another with no clear vestibular or balance problems, suggesting that artificial intelligence might help improve the diagnostic process.
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Affiliation(s)
- Rosa M. S. Visscher
- Institute for Biomechanics, ETH Zurich, Zurich, Switzerland
- Department of Neurology, Interdisciplinary Center for Vertigo and Neurological Visual Disorders, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Nina Feddermann-Demont
- Department of Neurology, Interdisciplinary Center for Vertigo and Neurological Visual Disorders, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Swiss Concussion Center, Schulthess Clinic, Zurich, Switzerland
| | - Fausto Romano
- Department of Neurology, Interdisciplinary Center for Vertigo and Neurological Visual Disorders, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Swiss Concussion Center, Schulthess Clinic, Zurich, Switzerland
| | - Dominik Straumann
- Department of Neurology, Interdisciplinary Center for Vertigo and Neurological Visual Disorders, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Swiss Concussion Center, Schulthess Clinic, Zurich, Switzerland
| | - Giovanni Bertolini
- Department of Neurology, Interdisciplinary Center for Vertigo and Neurological Visual Disorders, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Swiss Concussion Center, Schulthess Clinic, Zurich, Switzerland
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Veldman MP, Item-Glatthorn JF, Visscher RMS, Hortobágyi T, Maffiuletti NA. Somatosensory Electrical Stimulation Does Not Improve Motor Coordination in Patients with Unilateral Knee Osteoarthritis. J Clin Med 2019; 8:E259. [PMID: 30791367 PMCID: PMC6406642 DOI: 10.3390/jcm8020259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 02/12/2019] [Accepted: 02/18/2019] [Indexed: 11/30/2022] Open
Abstract
Non-surgical treatment of knee osteoarthritis (KOA) is often focused on the motor component of KOA even though there is evidence that sensory dysfunctions play an important role in the impaired control of the affected joint. Excitation of sensory afferents can increase motor function by exploiting the nervous system's ability to adapt to changing environments (i.e., neuronal plasticity). Therefore, the aim of this study was to explore the acute effects of a single session (30 min) of sensory intervention targeting neuronal plasticity using low-frequency (10 Hz) somatosensory electrical stimulation (SES) of the femoral nerve. We evaluated the effects of SES on the position and force control of the affected knee and self-reported pain in KOA patients (n = 14) in a sham-controlled randomized trial. The results showed that SES did not improve measures of lower-limb motor coordination compared to sham stimulation in KOA patients, nor did it improve self-reported knee function and pain (all p > 0.05). In conclusion, despite sensory involvement in KOA, the sensory intervention used in the present explorative study did not relieve self-reported pain, which may underlie the absence of an effect on measures of motor coordination. In sum, the present explorative study showed that SES alone does not improve motor coordination in KOA patients.
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Affiliation(s)
- Menno P Veldman
- Center for Human Movement Sciences, University Medical Center Groningen, University of Groningen, 9713AV Groningen, The Netherlands.
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, 3001 Leuven, Belgium.
| | | | | | - Tibor Hortobágyi
- Center for Human Movement Sciences, University Medical Center Groningen, University of Groningen, 9713AV Groningen, The Netherlands.
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