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1.
Artigo em Inglês | MEDLINE | ID: mdl-32086225

RESUMO

Mobile gait analysis using wearable inertial measurement units (IMUs) provides valuable insights for the assessment of movement impairments in different neurological and musculoskeletal diseases, for example Parkinson's disease (PD). The increase in data volume due to arising long-term monitoring requires valid, robust and efficient analysis pipelines. In many studies an upstream detection of gait is therefore applied. However, current methods do not provide a robust way to successfully reject non-gait signals. Therefore, we developed a novel algorithm for the detection of gait from continuous inertial data of sensors worn at the feet. The algorithm is focused not only on a high sensitivity but also a high specificity for gait. Sliding windows of IMU signals recorded from the feet of PD patients were processed in the frequency domain. Gait was detected if the frequency spectrum contained specific patterns of harmonic frequencies. The approach was trained and evaluated on 150 clinical measurements containing standardized gait and cyclic movement tests. The detection reached as sensitivity of 0.98 and a specificity of 0.96 for the best sensor configuration (angular rate around the medio-lateral axis). On an independent validation data set including 203 unsupervised, semi-standardized gait tests, the algorithm achieved a sensitivity of 0.97. Our algorithm for the detection of gait from continuous IMU signals works reliably and showed promising results for the application in the context of free-living and non-standardized monitoring scenarios.

2.
Sensors (Basel) ; 19(14)2019 Jul 13.
Artigo em Inglês | MEDLINE | ID: mdl-31337067

RESUMO

Mobile gait analysis systems using wearable sensors have the potential to analyze and monitor pathological gait in a finer scale than ever before. A closer look at gait in Parkinson's disease (PD) reveals that turning has its own characteristics and requires its own analysis. The goal of this paper is to present a system with on-shoe wearable sensors in order to analyze the abnormalities of turning in a standardized gait test for PD. We investigated turning abnormalities in a large cohort of 108 PD patients and 42 age-matched controls. We quantified turning through several spatio-temporal parameters. Analysis of turn-derived parameters revealed differences of turn-related gait impairment in relation to different disease stages and motor impairment. Our findings confirm and extend the results from previous studies and show the applicability of our system in turning analysis. Our system can provide insight into the turning in PD and be used as a complement for physicians' gait assessment and to monitor patients in their daily environment.


Assuntos
Algoritmos , Monitorização Fisiológica/instrumentação , Doença de Parkinson/fisiopatologia , Sapatos , Dispositivos Eletrônicos Vestíveis , Adulto , Idoso , Idoso de 80 Anos ou mais , Estudos de Casos e Controles , Desenho de Equipamento , Feminino , Transtornos Neurológicos da Marcha/diagnóstico , Transtornos Neurológicos da Marcha/fisiopatologia , Humanos , Masculino , Pessoa de Meia-Idade , Monitorização Fisiológica/métodos , Monitorização Fisiológica/normas , Reprodutibilidade dos Testes , Análise Espaço-Temporal
3.
J Neuroeng Rehabil ; 16(1): 98, 2019 Jul 26.
Artigo em Inglês | MEDLINE | ID: mdl-31349860

RESUMO

The original article [1] contained an error whereby Fig. 6 contained a minor shading glitch affecting its presentation. This has now been corrected.

4.
J Neuroeng Rehabil ; 16(1): 77, 2019 Jun 26.
Artigo em Inglês | MEDLINE | ID: mdl-31242915

RESUMO

BACKGROUND: Gait symptoms and balance impairment are characteristic indicators for the progression in Parkinson's disease (PD). Current gait assessments mostly focus on straight strides with assumed constant velocity, while acceleration/deceleration and turning strides are often ignored. This is either due to the set up of typical clinical assessments or technical limitations in capture volume. Wearable inertial measurement units are a promising and unobtrusive technology to overcome these limitations. Other gait phases such as initiation, termination, transitioning (between straight walking and turning) and turning might be relevant as well for the evaluation of gait and balance impairments in PD. METHOD: In a cohort of 119 PD patients, we applied unsupervised algorithms to find different gait clusters which potentially include the clinically relevant information from distinct gait phases in the standardized 4x10 m gait test. To clinically validate our approach, we determined the discriminative power in each gait cluster to classify between impaired and unimpaired PD patients and compared it to baseline (analyzing all straight strides). RESULTS: As a main result, analyzing only one of the gait clusters constant, non-constant or turning led in each case to a better classification performance in comparison to the baseline (increase of area under the curve (AUC) up to 19% relative to baseline). Furthermore, gait parameters (for turning, constant and non-constant gait) that best predict motor impairment in PD were identified. CONCLUSIONS: We conclude that a more detailed analysis in terms of different gait clusters of standardized gait tests such as the 4x10 m walk may give more insights about the clinically relevant motor impairment in PD patients.

5.
Gait Posture ; 66: 194-200, 2018 10.
Artigo em Inglês | MEDLINE | ID: mdl-30199778

RESUMO

BACKGROUND: Despite the general success of total knee arthroplasty (TKA) regarding patient-reported outcome measures, studies investigating gait function have shown diverse functional outcomes. Mobile sensor-based systems have recently been employed for accurate clinical gait assessments, as they allow a better integration of gait analysis into clinical routines as compared to laboratory based systems. RESEARCH QUESTION: In this study, we sought to examine whether an accurate assessment of gait function of knee osteoarthritis patients with respect to surgery outcome evaluation after TKA using a mobile sensor-based gait analysis system is possible. METHODS: A foot-worn sensor-based system was used to assess spatio-temporal gait parameters of 24 knee osteoarthritis patients one day before and one year after TKA, and in comparison to matched control participants. Patients were clustered into positive and negative responder groups using a heuristic approach regarding improvements in gait function. Machine learning was used to predict surgery outcome based on pre-operative gait parameters. RESULTS: Gait function differed significantly between controls and patients. Patient-reported outcome measures improved significantly after surgery, but no significant global gait parameter difference was observed between pre- and post-operative status. However, the responder groups could be correctly predicted with an accuracy of up to 89% using pre-operative gait parameters. Patients exhibiting high pre-operative gait function were more likely to experience a functional decrease after surgery. Important gait parameters for the discrimination were stride time and stride length. SIGNIFICANCE: The early identification of post-surgical functional outcomes of patients is of great importance to better inform patients pre-operatively regarding surgery success and to improve post-surgical management.


Assuntos
Artroplastia do Joelho/métodos , Análise da Marcha/métodos , Articulação do Joelho/fisiopatologia , Osteoartrite do Joelho/fisiopatologia , Acelerometria/métodos , Idoso , Feminino , Marcha/fisiologia , Humanos , Articulação do Joelho/cirurgia , Masculino , Pessoa de Meia-Idade , Osteoartrite do Joelho/cirurgia , Sensibilidade e Especificidade , Análise Espaço-Temporal , Resultado do Tratamento
6.
Sensors (Basel) ; 18(1)2018 Jan 06.
Artigo em Inglês | MEDLINE | ID: mdl-29316636

RESUMO

Robust gait segmentation is the basis for mobile gait analysis. A range of methods have been applied and evaluated for gait segmentation of healthy and pathological gait bouts. However, a unified evaluation of gait segmentation methods in Parkinson's disease (PD) is missing. In this paper, we compare four prevalent gait segmentation methods in order to reveal their strengths and drawbacks in gait processing. We considered peak detection from event-based methods, two variations of dynamic time warping from template matching methods, and hierarchical hidden Markov models (hHMMs) from machine learning methods. To evaluate the methods, we included two supervised and instrumented gait tests that are widely used in the examination of Parkinsonian gait. In the first experiment, a sequence of strides from instructed straight walks was measured from 10 PD patients. In the second experiment, a more heterogeneous assessment paradigm was used from an additional 34 PD patients, including straight walks and turning strides as well as non-stride movements. The goal of the latter experiment was to evaluate the methods in challenging situations including turning strides and non-stride movements. Results showed no significant difference between the methods for the first scenario, in which all methods achieved an almost 100% accuracy in terms of F-score. Hence, we concluded that in the case of a predefined and homogeneous sequence of strides, all methods can be applied equally. However, in the second experiment the difference between methods became evident, with the hHMM obtaining a 96% F-score and significantly outperforming the other methods. The hHMM also proved promising in distinguishing between strides and non-stride movements, which is critical for clinical gait analysis. Our results indicate that both the instrumented test procedure and the required stride segmentation algorithm have to be selected adequately in order to support and complement classical clinical examination by sensor-based movement assessment.


Assuntos
Marcha , Algoritmos , Transtornos Neurológicos da Marcha , Humanos , Doença de Parkinson
7.
IEEE J Biomed Health Inform ; 22(2): 354-362, 2018 03.
Artigo em Inglês | MEDLINE | ID: mdl-28333648

RESUMO

OBJECTIVE: Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of state-of-the-art double integration approaches to gait patterns with a clear zero-velocity phase. METHODS: We describe a novel approach to stride length estimation that uses deep convolutional neural networks to map stride-specific inertial sensor data to the resulting stride length. The model is trained on a publicly available and clinically relevant benchmark dataset consisting of 1220 strides from 101 geriatric patients. Evaluation is done in a tenfold cross validation and for three different stride definitions. RESULTS: Even though best results are achieved with strides defined from midstance to midstance with average accuracy and precision of , performance does not strongly depend on stride definition. The achieved precision outperforms state-of-the-art methods evaluated on the same benchmark dataset by . CONCLUSION: Due to the independence of stride definition, the proposed method is not subject to the methodological constrains that limit applicability of state-of-the-art double integration methods. Furthermore, it was possible to improve precision on the benchmark dataset. SIGNIFICANCE: With more precise mobile stride length estimation, new insights to the progression of neurological disease or early indications might be gained. Due to the independence of stride definition, previously uncharted diseases in terms of mobile gait analysis can now be investigated by retraining and applying the proposed method.


Assuntos
Marcha/fisiologia , Processamento de Sinais Assistido por Computador , Caminhada/fisiologia , Bases de Dados Factuais , Aprendizado Profundo , Humanos , Modelos Biológicos , Análise de Regressão
8.
Front Aging Neurosci ; 9: 316, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-29021758

RESUMO

Patients suffering from Parkinson's disease (PD) present motor impairments reflected in the dynamics of the center of pressure (CoP) adjustments during quiet standing. One method to study the dynamics of CoP adjustments is the entropic half-life (EnHL), which measures the short-term correlations of a time series at different time scales. Changes in the EnHL of CoP time series suggest neuromuscular adaptations in the control of posture. In this study, we sought to investigate the immediate changes in the EnHL of CoP adjustments of patients with PD during one session of perturbed (experimental group) and unperturbed treadmill walking (control group). A total of 39 patients with PD participated in this study. The experimental group (n = 19) walked on a treadmill providing small tilting of the treadmill platform. The control group (n = 20) walked without perturbations. Each participant performed 5-min practice followed by three 5-min training blocks of walking with or without perturbation (with 3-min resting in between). Quiet standing CoP data was collected for 30 s at pre-training, after each training block, immediately post-training, and after 10 min retention. The EnHL was computed on the original and surrogates (phase-randomized) CoP signals in the medio-lateral (ML) and anterior-posterior (AP) directions. Data was analyzed using four-way mixed ANOVA. Increased EnHL values were observed for both groups (Time effect, p < 0.001) as the intervention progressed, suggesting neuromuscular adaptations in the control of posture. The EnHL of surrogate signals were significantly lower than for original signals (p < 0.001), confirming that these adaptations come from non-random control processes. There was no Group effect (p = 0.622), however by analyzing the significant Group by Direction by Time interaction (p < 0.05), a more pronounced effect in the ML direction of the perturbed group was observed. Altogether, our findings show that treadmill walking decreases the complexity of CoP adjustments, suggesting neuromuscular adaptations in balance control during a short training period. Further investigations are required to assess these adaptations during longer training intervals.

9.
Sensors (Basel) ; 17(9)2017 Aug 23.
Artigo em Inglês | MEDLINE | ID: mdl-28832511

RESUMO

Mobile gait analysis systems based on inertial sensing on the shoe are applied in a wide range of applications. Especially for medical applications, they can give new insights into motor impairment in, e.g., neurodegenerative disease and help objectify patient assessment. One key component in these systems is the reconstruction of the foot trajectories from inertial data. In literature, various methods for this task have been proposed. However, performance is evaluated on a variety of datasets due to the lack of large, generally accepted benchmark datasets. This hinders a fair comparison of methods. In this work, we implement three orientation estimation and three double integration schemes for use in a foot trajectory estimation pipeline. All methods are drawn from literature and evaluated against a marker-based motion capture reference. We provide a fair comparison on the same dataset consisting of 735 strides from 16 healthy subjects. As a result, the implemented methods are ranked and we identify the most suitable processing pipeline for foot trajectory estimation in the context of mobile gait analysis.


Assuntos
Marcha , Benchmarking , , Humanos
10.
Sensors (Basel) ; 17(7)2017 Jun 28.
Artigo em Inglês | MEDLINE | ID: mdl-28657587

RESUMO

The purpose of this study was to assess the concurrent validity and test-retest reliability of a sensor-based gait analysis system. Eleven healthy subjects and four Parkinson's disease (PD) patients were asked to complete gait tasks whilst wearing two inertial measurement units at their feet. The extracted spatio-temporal parameters of 1166 strides were compared to those extracted from a reference camera-based motion capture system concerning concurrent validity. Test-retest reliability was assessed for five healthy subjects at three different days in a two week period. The two systems were highly correlated for all gait parameters ( r > 0.93 ). The bias for stride time was 0 ± 16 ms and for stride length was 1.4 ± 6.7 cm. No systematic range dependent errors were observed and no significant changes existed between healthy subjects and PD patients. Test-retest reliability was excellent for all parameters (intraclass correlation (ICC) > 0.81) except for gait velocity (ICC > 0.55). The sensor-based system was able to accurately capture spatio-temporal gait parameters as compared to the reference camera-based system for normal and impaired gait. The system's high retest reliability renders the use in recurrent clinical measurements and in long-term applications feasible.


Assuntos
Marcha , , Voluntários Saudáveis , Humanos , Imagem por Ressonância Magnética , Reprodutibilidade dos Testes
11.
IEEE J Biomed Health Inform ; 21(1): 85-93, 2017 01.
Artigo em Inglês | MEDLINE | ID: mdl-28103196

RESUMO

Measurement of stride-related, biomechanical parameters is the common rationale for objective gait impairment scoring. State-of-the-art double-integration approaches to extract these parameters from inertial sensor data are, however, limited in their clinical applicability due to the underlying assumptions. To overcome this, we present a method to translate the abstract information provided by wearable sensors to context-related expert features based on deep convolutional neural networks. Regarding mobile gait analysis, this enables integration-free and data-driven extraction of a set of eight spatio-temporal stride parameters. To this end, two modeling approaches are compared: a combined network estimating all parameters of interest and an ensemble approach that spawns less complex networks for each parameter individually. The ensemble approach is outperforming the combined modeling in the current application. On a clinically relevant and publicly available benchmark dataset, we estimate stride length, width and medio-lateral change in foot angle up to -0.15 ± 6.09 cm, -0.09 ± 4.22 cm and 0.13 ± 3.78° respectively. Stride, swing and stance time as well as heel and toe contact times are estimated up to ±0.07, ±0.05, ±0.07, ±0.07 and ±0.12 s respectively. This is comparable to and in parts outperforming or defining state of the art. Our results further indicate that the proposed change in the methodology could substitute assumption-driven double-integration methods and enable mobile assessment of spatio-temporal stride parameters in clinically critical situations as, e.g., in the case of spastic gait impairments.


Assuntos
Marcha/fisiologia , Processamento de Sinais Assistido por Computador , Acelerometria/instrumentação , Pé/fisiologia , Humanos , Aprendizado de Máquina , Análise de Regressão , Caminhada/fisiologia
12.
Med Image Comput Comput Assist Interv ; 17(Pt 2): 603-10, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-25485429

RESUMO

The multi-scale Frangi vesselness filter is an established tool in (retinal) vascular imaging. However, it cannot properly cope with crossings or bifurcations since it only looks for elongated structures. Therefore, we disentangle crossings/bifurcations via (multiple scale) invertible orientation scores and apply vesselness filters in this domain. This new method via scale-orientation scores performs considerably better at enhancing vessels throughout crossings and bifurcations than the Frangi version. Both methods are evaluated on a public dataset. Performance is measured by comparing ground truth data to the segmentation results obtained by basic thresholding and morphological component analysis of the filtered images.


Assuntos
Algoritmos , Aumento da Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Reconhecimento Automatizado de Padrão/métodos , Vasos Retinianos/anatomia & histologia , Retinoscopia/métodos , Humanos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
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