A fuzzy decision tree-based SVM classifier for assessing osteoarthritis severity using ground reaction force measurements

Moustakidis, Serafeim; Theocharis, John B.; Giakas, Giannis

doi:10.1016/j.medengphy.2010.08.006

Cited by 34 publications

(29 citation statements)

References 27 publications

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“…[8], Deluzio and Astephen [9], Moustakidis et al . [10], and Mezghani et al . [11] have discriminated between subjects with knee OA versus normal subjects, as detailed below.…”

Section: Introductionmentioning

confidence: 98%

“…Wavelet analysis by Moustakidis et al . [10] has shown that a reduction in peak anterior–posterior ground reaction forces during the stance phase occurs in knee OA subjects (12 healthy, 24 with knee OA). They were grouped in no, moderate, and severe OA categories with a 93.4% performance.…”

Section: Introductionmentioning

confidence: 99%

“…That is the reason why we refrain from abstract mathematical approaches such as wavelet packet decomposition as done by Moustakidis et al. [10], as they lack a direct physical interpretation. Moreover, we consider all the trials provided by each subject, rather than averaging across trials in order to calculate the mean GRFs, as is the case of Mezghani et al .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting knee osteoarthritis and its discriminating parameters using random forests

Kotti¹,

Duffell²,

Faisal³

et al. 2017

Medical Engineering & Physics

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“…[8], Deluzio and Astephen [9], Moustakidis et al . [10], and Mezghani et al . [11] have discriminated between subjects with knee OA versus normal subjects, as detailed below.…”

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detecting knee osteoarthritis and its discriminating parameters using random forests

Kotti¹,

Duffell²,

Faisal³

et al. 2017

Medical Engineering & Physics

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show abstract

“…For example, in [37], wavelet analysis of GRFs has proved a reduction in peak anteriorposterior GRFs during the stance phase for knee OA subjects. Also, the vertical GRFs were lower in severe cases compared to the moderate cases.…”

Section: Discussionmentioning

confidence: 99%

The Complexity of Human Walking: A Knee Osteoarthritis Study

et al. 2014

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This study proposes a framework for deconstructing complex walking patterns to create a simple principal component space before checking whether the projection to this space is suitable for identifying changes from the normality. We focus on knee osteoarthritis, the most common knee joint disease and the second leading cause of disability. Knee osteoarthritis affects over 250 million people worldwide. The motivation for projecting the highly dimensional movements to a lower dimensional and simpler space is our belief that motor behaviour can be understood by identifying a simplicity via projection to a low principal component space, which may reflect upon the underlying mechanism. To study this, we recruited 180 subjects, 47 of which reported that they had knee osteoarthritis. They were asked to walk several times along a walkway equipped with two force plates that capture their ground reaction forces along 3 axes, namely vertical, anterior-posterior, and medio-lateral, at 1000 Hz. Data when the subject does not clearly strike the force plate were excluded, leaving 1–3 gait cycles per subject. To examine the complexity of human walking, we applied dimensionality reduction via Probabilistic Principal Component Analysis. The first principal component explains 34% of the variance in the data, whereas over 80% of the variance is explained by 8 principal components or more. This proves the complexity of the underlying structure of the ground reaction forces. To examine if our musculoskeletal system generates movements that are distinguishable between normal and pathological subjects in a low dimensional principal component space, we applied a Bayes classifier. For the tested cross-validated, subject-independent experimental protocol, the classification accuracy equals 82.62%. Also, a novel complexity measure is proposed, which can be used as an objective index to facilitate clinical decision making. This measure proves that knee osteoarthritis subjects exhibit more variability in the two-dimensional principal component space.

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“…Gait analysis can be also used to detect gait disorders, identify balancing impairment, and design prosthesis [22]. There are a number of methods and algorithms reported in the literature for gait modelling and identifying the walking characteristics of individuals [23].…”

Section: A Modeling Of Gaitmentioning

confidence: 99%

Assessing the impact of fatigue on gait using inertial sensors

Ameli

Stirling

Naghdy

et al. 2013

2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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Conventionally subjective methods are often employed for the assessment of fatigue. These approaches are prone to error and inaccuracy. Quantitative methods in a very limited extent have been applied. Inertial sensors and a Six Minute Walking Test (6MWT) are employed to measure gait and posture characteristics before and after a repeated sit and stand task that induces a degree of fatigue. Using a set of 17 sensors, the inertial signals corresponding to position, velocity, acceleration, orientation, angular velocity and angular acceleration are recorded based on a 23 degree of freedom humanoid model. The data streams obtained are subsequently segmented by an intrinsic clustering algorithm known as Minimum Message Length encoding (MML) forming a Gaussian Mixture Model (GMM). Several postural states (exemplar motion primitives) are captured in the resultant model, which is subsequently utilized to derive a holistic index corresponding to the physical ambulatory status of patient. The proposed method is applied to both data collected pre-and post-fatigue performance. The results are encouraging as they clearly demonstrate that fatigue affects physical status during 6MWT. The current results are promising in the development of an objective fatigue assessment tool for different patients.

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A fuzzy decision tree-based SVM classifier for assessing osteoarthritis severity using ground reaction force measurements

Cited by 34 publications

References 27 publications

Detecting knee osteoarthritis and its discriminating parameters using random forests

Detecting knee osteoarthritis and its discriminating parameters using random forests

The Complexity of Human Walking: A Knee Osteoarthritis Study

Assessing the impact of fatigue on gait using inertial sensors

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