Pelvic movement variability of healthy and unilateral hip joint involvement individuals

Andrade, Adriano O.; Ferreira, Luiz Cláudio Vieira; Rabelo, Amanda Gomes; Vieira, Marcus Fraga; Campos, Alexwell Rodrigues; Gonçalves, Bruno Ferreira; Pereira, Adriano Alves

doi:10.1016/j.bspc.2016.10.008

Cited by 18 publications

(14 citation statements)

References 28 publications

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“…Specifically, a review by Mills et al (2013 [ 9 ]) found that stride time was the strongest and most consistent biomechanical deviation in adults with severe knee OA, as compared to healthy controls. Similarly, numerous studies in this scoping review found that mean ST parameters displayed significant differences between healthy controls and adults with OA [ 20 , 21 , 28 , 31 , 35 , 40 , 75 , 80 ]. However, these studies were generally comparing adults with severe OA or post-total joint arthroplasty (TJA) to healthy controls.…”

Section: Discussionmentioning

confidence: 54%

“…The duplicate removal rendered 376 articles, which were screened at the title/abstract level. A full-text screening process of 107 articles resulted in 72 articles, and these 72 articles were included in our scoping review [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ...…”

Section: Resultsmentioning

confidence: 99%

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Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis—A Scoping Review

Kobsar

Masood

Khan

et al. 2020

Sensors

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Our objective was to conduct a scoping review which summarizes the growing body of literature using wearable inertial sensors for gait analysis in lower limb osteoarthritis. We searched six databases using predetermined search terms which highlighted the broad areas of inertial sensors, gait, and osteoarthritis. Two authors independently conducted title and abstract reviews, followed by two authors independently completing full-text screenings. Study quality was also assessed by two independent raters and data were extracted by one reviewer in areas such as study design, osteoarthritis sample, protocols, and inertial sensor outcomes. A total of 72 articles were included, which studied the gait of 2159 adults with osteoarthritis (OA) using inertial sensors. The most common location of OA studied was the knee (n = 46), followed by the hip (n = 22), and the ankle (n = 7). The back (n = 41) and the shank (n = 40) were the most common placements for inertial sensors. The three most prevalent biomechanical outcomes studied were: mean spatiotemporal parameters (n = 45), segment or joint angles (n = 33), and linear acceleration magnitudes (n = 22). Our findings demonstrate exceptional growth in this field in the last 5 years. Nevertheless, there remains a need for more longitudinal study designs, patient-specific models, free-living assessments, and a push for “Code Reuse” to maximize the unique capabilities of these devices and ultimately improve how we diagnose and treat this debilitating disease.

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Section: Discussionmentioning

confidence: 54%

Section: Resultsmentioning

confidence: 99%

Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis—A Scoping Review

Kobsar

Masood

Khan

et al. 2020

Sensors

View full text Add to dashboard Cite

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“…Feature extraction was performed over the filtered signals (FS), the instantaneous amplitude (IA), and the instantaneous frequency (IF), estimated from the Hilbert transform [ 32 ], as pointed out in the step 1c of Figure 3 . The following features, which are fully described in Table 1 of [ 33 , 34 ], were estimated: mean absolute value (MAV), root mean squared (RMS), global maximum (PEAK), mean of the absolute values of the second differences of the normalized signal (MAVSDN), mean of the absolute values of the second differences (MAVSD), mean of the absolute values of the first differences of the normalized signal (MAVFDN), mean of the absolute values of the first differences of the signal (MAVFD), interquartile range of the signal (INTERQ_RANGE), difference between the maximum and minimum values of a signal (RANGE), standard deviation (STD), variance (VAR), and approximate entropy.…”

Section: Methodsmentioning

confidence: 99%

On the Use oft-Distributed Stochastic Neighbor Embedding for Data Visualization and Classification of Individuals with Parkinson’s Disease

Oliveira

Machado

Andrade

2018

Computational and Mathematical Methods in Medicine

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Parkinson's disease (PD) is a neurodegenerative disorder that remains incurable. The available treatments for the disorder include pharmacologic therapies and deep brain stimulation (DBS). These approaches may cause distinct side effects and motor responses. This work presents the application of t-distributed stochastic neighbor embedding (t-SNE), which is a machine learning algorithm for nonlinear dimensionality reduction and data visualization, for the problem of discriminating neurologically healthy individuals from those suffering from PD (treated with levodopa and DBS). Furthermore, the assessment of classification methods is presented. Inertial and electromyographic data were collected while the subjects executed a sequence of four motor tasks. The results were focused on the comparison of the classification performance of a support vector machine (SVM) while discriminating two-dimensional feature sets estimated from Principal Component Analysis (PCA), Sammon's mapping, and t-SNE. The results showed visual and statistical differences for all three investigated groups. Classification accuracy for PCA, Sammon's mapping, and t-SNE was, respectively, 73.5%, 78.6%, and 96.9% for the training set and 67.8%, 74.1%, and 76.6% for the test set. The possibility of discriminating healthy individuals from those with PD treated with levodopa and DBS highlights the fact that each treatment method produces distinct motor behavior. The scatter plots resulting from t-SNE could be used in the clinical practice as an objective tool for measuring the discrepancy between normal and abnormal motor behaviors, being thus useful for the adjustment of treatments and the follow-up of the disorder.

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“…The signals collected from the gyroscope were pre-processed by using a moving average filter and a nonlinear detrending technique described in [21]. For the signals detected from the NCC system, the sequence of steps was employed, first the signal envelope of the peaks at 60 Hz component was estimated to remove the carrier signal, followed by the same sequence of steps applied to the gyroscope.…”

Section: Signal Pre-processingmentioning

confidence: 99%

A non-contact system for the assessment of hand motor tasks in people with Parkinson’s disease

et al. 2021

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Clinical diagnosis of Parkinson’s disease (PD) motor symptoms remains a problem. Most of the current studies focus on objective evaluations to make the evaluation more reliable. Most of these systems are based on the use of inertial and electromyographic sensors that require contact with the body part being assessed. Contact sensors restrict natural movement, may be uncomfortable and may require preparation of the body, which may cause irritation. As an alternative to contact sensors for the study of hand motor tasks performed by subjects with and without PD, electrical potential sensing technology is used in this research. A custom hardware has been designed to enable data collection by hand movement. A micro-machine system validated the developed system, and a relationship model was established between hand displacement and non-contact capacitive (NCC) sensor response. An experiment was conducted, including 57 subjects, 30 with PD (experimental group) and 27 healthy control group, followed by an analysis of statistical features extracted from the instantaneous mean frequency (IMNF) of NCC sensor. These results were compared with those obtained from gyroscope signals that are considered in the field to be the gold standard. As a result, NCC responses were correlated linearly with hand displacement (R2 = 0.7692 and $${\text{R}}_{\text{adj}}^{2}$$ R adj 2 = 0.7631). The statistical evaluation of IMNF features showed, that both, contact and non-contact sensors, were able to discriminate movement patterns of the control group from the experimental one. The results confirm statistical similarity between features extracted from NCC and gyroscope signals.

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Pelvic movement variability of healthy and unilateral hip joint involvement individuals

Cited by 18 publications

References 28 publications

Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis—A Scoping Review

Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis—A Scoping Review

On the Use oft-Distributed Stochastic Neighbor Embedding for Data Visualization and Classification of Individuals with Parkinson’s Disease

A non-contact system for the assessment of hand motor tasks in people with Parkinson’s disease

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