Quantifying six-minute walk induced gait deterioration with inertial sensors in multiple sclerosis subjects

Engelhard, Matthew M.; Dandu, Sriram Raju; Patek, Stephen D.; Lach, John; Goldman, Myla D.

doi:10.1016/j.gaitpost.2016.07.184

Cited by 45 publications

(39 citation statements)

References 20 publications

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“…The strong relationships of the clinical data to speed are intuitive and also confirmed by literature, which show that the 6MWT speed has the significant correlation with cognitive and physical impairment in MS, better than the available inertial features, including the gradient of the STSD [17,99]. The adjusted R 2 for cognitive/concentration corresponding to both σ st and ∆ 6M W T is a little over 50.…”

Section: Resultssupporting

confidence: 70%

“…Computation of other commonly used gait variables such as stride time SD [114], dynamic time warping (DTW) scores [17], gait phase, stride time [15], etc., depend on accurate identification of gait cycles, which is a computationally expensive and noise-sensitive process. The KDE peak is most convenient from a processing standpoint, as it is distribution based and does not require segmentation of gait data into individual gait cycles.…”

Section: Discussionmentioning

confidence: 99%

“…The authors of [17] show the capability of DTW and warp scores to detect changes in gait dynamics introduced by the 6MWT and validate them as clinical measures alongside gait speed to quantify fatigue-related gait deterioration.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In [47], Gong et al determined the correlations of total MSWS scores and other clinical measures to body sensor measurements. [17] validated warp score as an inertial measure to quantify fatigue-related gait deterioration and showed it had significant correlations to MSWS, MFIS, and two functional systems, namely, cerebellar and pyramidal. [49] showed the clinical significance of DTW and warp scores to MS symptoms specific to gait impairment.…”

Section: Metricsmentioning

confidence: 99%

“…Previous works have demonstrated the potential of using inertial gait features for quantitative mobility assessments in persons with MS. The features studied include, but are not limited to, energy expenditure estimator [13], causality index [14], stance to swing ratio [15], double support [15], Lyapunov exponent [16], and warp scores [17], each with a certain separability performance. These works primarily focus on finding gait variables that improve separability performance to distinguish between controls and MS subjects with or without reference to MS disability levels, assigned using an expanded disability status scale (EDSS) [7], and often, the relationships between inertial gait variables and clinical data are not explored.…”

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confidence: 99%

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Gait Features and their Relationships to Physiological Attributes of Multiple Sclerosis

Qureshi

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To Riaz and Saira AcknowledgementsI would like to sincerely thank my advisor, Dr. Maite Brandt-Pearce, for her relentless support, understanding, and patience. It was her persistent encouragement which pushed me to be my best and guided me to the right direction whenever I was directionless. She inspired me not only as a role model but also as a friend. Her camaraderie, exemplary work ethic, determination, and emphasis on participating in group meetings, seminars, and social activities fostered my multi-dimensional technical skills and shaped me as an independent researcher. At the same time, it instilled team spirit, mutual respect, empathy, flexibility, and numerous other personal and interpersonal skills in me, which I will cherish for the rest of my life. Assessments of MS-associated cognitive and motor disability, the disease course and its progression, and decision-making regarding disease-modifying treatments and symptoms management, are based on clinical observations, comprised of outcomes of physical examinations and medical imaging, and patient-rated questionnaires. Being reliant on physicians' judgment in interpreting imaging and clinical outcomes, affected by the differences among individuals regarding the notion of disability or improvements, time-consuming, imprecise, having limited sensitivity to subtle changes in gait, and low variance in ratings are some of the drawbacks of current subjective evaluations. Moreover, patient-reported outcomes (PROs) are subject to response shifts due to changes within individuals over time regarding health standards, and could lead to confusing findings and discrepancies between expected and observed indicators, negatively impacting disease prognosis. We intend to augment existing information, on-going research, and currently-used speed and distance-based clinical assessments, for a neurologic condition, with new, objective, and clinically meaningful anchors. Although our goals are motivated for a target application (finding physiologically meaningful gait features for assessing functional quality in MS using inertial gait data), our test measures could be adopted for gait assessments and monitoring in other neurological disorders, balance, stability, and fall risk prediction, and general health and wellness applications.We derive inertial features using angular rate gait data collected using a body sensor node for improved gait assessment with three goals -(i) using variations in gait features over time, i.e., gait dynamics, to remove the inter-subject variability and guide personalized assessments in neurology-affected locomotion, aging, or chronic diseases, (ii) finding computationally efficient and robust gait features that neither require identification of exact gait cycles nor need a large dataset to capture gait deterioration and make physical sense, (iii) translating pathology-induced fluctuations into frequency-domain features to identify the impact of MS on important gait phases.We include gait variables for comparable controls in the study to determine ...

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Section: Resultssupporting

confidence: 70%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Metricsmentioning

confidence: 99%

mentioning

confidence: 99%

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Gait Features and their Relationships to Physiological Attributes of Multiple Sclerosis

Qureshi

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Semi‐supervised clustering of quaternion time series: Application to gait analysis in multiple sclerosis using motion sensor data

Drouin

Stamm

Chevreuil³

et al. 2022

Statistics in Medicine

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Funding informationFondation pour l'Aide à la Recherche sur la Sclérose en Plaques Recent approaches in gait analysis involve the use of wearable motion sensors to extract spatio-temporal parameters that characterize multiple aspects of an individual's gait. In particular, the medical community could largely benefit from this type of devices as they could provide the clinicians with a valuable tool for assessing gait impairment. Motion sensor data are however complex and there is an urgent unmet need to develop sound statistical methods for analyzing such data and extracting clinically relevant information. In this article, we measure gait by following the hip rotation over time and the resulting statistical unit is a time series of unit quaternions. We explore the possibility to form groups of patients with similar walking impairment by taking into account their walking data and their global decease severity with semi-supervised clustering. We generalize a compromise-based method named hclustcompro to unit quaternion time series by combining it with the proper dissimilarity quaternion dynamic time warping. We apply this method on patients diagnosed with multiple sclerosis to form groups of patients with similar walking deficiencies while accounting for the clinical assessment of their overall disability. We also compare the compromise-based clustering approach with the method mergeTrees that falls into a sub-class of ensemble clustering named collaborative clustering. The results provide a first proof of both the interest of using wearable motion sensors for assessing gait impairment and the use of prior knowledge to guide the clustering process. It also demonstrates that compromise-based clustering is a more appropriate approach in this context.

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Deterioration of specific aspects of gait during the instrumented 6-min walk test among people with multiple sclerosis

et al. 2019

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Quantifying six-minute walk induced gait deterioration with inertial sensors in multiple sclerosis subjects

Cited by 45 publications

References 20 publications

Gait Features and their Relationships to Physiological Attributes of Multiple Sclerosis

Gait Features and their Relationships to Physiological Attributes of Multiple Sclerosis

Semi‐supervised clustering of quaternion time series: Application to gait analysis in multiple sclerosis using motion sensor data

Deterioration of specific aspects of gait during the instrumented 6-min walk test among people with multiple sclerosis

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