Dynamic time warping approach for optimized locomotor impairment detection using biomedical signal processing

Adhikary, Subhrangshu; Ghosh, Arindam

doi:10.1016/j.bspc.2021.103321

Cited by 18 publications

(10 citation statements)

References 63 publications

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“…Selecting the gesture candidates for recognizing real-time hand gestures of a Kinect sensor is another application of the dynamic time warping [30,31]. Additional uses involve identifying Alzheimer disease by comparing foot movements [32,33], fnding the imputation of missing values for univariate time series data [34], and diferentiating bee propolis based on its geographical origin [35].…”

Section: Dynamic Time Warpingmentioning

confidence: 99%

Real-Time and Automatic System for Performance Evaluation of Karate Skills Using Motion Capture Sensors and Continuous Wavelet Transform

Fathalla

Salah

Bekhit

et al. 2023

International Journal of Intelligent Systems

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In sports science, the automation of performance analysis and assessment is urgently required to increase the evaluation accuracy and decrease the performance analysis time of a subject. Existing methods of performance analysis and assessment are either performed manually based on human experts’ opinions or using motion analysis software, i.e., biomechanical analysis software, to assess only one side of a subject. Therefore, we propose an automated system for performance analysis and assessment that can be used for any human movement. The performance of any skill can be described by a curve depicting the joint angle over the time required to perform a skill. In this study, we focus on only 14 body joints, and each joint comprises three angles. The proposed system comprises three main stages. In the first stage, data are obtained using motion capture inertial measurement unit sensors from top professional fighters/players while they are performing a certain skill. In the second stage, the collected sensor data obtained are input to the biomechanical software to extract the player’s joint angle curve. Finally, each joint angle curve is processed using a continuous wavelet transform to extract the main curve points (i.e., peaks and valleys). Finally, after extracting the joint curves from several top players, we summarize the players’ curves based on five statistical indicators, i.e., the minimum, maximum, mean, and mean ± standard deviation. These five summarized curves are regarded as standard performance curves for the joint angle. When a player’s joint curve is surrounded by the five summarized curves, the performance is considered acceptable. Otherwise, the performance is considered unsatisfactory. The proposed system is evaluated based on four different karate skills. The results of the proposed system are identical to the decisions of the expert panels and are thus suitable for real-time decisions.

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Section: Dynamic Time Warpingmentioning

confidence: 99%

Real-Time and Automatic System for Performance Evaluation of Karate Skills Using Motion Capture Sensors and Continuous Wavelet Transform

Fathalla

Salah

Bekhit

et al. 2023

International Journal of Intelligent Systems

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“…The most common one in shape analysis of time series is the DTW dissimilarity 8 . It has been widely used in the field of clinical gait analysis to compare the shape of walking data measured by optical device 10,12,29 and wearable sensors 9,11,30,31 …”

Section: A Semi‐supervised Clustering Framework For Unit Quaternion T...mentioning

confidence: 99%

“…8 It has been widely used in the field of clinical gait analysis to compare the shape of walking data measured by optical device 10,12,29 and wearable sensors. 9,11,30,31 DTW determine the optimal nonlinear alignment between the elements of two time series Q i = (q i,1 , … , q i,N 1 ) of size N i and Q j = (q j,1 , … , q j,N j ) of size N j using the following the following formula: 32…”

Section: Dissimilarity Measure For Qts: Qdtwmentioning

confidence: 99%

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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“…Some researchers have used state-of-art methods, such as artificial intelligence (AI), internet of things (IoT), machine learning (ML), and deep learning for healthcare systems [ 1 , 2 ]. These methods are also utilized for studying various aspects of HHC [ 26 ].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

An intelligent algorithm to evaluate and improve the performance of a home healthcare center considering trust indicators

Torabzadeh¹,

Tavakkoli–Moghaddam²,

Samieinasab³

et al. 2022

Computers in Biology and Medicine

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Dynamic time warping approach for optimized locomotor impairment detection using biomedical signal processing

Cited by 18 publications

References 63 publications

Real-Time and Automatic System for Performance Evaluation of Karate Skills Using Motion Capture Sensors and Continuous Wavelet Transform

Real-Time and Automatic System for Performance Evaluation of Karate Skills Using Motion Capture Sensors and Continuous Wavelet Transform

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

An intelligent algorithm to evaluate and improve the performance of a home healthcare center considering trust indicators

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