FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384407301> ?p ?o ?g. }

• W4384407301 abstract "Introduction Stroke survivors often compensate for the loss of motor function in their distal joints by altered use of more proximal joints and body segments. Since this can be detrimental to the rehabilitation process in the long-term, it is imperative that such movements are indicated to the patients and their caregiver. This is a difficult task since compensation strategies are varied and multi-faceted. Recent works that have focused on supervised machine learning methods for compensation detection often require a large training dataset of motions with compensation location annotations for each time-step of the recorded motion. In contrast, this study proposed a novel approach that learned a linear classifier from energy-based features to discriminate between healthy and compensatory movements and identify the compensating joints without the need for dense and explicit annotations. Methods Six healthy physiotherapists performed five different tasks using healthy movements and acted compensations. The resulting motion capture data was transformed into joint kinematic and dynamic trajectories. Inspired by works in bio-mechanics, energy-based features were extracted from this dataset. Support vector machine (SVM) and logistic regression (LR) algorithms were then applied for detection of compensatory movements. For compensating joint identification, an additional condition enforcing the independence of the feature calculation for each observable degree of freedom was imposed. Results Using leave-one-out cross validation, low values of mean brier score (&lt;0.15), mis-classification rate (&lt;0.2) and false discovery rate (&lt;0.2) were obtained for both SVM and LR classifiers. These methods were found to outperform deep learning classifiers that did not use energy-based features. Additionally, online classification performance by our methods were also shown to outperform deep learning baselines. Furthermore, qualitative results obtained from the compensation joint identification experiment indicated that the method could successfully identify compensating joints. Discussion Results from this study indicated that including prior bio-mechanical information in the form of energy based features can improve classification performance even when linear classifiers are used, both for offline and online classification. Furthermore, evaluation compensation joint identification algorithm indicated that it could potentially provide a straightforward and interpretable way of identifying compensating joints, as well as the degree of compensation being performed." @default.
• W4384407301 created "2023-07-16" @default.
• W4384407301 creator A5024376647 @default.
• W4384407301 creator A5030888958 @default.
• W4384407301 creator A5034380661 @default.
• W4384407301 creator A5045156654 @default.
• W4384407301 creator A5086066403 @default.
• W4384407301 date "2023-07-14" @default.
• W4384407301 modified "2023-10-01" @default.
• W4384407301 title "Online detection of compensatory strategies in human movement with supervised classification: a pilot study" @default.
• W4384407301 cites W1964825231 @default.
• W4384407301 cites W1969622459 @default.
• W4384407301 cites W1985557169 @default.
• W4384407301 cites W2027935363 @default.
• W4384407301 cites W2032277247 @default.
• W4384407301 cites W2034959629 @default.
• W4384407301 cites W2059559260 @default.
• W4384407301 cites W2065684189 @default.
• W4384407301 cites W2082198299 @default.
• W4384407301 cites W2082938394 @default.
• W4384407301 cites W2091266471 @default.
• W4384407301 cites W2092196135 @default.
• W4384407301 cites W2097950056 @default.
• W4384407301 cites W2102555719 @default.
• W4384407301 cites W2103771133 @default.
• W4384407301 cites W2108408227 @default.
• W4384407301 cites W2109606373 @default.
• W4384407301 cites W2113162436 @default.
• W4384407301 cites W2121860971 @default.
• W4384407301 cites W2130281033 @default.
• W4384407301 cites W2149441008 @default.
• W4384407301 cites W2160523181 @default.
• W4384407301 cites W2161491846 @default.
• W4384407301 cites W2169508341 @default.
• W4384407301 cites W2171428093 @default.
• W4384407301 cites W2172127559 @default.
• W4384407301 cites W2180635266 @default.
• W4384407301 cites W2235129418 @default.
• W4384407301 cites W2332243215 @default.
• W4384407301 cites W2402297854 @default.
• W4384407301 cites W2415159071 @default.
• W4384407301 cites W2512564047 @default.
• W4384407301 cites W2561184891 @default.
• W4384407301 cites W2591436322 @default.
• W4384407301 cites W2601630108 @default.
• W4384407301 cites W2773114850 @default.
• W4384407301 cites W2783290178 @default.
• W4384407301 cites W2945020349 @default.
• W4384407301 cites W2965180487 @default.
• W4384407301 cites W2983283189 @default.
• W4384407301 cites W2990671391 @default.
• W4384407301 cites W2991946719 @default.
• W4384407301 cites W2994686963 @default.
• W4384407301 cites W2998376881 @default.
• W4384407301 cites W3016503979 @default.
• W4384407301 cites W3019313496 @default.
• W4384407301 cites W3097816393 @default.
• W4384407301 cites W3108751335 @default.
• W4384407301 cites W3135691841 @default.
• W4384407301 cites W3158745887 @default.
• W4384407301 cites W3163553645 @default.
• W4384407301 cites W4205279510 @default.
• W4384407301 cites W4205515439 @default.
• W4384407301 cites W4205971750 @default.
• W4384407301 cites W4210353804 @default.
• W4384407301 cites W4210895064 @default.
• W4384407301 cites W4214581232 @default.
• W4384407301 cites W4225941083 @default.
• W4384407301 cites W4281805478 @default.
• W4384407301 cites W4290546621 @default.
• W4384407301 cites W4295135118 @default.
• W4384407301 doi "https://doi.org/10.3389/fnbot.2023.1155826" @default.
• W4384407301 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37520678" @default.
• W4384407301 hasPublicationYear "2023" @default.
• W4384407301 type Work @default.
• W4384407301 citedByCount "1" @default.
• W4384407301 countsByYear W43844073012023 @default.
• W4384407301 crossrefType "journal-article" @default.
• W4384407301 hasAuthorship W4384407301A5024376647 @default.
• W4384407301 hasAuthorship W4384407301A5030888958 @default.
• W4384407301 hasAuthorship W4384407301A5034380661 @default.
• W4384407301 hasAuthorship W4384407301A5045156654 @default.
• W4384407301 hasAuthorship W4384407301A5086066403 @default.
• W4384407301 hasBestOaLocation W43844073011 @default.
• W4384407301 hasConcept C11171543 @default.
• W4384407301 hasConcept C119857082 @default.
• W4384407301 hasConcept C121332964 @default.
• W4384407301 hasConcept C12267149 @default.
• W4384407301 hasConcept C136389625 @default.
• W4384407301 hasConcept C153180895 @default.
• W4384407301 hasConcept C154945302 @default.
• W4384407301 hasConcept C15744967 @default.
• W4384407301 hasConcept C2780023022 @default.
• W4384407301 hasConcept C39920418 @default.
• W4384407301 hasConcept C41008148 @default.
• W4384407301 hasConcept C50644808 @default.
• W4384407301 hasConcept C74650414 @default.
• W4384407301 hasConcept C95623464 @default.
• W4384407301 hasConceptScore W4384407301C11171543 @default.
• W4384407301 hasConceptScore W4384407301C119857082 @default.

• next