About: Assessment and quantification of feature uncertainty in modeling gait pattern is crucial in clinical decision making. Automatic diagnostic systems for Cerebral Palsy gait often ignored the uncertainty factor while recognizing the gait pattern. In addition, they also suffer from limited clinical interpretability. This study establishes a low-cost data acquisition set up and proposes a state-space model where the temporal evolution of gait pattern was recognized by analyzing the feature uncertainty using Dempster-Shafer theory of evidence. An attempt was also made to quantify the degree of abnormality by proposing gait deviation indexes. Results indicate that our proposed model outperformed state-of-the-art with an overall [Formula: see text] of detection accuracy (sensitivity [Formula: see text], and specificity [Formula: see text]). In a gait cycle of a Cerebral Palsy patient, first double limb support and left single limb support were observed to be affected mainly. Incorporation of feature uncertainty in quantifying the degree of abnormality is demonstrated to be promising. Larger value of feature uncertainty was observed for the patients having higher degree of abnormality. Sub-phase wise assessment of gait pattern improves the interpretability of the results which is crucial in clinical decision making.

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About: Assessment and quantification of feature uncertainty in modeling gait pattern is crucial in clinical decision making. Automatic diagnostic systems for Cerebral Palsy gait often ignored the uncertainty factor while recognizing the gait pattern. In addition, they also suffer from limited clinical interpretability. This study establishes a low-cost data acquisition set up and proposes a state-space model where the temporal evolution of gait pattern was recognized by analyzing the feature uncertainty using Dempster-Shafer theory of evidence. An attempt was also made to quantify the degree of abnormality by proposing gait deviation indexes. Results indicate that our proposed model outperformed state-of-the-art with an overall [Formula: see text] of detection accuracy (sensitivity [Formula: see text], and specificity [Formula: see text]). In a gait cycle of a Cerebral Palsy patient, first double limb support and left single limb support were observed to be affected mainly. Incorporation of feature uncertainty in quantifying the degree of abnormality is demonstrated to be promising. Larger value of feature uncertainty was observed for the patients having higher degree of abnormality. Sub-phase wise assessment of gait pattern improves the interpretability of the results which is crucial in clinical decision making. Goto Sponge NotDistinct Permalink

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value	Assessment and quantification of feature uncertainty in modeling gait pattern is crucial in clinical decision making. Automatic diagnostic systems for Cerebral Palsy gait often ignored the uncertainty factor while recognizing the gait pattern. In addition, they also suffer from limited clinical interpretability. This study establishes a low-cost data acquisition set up and proposes a state-space model where the temporal evolution of gait pattern was recognized by analyzing the feature uncertainty using Dempster-Shafer theory of evidence. An attempt was also made to quantify the degree of abnormality by proposing gait deviation indexes. Results indicate that our proposed model outperformed state-of-the-art with an overall [Formula: see text] of detection accuracy (sensitivity [Formula: see text], and specificity [Formula: see text]). In a gait cycle of a Cerebral Palsy patient, first double limb support and left single limb support were observed to be affected mainly. Incorporation of feature uncertainty in quantifying the degree of abnormality is demonstrated to be promising. Larger value of feature uncertainty was observed for the patients having higher degree of abnormality. Sub-phase wise assessment of gait pattern improves the interpretability of the results which is crucial in clinical decision making.
subject	Analysis Prediction Decision-making Disorders causing seizures Neuropsychological assessment »more»
part of	Gait Abnormality Detection in People with Cerebral Palsy Using an Uncertainty-Based State-Space Model
is abstract of	Gait Abnormality Detection in People with Cerebral Palsy Using an Uncertainty-Based State-Space Model
is hasSource of	covid:ann/target/ac7f561c2f733944d117de8f5b28f26877aad981 covid:ann/target/57ee8bb2c74efd2588547aa7438b5da94d1d95f9 covid:ann/target/42eddb00e5ac117d8b6d03d6011902f7fbff5abb covid:ann/target/61f7edf3ce02acd9c802d4f9c6fc532e3020561c covid:ann/target/8874cdc56f10d303d1e9203348b6ec678c4dfd80 »more»

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