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Multi-Headed Deep Learning Models to Detect Abnormality of Alzheimer’s Patients

Computer Systems Science & Engineering 2023年第1期44卷 367-390页

作者： S.Meenakshi Ammal P.S.Manoharan Department of Electrical and Electronics Engineering Thiagarajar College of EngineeringMadurai625015Tamil NaduIndia

Worldwide,many elders are suffering from Alzheimer’s disease(AD).The elders with AD exhibit various abnormalities in their activities,such as sleep disturbances,wandering aimlessly,forgetting activities,etc.,which are the strong signs and symptoms of AD *** these symptoms in advance could assist to a quicker diagnosis and treatment and to prevent the progression of Disease to the next *** proposed method aims to detect the behavioral abnormalities found in Daily activities of AD patients(ADP)using *** the proposed work,a publicly available dataset collected using wearables is ***,no real-world data is available to illustrate the daily activities of ***,the proposed method has synthesized the wearables data according to the abnormal activities of *** the proposed work,multi-headed(MH)architectures such as MH Convolutional Neural Network-Long Short-Term Mem-ory Network(CNN-LSTM),MH one-dimensional Convolutional Neural Network(1D-CNN)and MH two dimensional Convolutional Neural Network(2D-CNN)as well as conventional methods,namely CNN-LSTM,1D-CNN,2D-CNN have been implemented to model activity pattern.A multi-label prediction technique is applied to detect abnormal *** results obtained show that the proposed MH architectures achieve improved performance than the conventional ***,the MH models for activity recognition perform better than the abnormality detection.

关键词： Alzheimer’s disease abnormal activity detection classifier chain multi-headed CNN-LSTM wearable sensor

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