Smart walking assistant (SWA) for elderly care using an intelligent realtime hybrid model

Bhattacharjee, Pratik; Biswas, Suparna

doi:10.1007/s12530-021-09382-5

Cited by 12 publications

(2 citation statements)

References 35 publications

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“…Al-Kababji et al [ 10 ] proposed a novel IoT-based fall-detection system that included a sensing device that transmitted data to a mobile application through a gateway device connected to the cloud. Bhattacharjee et al [ 11 ] developed a smart walking assistant (SWA) for elderly care using an intelligent real-time hybrid model, which was capable of identifying whether the faller had recovered within a stipulated period of time. In the case of fall without recovery, an alert message along with date, time, and location of fall would be sent.…”

Section: Related Workmentioning

confidence: 99%

Skeleton-Based Fall Detection with Multiple Inertial Sensors Using Spatial-Temporal Graph Convolutional Networks

Yan

Wang

Shi

et al. 2023

Sensors

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The application of wearable devices for fall detection has been the focus of much research over the past few years. One of the most common problems in established fall detection systems is the large number of false positives in the recognition schemes. In this paper, to make full use of the dependence between human joints and improve the accuracy and reliability of fall detection, a fall-recognition method based on the skeleton and spatial-temporal graph convolutional networks (ST-GCN) was proposed, using the human motion data of body joints acquired by inertial measurement units (IMUs). Firstly, the motion data of five inertial sensors were extracted from the UP-Fall dataset and a human skeleton model for fall detection was established through the natural connection relationship of body joints; after that, the ST-GCN-based fall-detection model was established to extract the motion features of human falls and the activities of daily living (ADLs) at the spatial and temporal scales for fall detection; then, the influence of two hyperparameters and window size on the algorithm performance was discussed; finally, the recognition results of ST-GCN were also compared with those of MLP, CNN, RNN, LSTM, TCN, TST, and MiniRocket. The experimental results showed that the ST-GCN fall-detection model outperformed the other seven algorithms in terms of accuracy, precision, recall, and F1-score. This study provides a new method for IMU-based fall detection, which has the reference significance for improving the accuracy and robustness of fall detection.

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Section: Related Workmentioning

confidence: 99%

Skeleton-Based Fall Detection with Multiple Inertial Sensors Using Spatial-Temporal Graph Convolutional Networks

Yan

Wang

Shi

et al. 2023

Sensors

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“…At the same time, due to limited care service resources, it is necessary to improve their utilization rate through precision care services to achieve sustainable care services for elderly individuals. Terefore, precision care services for elderly individuals have become a hot research issue [2][3][4]. Precision care services are mainly studied from two approaches.…”

Section: Introductionmentioning

confidence: 99%

Ability Grouping of Elderly Individuals Based on an Improved K‐Prototypes Algorithm

Yan-fang

Chu

2023

Mathematical Problems in Engineering

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Elderly individuals with similar abilities are likely to have similar care needs. Abilities are the basis for analysing the need patterns of elderly individuals. Abilities of elderly individuals are both numerical and categorical; therefore, an improved K-prototypes cluster algorithm for mixed attributes was proposed to group elderly individuals based on abilities. In this algorithm, the dissimilarity measure of categorical attributes is designed to consider not only the difference between the object to be allocated and the prototype of the cluster but also the differ6ence between the object to be allocated and other objects in the cluster and can measure the difference in sequential values of ordinal categorical attributes. Experimental results show that the improved K-prototypes algorithm performs well for clustering datasets containing mixed attributes. Taking the CHARLS dataset as an example, distinct groups of elderly individuals based on the improved K-prototypes algorithm showed significant ability differences.

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An Analysis of Current Advancements: Elderly Fall Detection Systems Using Machine Learning Techniques

Modak,

Roy,

Ghosh

et al. 2023

Communications in Computer and Information Science

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Smart walking assistant (SWA) for elderly care using an intelligent realtime hybrid model

Cited by 12 publications

References 35 publications

Skeleton-Based Fall Detection with Multiple Inertial Sensors Using Spatial-Temporal Graph Convolutional Networks

Skeleton-Based Fall Detection with Multiple Inertial Sensors Using Spatial-Temporal Graph Convolutional Networks

Ability Grouping of Elderly Individuals Based on an Improved K‐Prototypes Algorithm

An Analysis of Current Advancements: Elderly Fall Detection Systems Using Machine Learning Techniques

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