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Research on abnormal behavior recognition of the elderly based on spatial-temporal feature fusion

Published: 09 December 2022 Publication History

Abstract

With the deepening of the aging society, the health care of the elderly and disabled people has been widely concerned by society. It is of great practical significance to monitor the physical conditions and abnormal behaviors of elderly and disabled people based on sensor devices to prevent diseases and dangerous behaviors. However, most of the current algorithms (classical machine learning algorithms, CNN, RNN, LSTM) are only limited to the traditional Euclidean space, with little expansion in Non-Euclidean space. The translation invariance limits the expression ability of spatial data. The signals collected by the wearable sensor based on human abnormal behavior recognition often come from different parts and different directions of the human body. The traditional Euclidean algorithm is used to solve the problem, which makes the classification results easy to ignore the spatial characteristics of the sensor signals. The emergence of graph neural networks provides a new method to solve this problem. A large number of studies have proved that the graph neural network has a great role in dealing with Non-Euclidean space problems. Therefore, this paper proposes a new method for abnormal behavior recognition of the elderly based on spatiotemporal feature fusion (SCGAT). The Non-Euclidean spatial features of sensor signals are extracted by graph attention neural network, and the classification model is constructed by combining the extracted time domain features of the time-series coding network. In the experimental results, the proposed method achieves better classification results than most of the current algorithms.

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      cover image ACM Other conferences
      ISAIMS '22: Proceedings of the 3rd International Symposium on Artificial Intelligence for Medicine Sciences
      October 2022
      594 pages
      ISBN:9781450398442
      DOI:10.1145/3570773
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 09 December 2022

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      Author Tags

      1. Euclidean space
      2. Feature fusion
      3. Graph neural network
      4. Health care
      5. Sensor

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