Data portability for activities of daily living and fall detection in different environments using radar micro-doppler

• Published in: Neural computing & applications 2022-05, Vol.34 (10), p.7933-7953
• Main Authors: Shah, Syed Aziz, Tahir, Ahsen, Le Kernec, Julien, Zoha, Ahmed, Fioranelli, Francesco
• Format: Article
• Language: English
• Subjects: Activities of daily living; Algorithms; Artificial Intelligence; Artificial neural networks; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Continuous wave radar; Data Mining and Knowledge Discovery; Data transmission; Deep learning; Fall detection; Image Processing and Computer Vision; Machine learning; Neural networks; Older people; Original Article; Probability and Statistics in Computer Science; Radar; Support vector machines
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-022-06886-2

The health status of an older or vulnerable person can be determined by looking into the additive effects of aging as well as any associated diseases. This status can lead the person to a situation of ‘unstable incapacity’ for normal aging and is determined by the decrease in response to the environment and to specific pathologies with apparent decrease of independence in activities of daily living (ADL). In this paper, we use micro-Doppler images obtained using a frequency-modulated continuous wave radar (FMCW) operating at 5.8 GHz with 400 MHz bandwidth as the sensor to perform assessment of this health status. The core idea is to develop a generalized system where the data obtained for ADL can be portable across different environments and groups of subjects, and critical events such as falls in mature individuals can be detected. In this context, we have conducted comprehensive experimental campaigns at nine different locations including four laboratory environments and five elderly care homes. A total of 99 subjects participated in the experiments where 1453 micro-Doppler signatures were recorded for six activities. Different machine learning, deep learning algorithms and transfer learning technique were used to classify the ADL. The support vector machine (SVM), K-nearest neighbor (KNN) and convolutional neural network (CNN) provided adequate classification accuracies for particular scenarios; however, the autoencoder neural network outperformed the mentioned classifiers by providing classification accuracy of ~ 88%. The proposed system for fall detection in elderly people can be deployed in care centers and is application for any indoor settings with various age group of people. For future work, we would focus on monitoring multiple older adults, concurrently in indoor settings using continuous radar sensor data stream which is limitation of the present system.