Fall Detection in Video Sequences Based on a Three-Stream Convolutional Neural Network

Falls are the major cause of fatal and non-fatal injury among people aged more than 65 years. Due to the grave consequences of the occurrence of falls, it is necessary to conduct thorough research on falls. This paper presents a method for the study of fall detection using surface electromyography (sEMG) based on an improved dual parallel channels convolutional neural network (IDPC-CNN). The proposed IDPC-CNN model is designed to identify falls from daily activities using the spectral features of sEMG. Firstly, the classification accuracy of time domain features and spectrograms are compared using linear discriminant analysis (LDA), k-nearest neighbor (KNN) and support vector machine (SVM). Results show that spectrograms provide a richer way to extract pattern information and better classification performance. Therefore, the spectrogram features of sEMG are selected as the input of IDPC-CNN to distinguish between daily activities and falls. Finally, The IDPC-CNN is compared with SVM and three different structure CNNs under the same conditions. Experimental results show that the proposed IDPC-CNN achieves 92.55% accuracy, 95.71% sensitivity and 91.7% specificity. Overall, The IDPC-CNN is more effective than the comparison in accuracy, efficiency, training and generalization.

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Effective multiple person recognition in random video sequences using a convolutional neural network

Multimedia Tools and Applications ◽

10.1007/s11042-019-7323-z ◽

2019 ◽

Vol 79 (15-16) ◽

pp. 11125-11141

Author(s):

Niraimathi Puhalanthi ◽

Daw-Tung Lin

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Video Sequences ◽

Person Recognition

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Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor

Sensors ◽

10.3390/s20205957 ◽

2020 ◽

Vol 20 (20) ◽

pp. 5957

Author(s):

Shigeyuki Tateno ◽

Fanxing Meng ◽

Renzhong Qian ◽

Yuriko Hachiya

Keyword(s):

Neural Network ◽

Nursing Homes ◽

Convolutional Neural Network ◽

Motion Detection ◽

Three Dimensional ◽

Fall Detection ◽

Human Motion ◽

Portable Devices ◽

Human Motion Detection ◽

Infrared Array

Due to the rapid aging of the population in recent years, the number of elderly people in hospitals and nursing homes is increasing, which results in a shortage of staff. Therefore, the situation of elderly citizens requires real-time attention, especially when dangerous situations such as falls occur. If staff cannot find and deal with them promptly, it might become a serious problem. For such a situation, many kinds of human motion detection systems have been in development, many of which are based on portable devices attached to a user’s body or external sensing devices such as cameras. However, portable devices can be inconvenient for users, while optical cameras are affected by lighting conditions and face privacy issues. In this study, a human motion detection system using a low-resolution infrared array sensor was developed to protect the safety and privacy of people who need to be cared for in hospitals and nursing homes. The proposed system can overcome the above limitations and have a wide range of application. The system can detect eight kinds of motions, of which falling is the most dangerous, by using a three-dimensional convolutional neural network. As a result of experiments of 16 participants and cross-validations of fall detection, the proposed method could achieve 98.8% and 94.9% of accuracy and F1-measure, respectively. They were 1% and 3.6% higher than those of a long short-term memory network, and show feasibility of real-time practical application.

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