A Convolutional Neural Network-Based Sensor Fusion System for Monitoring Transition Movements in Healthcare Applications

By Neha Dawar, Nasser Kehtarnavaz. 2018

36 citations recorded by the source corpus.

This paper presents a convolutional neural network-based sensor fusion system to monitor six transition movements as well as falls in healthcare applications by simultaneously using a depth camera and a wearable inertial sensor. Weighted depth motion map images and inertial signal images are fed as inputs into two convolutional neural networks running in parallel, one for each sensing modality. Detection and thus monitoring of the transition movements and falls are achieved by fusing the movement scores generated by the two convolutional neural networks. The results obtained for both subject-generic and subject-specific testing indicate the effectiveness of this sensor fusion system for monitoring these transition movements and falls.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jun Liu
2. Qiuhong Ke
3. Hossein Rahmani
4. Zehua Sun
5. Chau Yuen
6. Yuren Zhou
7. Meng Zhang
8. Billy Pik Lik Lau

In Computer Vision and Pattern Recognition

1. Qiuhong Ke
2. Jun Liu
3. Hossein Rahmani
4. Zehua Sun
5. Chau Yuen
6. G. Wang
7. Amir Shahroudy
8. Mohammed Bennamoun

Citations in the corpus

• A survey of data fusion in smart city applications (2019, methodology): “We classify these approaches as ‘Higher Level Information Extraction’ class and examples can be found in [52]–[54].”
• Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “[337] represented inertial signal as an image, and utilized two CNNs to fuse the depth images and inertial signals using score fusion.”