Fall Detection in Homes of Older Adults Using the Microsoft Kinect

By Erik E. Stone, M. Skubic. IEEE journal of biomedical and health informatics, 2015

534 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Physical Therapy, Sports Therapy and Rehabilitation

View Paper: https://www.semanticscholar.org/paper/b6e79a47f5e920774473c84759b9858cc2976e21

How researchers see this paper

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

In Biomedical Engineering

1. Qiang Yang
2. Lei Yang

In Computer Vision and Pattern Recognition

1. Qiang Yang
2. Ying Wei
3. Yangqiu Song
4. Yingke Zhu
5. Daqing Zhang
6. Cyril Leung
7. Lei Yang
8. Gabriella Csurka

In Physical Therapy, Sports Therapy and Rehabilitation

1. Lei Yang

Citations in the corpus

• RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices (2017, background): “Although the recent advances in infra-red LED and depth camera like Microsoft Kinect [18], [19], have enlarged its”
• UP-Fall Detection Dataset: A Multimodal Approach (2019, methodology): “An extensive dataset recollected with Microsoft Kinect was presented in [24].”
• Security and Privacy in the Internet of Things (2017, background): “are deployed in the home of old adults for fall detection [136, 74].”
• Instilling Social to Physical: Co-Regularized Heterogeneous Transfer Learning (2016, background): “Ubiquitous computing tasks, such as Human Activity Recognition (HAR), are enabling a wide spectrum of applications, ranging from healthcare (Stone and Skubic 2015) to environment monitoring (Lane et al. 2010).”