Depth Cameras in AAL Environments

Author:

Gasparrini Samuele1,Cippitelli Enea1,Spinsante Susanna1,Gambi Ennio1

Affiliation:

1. Università Politecnica delle Marche, Italy

Abstract

Automatic and privacy-preserving systems to monitor elderly people in their home environment are one of the basic targets addressed by the wide research area of Ambient Assisted Living. Thanks to the low-cost Microsoft Kinect® device, high-resolution depth and visual sensing is now not limited to experimental and prototype implementations and is ready to address marketable solutions. This chapter emphasizes the advantages provided by Kinect in the field of automatic monitoring, discussing its performance in human subject detection and tracking. Two sample use cases are discussed in detail: the former deals with generating a numerical representation of the Get Up and Go Test outcome, the latter implements an automatic fall detection algorithm based on depth frames analysis, with the sensor in a top configuration. The chapter ends suggesting issues that need to be addressed to further extend the range of applications for the Kinect device and enhance the obtainable performance.

Publisher

IGI Global

Reference54 articles.

1. Who is who at different cameras: people re-identification using depth cameras

2. Centers for Disease Control and Prevention. (2013). Falls among older adults: An overview. Retrieved February 4, 2013 from http://www.cdc.gov/HomeandRecreationalSafety/Falls/adultfalls.html

3. Towards Pervasive Physical Rehabilitation Using Microsoft Kinect

4. A Reliable Fall Detection System Based on Wearable Sensor and Signal Magnitude Area for Elderly Residents

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An Integrated Approach to Fall Detection and Fall Risk Estimation Based on RGB-Depth and Inertial Sensors;Proceedings of the 7th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion;2016-12

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