A Vision-Based Posture Monitoring System for the Elderly Using Intelligent Fall Detection Technique

  • E. Ramanujam
  • S. Padmavathi
Part of the Computer Communications and Networks book series (CCN)


Elderly monitoring systems are the major applications of care for elderly and the disabled who live alone. Falls are the leading factor to be detected in the elderly monitoring system to avoid serious injuries and even death. The detection systems often use ambient sensors, wearable sensor, and vision-based technologies. In case of sensor-based devices, the elderly are required to wear the detection devices, however, quite often, they forget to wear these or do not wear them correctly. Moreover, the sensors need to be charged and maintained regularly. Also, the ambient sensors need to be installed in all the rooms to cover the whole actuation. The additional difficulty is that they are complex in circuitry and sensitive to temperature. Vision-based devices are the only plausible solution that can replace the aforementioned sensors. Besides, the cost of vision-based implementation is much lower and related devices are better than wearable devices in activity recognition. Much like Ambient sensors, cameras can also be installed in all the rooms; the cost and maintenance of these are less as compared to ambient sensors. This chapter proposes a vision-based posture monitoring system using infrared cameras connected to a digital video recorder and a fall detection mechanism to classify the falls. In the chapter, we observe the behavior of the elderly through the specially designed clothing fabricated with retroreflective radium tape (red in color) for posture identification. The proposed fall detection technique comprises various modules of operations such as image segmentation, rescaling, and classification. The infrared cameras observe the movement of the elderly people and signals are transmitted to a digital video recorder. The digital video recorder snaps only the motion frames from the signal. The motion images are segmented to red band using image segmentation and further rescaled for better classification using k-Nearest Neighbor and decision tree classifiers. The tests have been conducted on 10 different subjects to identify the falls during various motions such as supine, sitting, sitting with knee extension, and standing. We have shown a detection rate of 94% for the proposed model with k-nearest neighbor classifier.


Ambience intelligence AmI Falls Posture Image segmentation Ambient assistance living AAL K-Nearest neighbor Decision tree Classification Reflective tape 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Information TechnologyThiagarajar College of EngineeringMaduraiIndia

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