Ambient assisted living is a concept which uses information and communication technology to assist the daily living of people. Human fall detection is an important sub-area of ambient assisted living. Human fall has been seen as a critical problem for elderly people. Fall detection is an approach which analyzes sensor data (wearable sensors/ambient sensors or vision-based sensor) to detect human fall using various learning algorithms. This paper presents a fall detection method that detects and notifies fall activity in real-time using fog computing. Support Vector Machine based one class classification is used here to build fall detection model. Five features have been calculated from Smartphone accelerometer data to build fall detection model. To implement one class classification, a new method for kernel matrix calculation is proposed here. This fall detection model exploited the concept of fog computing to send real-time notification to the caregiver and it is also able to notify caregiver in absence of fog node to cloud connection. In the proposed method we have got 100% sensitivity and 98.77% specificity for human fall detection. This fall detection method is also tested on real fall data and it is found that this method is able to detect 100% fall activities. Use of fog computing concept drastically reduces amount of data transferred to the cloud from 900 values (10,799 bytes) to 5 values (59 bytes) per 6 s.
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We thank all participating men and women in the FARSEEING project, as well as all FARSEEING research scientists, study and data managers and clinical and administrative staff who make the study possible.
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Shrivastava, R., Pandey, M. Real time fall detection in fog computing scenario. Cluster Comput 23, 2861–2870 (2020). https://doi.org/10.1007/s10586-020-03051-z
- Fall detection
- Fog computing
- One-class classification