Challenges and Issues in Multisensor Fusion Approach for Fall Detection: Review Paper

Table 2

Context-aware sensors fusion.

Article	Year	Basis	Deployed sensors	Algorithm deployed	Evaluation	Performance
Brulin and Courtial [24]	2010	Fusion system architecture for fall detection	PIR, camera, thermopiles	Fuzzy logic + combination of location/posture duration	15 video sequences recorded in health smart home	Motion detection: 84%
Huang et al. [25]	2008	Intelligent cane fall detection based on sensor fusion	Laser range finder, CCD camera	Probability distribution function with relevant parameter, rule-based approach	Normal walking/fall detection experiments with cane robots	Effectiveness is confirmed through experiments
Zigel et al. [26]	2009	Fall detection based on detection of vibration and sound signals	Accelerometer, microphone	Feature extraction, Bayes decision rule classifier	Mimicking doll “Rescue Randy,” 40 drops. Other objects: 80 drops	SE (sensibility): 97.5%; SP (specificity): 98.6%
Yazar et al. [27]	2014	Multisensor system for fall detection	Vibration sensor, PIR sensors	Winner-takes-all (WTA) decision fusion algorithm	Demo including; falling person, human footstep, human motion, unusual inactivity detection	No data is provided
Toreyin et al. [28]	2008	Fall detection using multisensor signal processing	Infrared, sound sensors	Hidden Markov Models	2 minutes of walking falling and speech sounds generation	All falls are detected correctly
Ariani et al. [29]	2012	Unobtrusive falls detection with multiple persons	PIR and motion detector, pressure mats	Decision tree algorithm	3 ADL scenarios; 12 types of falls	SE: 100%; SP: 77.14%. Accuracy: 89.33%
Li et al. [21]	2013	Improvement of acoustic fall detection using Kinect depth sensing	FADE (acoustic) Kinect	Segmentation, thresholding	Recorded video data	Error reduction by 80%