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Table 1 Comparison of context-aware systems

From: Challenges, issues and trends in fall detection systems

Article Year Basis Features used for fall detection Fall types Subjects Declared performance Type of sensor Elders Yes/No Comments
Lee et al.[20] 2005 Vision-based method for monitoring falls at home State and geometrical orientation of the silhouette at time t, spatial orientation and speed of the centre of the silhouette Fall lying down in a ‘stretched’ position and fall lying down in a ‘tucked’ position 21 subjects (age 20–40) SP: 80.5% Camera No Personalized thresholds are established based on the height of the subjects
SE: 93.9%
Miaou et al. [15] 2006 Customized fall detection system using omni-camera images The ratio of people’s height and weight Not specified 20 subjects With personal information: SP: 86% SE: 90% Camera No Determining a proper threshold statistically for different ranges of height or weight alone does not improve the system performance
Vishwakarma et al.[21] 2007 Automatic detection of human fall in video Aspect ratio, horizontal and vertical gradient distribution of object in XY plane and fall angle Sideways, forward, backward falls 1 subject SP: 100% SE: 100% Camera No Both indoor and outdoor video containing different types of possible falls are taken
Cucchiara et al.[19] 2007 A multi-camera vision system for detecting and tracking people and recognizing dangerous behaviours Geometrical and colour features together with the projection of the silhouette’s shape on the x and y axes. Not specified Not specified Difficulties with occlusions are reported Camera No If a fall is suspected it delivers live video streams to clinicians in order to check the validity of a received alarm
Fu et al.[16] 2008 Contrast vision system designed to detect accidental falls Change in illumination Backward, forward and sideways falls 3 subjects 3 possible scenarios evaluated with positive results Contrast vision sensor No Instantaneous motion vectors are computed and fall hazards are immediately reported with low computational effort
Hazelhoff et al.[17] 2008 Real-time vision system to detect fall incidents in unobserved home situations The orientation of the main axis of the person and the ratio of the variances in horizontal and vertical direction Skin colour Not specified At least 2 subjects SE: 100% when large occlusions are absent Camera No The position of the head is taken into account in order to obtain a high robustness
Anderson et al.[22] 2009 3D representation of humans (voxels) using multiple cameras. Two levels of fuzzy logic determines first a state and then activities (f.i. a fall) At low level: silhouettes from each camera, to build a set of voxels. At an intermediate level: centroid, height, major orientation of the body and similarity of the major orientation with the ground plane normal. At least, falls forward, backwards, and to the side (with recovery, attempting to get back up, lying motionless) Not specified SE: 100% Camera No The system can produce sentences like “the person is on-the-ground in the kitchen for a moderate amount of time”
SP: 93.75%
Lie et al.[23] 2010 Vision fall detection system considering privacy issues The ratio and difference of human body silhouette bounding box height and width Not specified 15 subjects (age 24–60) Accuracy 84.44% Camera No Activities are divided into three categories: standing posture, temporary posture and lying down posture
Rimminen et al. [28] 2010 Fall-detection method using a floor sensor based on near-field imaging Features related to the near-field imaging floor (the number of observations, the sum of magnitudes and dimensional features) Backward to sit, backward to lateral, to supine, onto knees, arm protect, to prone, rotate right and left, right and left lateral 10 subjects SE: 91% Near-field image sensor No The fall-detection performance is valid for multiple people in the same room
SP: 91%
Tzeng et al.[25] 2010 A system that adjusts the detection sensitivity on a case-by-case basis to reduce unnecessary alarms Floor pressure signal Backward, forward and sideways falls Not specified SP: 96.7% Pressure/ infrared sensors No The floor pressure sensor is combined with the infrared sensor
Image features: standard deviation of vertical projection histogram, standard deviation of horizontal projection histogram, and aspect ratio SE: 100%
Diraco et al.[24] 2010 An active vision system for the detection of falls and the recognition of postures for elderly homecare applications. People’s silhouette and their centre-of-mass Backward falls, forward falls, lateral falls Not specified SE: 80% Camera No Information about the 3D position of the subject is combined with the detection of inactivity.
SP: 97.3%
An approach for posture recognition is proposed
Rougier et al.[14] 2011 A vision system based on analyzing human shape deformation Some edge points from the silhouette of the person Forward falls, backward falls, falls when inappropriately sitting down, loss of balance Not specified Accuracy (falls and ADL correctly classified): 98% Camera No The fall impact is an important feature to detect a fall, but the lack of movement after the fall is crucial for robustness
Li et al.[29] 2012 Acoustic fall detection system Acoustic signals sampled at 20 KHz Backward, forward and sideways falls (balance, lose consciousness, trip, slip, reach chair, couch) 3 subjects (2 female, 1 male, ages 30, 32, 46) SE: 100% Array of microphones No The source of the sound is located.
SP: 97%
The performance of the acoustic detector is evaluated using simulated fall and nonfall sounds
Mastorakis et al.[18] 2012 Real-time fall detection system based on the Kinect sensor The width, height and depth of the human posture, which define a 3D bounding box Backward, forward and sideways falls 8 subjects All falls were accurately detected Infrared sensor No The system requires no pre-knowledge of the scene and three parameters to operate; the width, height and depth of the subject
Zhang et al.[27] 2012 Privacy Preserving Automatic Fall Detection Deformation and person’s height Fall from chair, fall from standing 5 subjects Accuracy 94% RGBD cameras No The system can handle special cases such as light turning off (insufficient illumination)