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Table 2 Fall detection techniques in the context-aware studies

From: Challenges, issues and trends in fall detection systems

  Fall detection method
1ststep 2ndstep 3rdstep 4thstep 5thstep
Lee et al.[20] Adaptive background subtraction to detect the object of interest Image processing using a connective-component labelling technique, with the end product being a ‘blob’ or silhouette Feature extraction Determination of the threshold values for each of the features based on the height of the users  
Miaou et al.[15] Background subtraction to detect the objects. Image processing: erosion and dilatation, connected component labelling technique Feature extraction (height and width of object’s silhouettes) Simple threshold-based decision algorithm for fall detection  
Vishwakarma et al.[21] Patient detection (adaptive background subtraction method using Gaussian mixture model) Feature extraction Fall detection using aspect ratio and pixel's gradient distribution and applying rule-based decisions Fall confirmation using the fall angle and applying rule-based decisions  
Cucchiara et al.[19] Extraction of moving objects using background suppression with selective and adaptive update Tracking algorithm: A probabilistic and appearance-based tracking Classification as people of tracks that satisfy some geometrical and colour constraints Posture classifier based on the projection histograms computed over the temporal probabilistic maps obtained by the tracker Hidden Markov Models formulation is adopted to classify the posture
Fu et al.[16] Extraction of changing pixels (motion events) from the background A lightweight algorithm computes the instantaneous motion vectors Fall events are reported using the temporal average of the motion events   
Hazelhoff et al.[17] Object segmentation: (background subtraction and connection of information components) Object tracking: the tracker can mark objects as non-human, which are identified based on size and absence of both motion and a head region PCA-based feature extraction: the direction of the principal component and the variance ratio are extracted Fall detection: using a multi-frame Gaussian classifier Head tracking using skin-colour model to confirm the fall
Anderson et al. [22] Silhouette extraction from each camera. Then, a 3D representation of the body is constructed Extraction of centroid, height, major orientation of the body and similarity of the major orientation with the ground plane normal Human state inferred using fuzzy logic (3 states: upright, on-the-ground and in-between) Information in sequences of states is reduced by linguistic summarization to produce human readable sentences Fall detected by a second level of fuzzy logic, taking inputs from a single summary: average state, time duration, speed, oscillation, etc.
Lie et al.[23] Human body identification using frame differencing approach Image processing: mean filter to make the image more smooth, thresholding to obtain a binary image, connected component labelling Features extraction and reduction of upper limb activities effect k-nearest neighbour classifier for human body postures classification Fall event detection flow: the decision of a fall incident is determined by the event transition and time difference between events
Rimminen et al.[28] Estimate the position of the subject using the near-field image sensor observations Tracking (Kalman filter) and multi-target tracking (Rao-Blackwellized Monte Carlo data association algorithm) Features extraction related to the NFI floor Modelling of the state evolution as a two-state Markov chain (falling, getting up) Pose estimation using Bayesian filtering. It combines the prior model with information from the features
Tzeng et al.[25] Fall suspection: Thresholding of the floor pressure signal If the floor preassure exceeds a given threshold: Image capture Background subtraction through an image thresholding. Objects labelling and expansion (morphological operations) Image features extraction Combination of the floor pressure signal and image features to report on a fall
Diraco et al.[24] Camera calibration Background modelling using Mixture of Gaussians method Moving regions detection (Bayesian segmentation) and segmented blobs refining (morphological operations and connected components) Fall suspection: The distance of the centroid from the floor plane is lower than a prefixed value Fall confirmation if an unchangeable situation persists for at least 4 seconds
Rougier et al.[14] Silhouette detection (foreground segmentation method) and edge points extraction (Canny edge detector) Silhouette edge points matching through the video sequence Shape analysis using the mean matching cost and the full Procrustes distance Fall classification: Gaussian mixture model, based on shape deformation during the fall and the subsequent lack of movement  
Li et al.[29] Locate the position of the sound source Beamforming to enhance the sound signal using the estimated source position Mel-frequency cepstral coefficients features are extracted from the sound signal A nearest neighbour classifier determines if the sound is from a fall  
Mastorakis et al.[18] Feature extraction: width, height and depth of the human posture Obtaining of the velocities of height and the composite vector of width and depth When both velocities exceed particular thresholds fall initiation is detected Inactivity detection: a fall is detected if the height velocity is less than a certain threshold  
Zhang et al.[27] Kinematic Model Based Feature Extraction from Depth Channel Person tracking by background subtraction Histogram represented features Hierarchy Support Vector Machine classification