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Table 3 Comparison of acceleration based fall detectors using external accelerometers

From: Challenges, issues and trends in fall detection systems

Article Year Basis Detection technique Fall types Subjects Declared perform Position Elders Yes/No Comments
Lindeman et al.[33] 2005 A fall detector placed at head level TBM considering the spatial direction of the head, the velocity right before the initial contact with the ground and the impact Falls to the front, side with a 90° turn, back, back with hip flexion. A young volunteer and an elderly woman (83 years) High sensitivity and specificity Ear Yes Accelerometers were integrated into a hearing-aid housing, which was fixed behind the ear
Falls backwards against a wall, while picking up an object and collapse.
Chen et al.[34] 2005 Detect the occurrence of a fall and the location of the victim TBM considering the impact and the change in orientation Backward and sideways falls 2 subjects The acceleration for ADL is much less than the observed from falling Waist No The final orientation of the wearer is considered
Zhang et al. [45] 2006 Fall detection using machine learning strategies MLM. 1) Extraction of temporal and magnitude features from the acceleration signal, 2) One-class Support Vector Machine classifier Soft fall 12 subjects (8 males, 4 females, ages 10–70) Accuracy 96,7% Waist Yes To the best of our knowledge, this study is the first in using machine learning techniques
Hard fall in the ground, stairs and slopes (using a mannequin)
Bourke et al.[35] 2007 Investigation into the ability to discriminate between falls and ADL TBM using information from the impact Forward falls, backward falls and lateral falls left and right, performed with legs straight and flexed 10 subjects (ages 21–29) Trunk Trunk, thigh Yes The trunk appears to be the optimum location for a fall sensor
SP:100%
10 community-dwelling elderly subjects (3 females, 7 males, ages 70–83) Thigh
SP: 83.3%
Doukas et al.[46] 2007 Accelerometers transmit patient movement data wirelessly to the monitoring unit MLM. The acceleration in the three axis is classified using Support Vector Machine Not specified 1 subject SE: 98.2% Foot No If a fall is suspected it also transmits video images to remote monitoring units
SP: 96.7%
Kangas et al.[36] 2008 Comparison of 3 low-complexity algorithms TBM considering the start of the fall, the velocity, the impact and the lying posture Forward, backward, and lateral falls 3 volunteers (1 female, 2 males; ages 38, 42, 48) Waist Wrist, head, waist No Waist worn accelerometer might be optimal for fall detection considering the fall associated impact and the posture after the fall
SP: 100%
SE: 98%
Kangas et al.[37] 2009 To validate the data collection of a new fall detector prototype TBM considering two or more of the following phases of a fall event: start of the fall, falling velocity, fall impact, and posture after the fall Syncope, tripping, sitting on empty air, slipping, lateral fall, rolling out of bed 20 subjects (40–65 years old), 21 voluntary older people (58–98 years old) SP: 100% Waist Yes Middle-aged persons could be considered to mimic the fall events of older people more adequately than young subjects would
SE: 97.5%
Li et al.[38] 2009 Fall detection system using both accelerometers and gyroscopes TBM analyzing the intensity of the activity, the posture and whether the transition to a lying posture was unintentional or not Forward, backward, sideways and vertical falls. Falling on stairs and fall against walls ending with a sitting position 3 male subjects (age 20) SP: 92% Chest, thigh No Human activities are divided into static postures and dynamic transitions
SE: 91%
Shan et al.[42] 2010 Investigation of a pre-impact fall detector MLM 1) A discriminant analysis is applied to time-domain statistical characteristics to select the features, 2) Support Vector Machine is used for fall recognition Forward falls, backward falls, lateral falls left and right (subjects were instructed to keep their postures for about 2 seconds after the fall) 5 male subjects (ages 21 – 28) SP: 100% Waist No Impending falls are detected in their descending phase before the body hits the ground
SE: 100%
Bianchi et al.[39] 2010 Augmentation of accelerometer-based systems with a barometric pressure sensor TBM considering the impact, the postural orientation, and the change in altitude associated with a fall Forward, backward and lateral falls (ending lying, with recovery, with attempt to break the fall) 20 subjects (12 male, 8 female; mean age: 23.7) SP: 96.5% Waist No A system based on a barometric pressure sensor is compared with an accelerometry-based technique. The acceleration and air pressure data are recorded using a wearable device
SE: 97.5%
5 subjects (2 male, 3 female; mean age: 24)
Resting against a wall, then sliding vertically down to the end in the sitting position
5 subjects (5 male, mean age: 26.4)
Bourke et al.[40] 2010 It compares novel fall-detection algorithms of varying complexity TBM considering the fall impact, the velocity and the posture Forward falls, backward falls, lateral falls left and right all performed with both legs straight and with knees relaxed 10 male subjects (age 24–35) SP: 100% Waist Yes The algorithms were tested against ADL performed by elderly subjects
10 older subjects (6 male, 4 female, age 73–90) SE: 94.6%
Lai et al.[41] 2011 Several acceleration sensors for joint sensing fall events TBM to differentiate dynamic/static states using the acceleration of the three axis Forward, backward, rightward or leftward falls 16 subjects Accuracy 92.92% Neck, hand, waist, foot No After a fall accident occurs, the system determines the level of injury
Bagala et al.[9] 2012 Benchmark the performance of published fall-detection methods when they are applied to real-world falls TBM including, among others, the algorithms published in [35, 36] Real-world falls: indoor/outdoor, forward /backward /sideward, impact against the floor /wall or locker before hitting the floor / sofa or bed/ desk 9 subjects (7 women, 2 men, age: 66.4±6.2) Average 13 algorithms Lower back Yes Algorithms that were successful at detecting simulated falls did not perform well when attempting to detect real-world falls
SP: 83.0% ±30.3%
15 subjects
29 subjects SE: 57.0%
1 subject ±27.3%
Yuwono et al.[43] 2012 Use of a sophisticated fall detection method MLM. 1) Discrete wavelet transform, 2) Associate a cluster to the input feature vector; fuse cluster information with input, 3) Combined classification (vote majority): Multilayer Perceptron and Augmented Radial Basis Function Not specified 8 individuals (age 19–28) SP: 99.6% Waist No Training and clustering are done off-line. Clustering is done using Regrouping particle swarm optimization
SE: 98.6%
Kerdegari et al.[44] 2012 Investigation of the performance of different classification algorithms MLM. Input is pre-processed using windowing techniques. Features include acceleration, angular velocity, velocity, position and time domain features: maximum, minimum, mean, range, variance and standard deviation. Several methods are compared. With flexed knees: forward, backward, sideways falls 50 volunteers (18 male, 32 female, average age 32) SE: 90.15% Waist No Multilayer Perceptron, Naive Bayes, Decision tree, Support Vector Machine, ZeroR and OneR algorithms are compared.
Base on wall: backward, sideways falls
Backward falls sitting on empty, turning left and right
Results show that the Multilayer Perceptron algorithm is the best option
Cheng et al.[47] 2013 Daily activity monitoring and fall detection TBM using a decision tree: 1) A decision tree is applied to the angles of all the body postures to recognize posture transitions, 2) the impact magnitude is thresholded to detect the falls Four types of falls: from standing to face-up lying, face-down lying, left-side lying, and right-side lying 10 subjects (6 males, 4 females, age 22–26) SE: 95.33% Chest No Dynamic gait activities are also identified using Hidden Markov Models. Surface electromyography signals are combined with the acceleration signals.
SP: 97.66% Thigh
  1. TBM: Threshold Based Method, MLM: Machine Learning Method.