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 |