From: Challenges, issues and trends in fall detection systems
Article | Year | Basis | Detection technique | Fall types | Study design | Declared perform | Position | Elders Yes/No | Comments |
---|---|---|---|---|---|---|---|---|---|
Sposaro et al.[49] | 2009 | Alert system for fall detection using smart phones | TBM considering the impact, the difference in position before and after the fall and whether the fallen patient is able to regain the upright position | Not included | Not included | Not included | Thigh (pocket) | No | First documented mobile phone-based fall detector |
The existence of a lying period after falling is checked | |||||||||
Dai et al.[50] | 2010 | Mobile phones as a platform for developing fall detection systems | TBM considering the impact, the wearer’s orientation and the common step mechanics during falling | Forward, lateral and backward falls with different speeds (fast and slow) and in different environment (living room, kitchen, etc.) | 15 participants from 20 to 30 years old (2 females, 13 males) | Good detection performance | Chest, waist, thigh | No | A detection algorithm with an external accessory is included |
Lopes et al.[51] | 2011 | Application to detect and report falls, sending SMS or locating the phone | TBM considering the impact | Fall into bed, forward fall, backward fall, fall in slow motion | Not specified | Not specified | Thigh | No | Five scenarios to validate the detector are presented. Each scenario includes ADL and falls. |
Albert et al.[54] | 2012 | Demonstrate techniques to not only reliably detect a fall but also to automatically classify the type | MLMs using a large time-series feature set from the acceleration signal. | Left and right lateral, forward trips, and backward slips | 15 subjects (8 females, 7 males, ages 22–50) | Across an average week of everyday movements there are 2–3 non-falls misclassified as falls | Back | No | Five machine learning classifiers are compared: Support vector machines, Sparse multinomial logistic regression, Naïve Bayes, K-nearest neighbours, and Decision trees |
Lee et al.[52] | 2012 | Study the sensitivity and specificity of fall detection using mobile phone technology | TBM considering the impact | Forwards, backwards, lateral left and lateral right | 18 subjects (12 males, 6 females, ages 29±8.7) | SP: 81% | Waist | No | The motion signals acquired by the phone are compared with those recorded by an independent accelerometer |
SE: 77% | |||||||||
Fang et al.[53] | 2012 | Fall detection prototype for the Android-based platform | TBM considering the impact and the patient’s orientation | Not specified | 4 subjects | SP: 73.78% | Chest, waist thigh | No | Different phone-attached locations are analysed. The chest seems to be the best place. |
SE: 77.22% | |||||||||
Abbate et al.[55] | 2012 | A system to monitor the movements of patients, recognize a fall, and automatically send a request for help to the caregivers | MLM Eight acceleration properties of fall-like events are classified using multi-layer feed-forward neural network | Forward fall, backward fall, and faint (normal speed and slow motion) | 7 volunteers (5 male, 2 female, ages 20–67) | SP: 100% | Waist | No | The proposed approach is compared with the techniques described in [35, 36, 49, 66] |
SE: 100% |