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Table 4 Smartphone based fall detectors

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%