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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%