Table 1 Summary on pre-impact fall detection studies

Wu

MCS

Horizontal and vertical profile of trunk velocity

Single THD based on the trunk velocity, THD = −1 m/s

3 SMF: tripping, forward fall, backward fall

3 Young adults

Not specified

300–400 ms

Lindemann et al.

A 3D ACCM, attached to head

Sum-vector of head acceleration

Multiple THD: (1) the sum-vector of 2D (i.e. planar) acceleration from head >2 g; (2) the sum-vector of 3D velocity >0.7 m/s

7 SMF: forward fall, backward fall, sideway fall, fall to the back with hip flexion, fall backwards against a wall, imitation of a collapse, fall while picking up an object

1 Young adult and 1 elderly

SEN = 100 %, SPE not specified

Not specified

Nyan et al.

Three 3D GYRO, attached to sternum, waist and underarm

Angular rate of sternum, waist and underarm

Multiple THD method: (1) determine the THD (for angular rate) based on a series of ADLs: (2) the THDs were ranged from 100–170 degree/s for different fall indicators

2 SMF: sideway fall; backward fall

10 Young adults

SEN = 100 %, SPE = 92.5–97.5 % (different fall types)

98–220 ms

Bourke et al.

An integrated 3D ACCM and 3D GYRO, attached to chest

Negative downward vertical velocity of trunk

Single THD based on the trunk velocity, THD = 1.3 m/s

4 SMF: forward fall with legs straight; forward fall with knee flexion; backward fall with knee flexion; side fall to right with knee-flexion

5 Young male adults

SEN = 100 %, SPE = 100 %

150–750 ms; mean = 323 ms

Bourke et al.

A 3D ACCM, attached to chest

Negative downward vertical velocity of trunk

Single THD based on the trunk velocity, THD = 1.3 m/s

4 SMF: forward fall with legs straight; forward fall with knee flexion; backward fall with knee flexion; side fall to right with knee-flexion

5 Young male adults

SEN = 100 %, SPE = 100 %

150–750 ms, mean = 323 ms

Bourke et al.

A 2D GYRO, attached to chest

Negative downward vertical velocity of trunk

Single THD based on the trunk velocity, THD = 1.3 m/s

4 SMF: forward fall with legs straight; forward fall with knee flexion; backward fall with knee flexion; side fall to right with knee-flexion

20 Young male adults

SEN = 100 %, SPE = 100 %

Not specified

Nyan et al.

(1) An integrated 3D ACCM and 3D GYRO, attached to chest; (2) an integrated 3D ACCM and 2D GYRO, attached to right thigh

(1) Sagittal and lateral angles of thigh; (2) correlation of angles between thigh and torso; (3) correlation of angular velocity between thigh and a pre-defined segment

Multiple THD: (1) the THD for thigh angles = ±10 degree; (2) the THD for correlation = 0.99

3 SMF: forward fall; backward fall; side fall

10 Young adults

SEN = 95.2 %, SPE = 100 %

727 ± 190 ms

Nyan et al.

(1) An integrated 3D ACCM and 3D GYRO, attached to chest; (2) an integrated 3D ACCM and 2D GYRO, attached to right thigh

(1) Torso segment orientation; (2)thigh segment orientation

Multiple THD: (1) 2D thigh orientation THD = ±10 degree; (2) the correlation coefficient between the thigh segment and torso segment orientation >0.8; (3) the correlation coefficient between the body segment orientation and template >0.8

3 SMF: forward fall; backward fall; side fall

13 Young male and 8 young female adults

SEN = 95.2 %, SPE = 100 %

Mean = 700 ms

Wu and Xue

An integrated 3D ACCM and 3D GYRO, attached to anterior waist

Velocity of inertial sensor frame (i.e. the waist)

Single THD: the THD was first set to −1 m/s and then set to the maximum value of vertical velocity during the non-fall activities

4 SMF: forward fall; backward fall; sideways fall; downward fall

10 Young adults and 8 old adults (ADLs)

SEN = 100 %, SPE = 100 %

70–375 ms

Shi et al.

An integrated 3D ACCM and 3D GYRO, attached to waist

Waist acceleration and angular velocity

MLM: (1) setting up a database of “falling down” and “non-falling down”; (2) feature selection based on Principle Component Analysis; (3) derive Singular Vector Machine (SVM) classifier by data training; (4) Fall detection by SVM classifier and inflation of airbag for fall detection

2 SMF: lateral fall with fast and slow fall motion

2 Young adults

SEN = 100 %, SPE = 100 %

Not specified

Tamura et al.

An integrated 3D ACCM and 3D GYRO, attached to waist

Waist acceleration and angular velocity

Multiple THD: (1) waist acceleration <3 m/s² (assumed to be free fall); (2)angular velocity >0.52 rad/s;

3 SMF: forward fall; backward fall; side fall

16 Young adults

SEN = 93.1 %, SPE not specified

Lead time not specified; the detection time = 93–197 ms

Shan and Yuan

A 3D ACCM, attached to posterior waist

Waist acceleration

MLM: (1) feature selection based on the acceleration data by a discriminant analysis; (2) support vector machine classifier was used

3 SMF: forward fall; backward fall; side fall

5 Young male adults

SEN = 100 %, SPE = 100 %

182–228 ms, mean = 204 ms

Zhao et al.

A 9-axial IMU (Xsens Tech. B.V.), attached to upper trunk

(1) Acceleration from upper trunk; (2) angular velocity of trunk

Multiple THD: (1) trunk acceleration >7 m/s2; (2) trunk angular velocity >3 degree/s

3 SMF: forward fall; backward fall; side fall

8 Young adults

Not specified

257–329 ms

Liu and Lockhart

[22, 23]

(1) MCS; (2)an IMU attached to trunk; a 3D ACCM attached to thigh

Trunk sagittal angular angle and trunk angular velocity

Individualized THD extracted from a fall discriminant function

Slip induced falls

10 Older adults

SEN = 100 %, SPE = 96.5 %

Mean detection time = 255 ms

Tong et al.

A 3D accelerometer, attached to chest

Acceleration time series (ATS) from upper trunk

MLM: (1) the ATS extracted from the accelerometer attached to the upper trunk was used to train a Hidden Markov Model (HMM); (2) The output of the HMM, known as the marching degrees (P), was compared with a pre-defined THD, i.e. P1 = 0.334 %; (3) Falls were detected if P < P1

2 SMF: forward fall; side fall

8 Young adults

SEN = 100 %, SPE = 88.75 %

200–400 ms

Martelli et al.

MCS

Linear acceleration from all body segment

MLM: (1) The linear acceleration of all the body segments was parsed by independent component analysis; (2) a Neural Network was used to classify walking from unexpected perturbations

Slip induced falls

15 Young adults

SEN = 92.7 %, SPE = 98 %

Mean detection, time = 351 ms

Aziz et al.

A 3D ACCM and a 3-axis GYRO, attached to waist

Waist acceleration, velocity and angular velocity

MLM: (1) use the means and variances of X-, Y- and Z-axis accelerations, velocities and angular velocities to form the 18 features; (2) These features were used in Support Vector Machine to for activities classification

7 Types of falls (involuntary and simulated): slip-induced fall; trip-induced fall; fall from hit or bump by an object or another person; collapse or loss of consciousness; misstep or cross step while walking and; incorrect shift of bodyweight while sitting down on or rising from a chair

10 Young adults

SEN = 93.5−100 %, SPE = 85.6−99.7 %

63–188 ms

Hu and Qu

MCS

Five fall indicators, tested separately: head vertical acceleration, upper arm vertical velocity, trunk vertical velocity, shank frontal velocity, and head frontal angular velocity

Statistical: (1) an ARIMA model based statistical process control chart was constructed based on historical movement data; (2) The individual-specific control limit based on each fall indicator was used for fall detection

Slip induced falls

60 Young adults

SEN = 88.5−94.7 %, SPE = 92.9−99.2 %

mean detection time = 620–710 ms

Sabatini et al.

An integrated device with a 3D ACCM, a 3-axis GYRO and a barometric altimeter, attached to right anterior iliac spine

Downward vertical velocity at waist

Single THD based on the trunk velocity, THD = 1.38 m/s

7 Types of falls (involuntary and simulated): slip-induced fall; trip-induced fall; fall from hit or bump by an object or another person; collapse or loss of consciousness; misstep or cross step while walking and; incorrect shift of bodyweight while sitting down on or rising from a chair

25 Young adults

SEN = 80 %, SPE = 100 %

40–300 ms mean = 157 ms

Hu and Qu

MCS

Linear combination of body kinematic measures

Statistical: (1) define the fall indicator by a linear combination of two kinematic measures; (2) determine the weighting factor of the linear combination by optimization procedure; (3) The individual-specific control limit based on each fall indicator was used for fall detection

Slip induced falls

60 Young adults

SEN = 97.3 %, SPE = 99.2 %

Not specified

Kianoush et al.

Radio frequency

Difference of RF signal fluctuation between ADLs and falls

MLM: (1) RF power fluctuation was received by the wireless link in the cover area; (2) Hidden Markov Model (HMM) was trained by the RF signal perturbations; (3) The output from the HMM model, was used to distinguish falls from other activities

2 SMF: fall from chairs; fall from stand

2 Young adults

100 % Accuracy

Not specified

Lee et al.

A 9-axial IMU, attached to waist

Negative downward vertical velocity of trunk

Single THD based on the trunk velocity: THD = 1.2 m/s for fall vs. ADL, THD = 1.4 m/s for fall vs. Non-fall

5 Types of falls (involuntary and simulated): slip-induced fall; trip-induced fall; fall from hit or bump; misstep fall; sit-to-stand fall; falls due to fainting; stand-to-sit fall

11 Young male adults were asked to mimic the falling behavior of elderly

SEN = 97.4 %, SPE = 99.4 %, (falls vs. ADL), SEN = 95.2 % SPE = 97.6 % (falls vs. near-fall)

184–231 ms