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Table 1 Study Characteristics

From: Novel sensing technology in fall risk assessment in older adults: a systematic review

Author/Year Faller Identification Method Population/Sample Size/Age (Mean ± SD) Technology Sensor placement if applicable Test Protocol Outcome Measures Model Model validation Accuracy Specificity Sensitivity AUC
Bautmans et al. 2011 [34] Fall history (> = 1) in past 6- month, or TUG >15 s, or Tinetti <=24 F (n = 40, 80.6 ± 5.4),
NF (n = 41, 79.1 ± 4.9),
YA (n = 40, 21.6 ± 1.9)
A single Tri-axial Accelerometer Sacrum Straight line walking Gait Speed, Step time symmetry, step/stride regularity Logistic regression.
Only gait speed was effective for discrimination analysis
NA 77 78 78 0.83
Caby et al. 2011 [35] Fall history (> = 1) in past 1-year, with additional physician screening F (n = 15, 80.1 ± 5.3),
NF (n = 5, 83.2 ± 4.3)
10 Tri-axial Accelerometer sensor network Knee, Ankle, Elbow, Wrist, Shoulder Straight line walking 67 gait acceleration features extracted(temporal, frequency, power, and correlation between sensors) RBFN SVM, KNN, NB Leave-one-out cross validation 75-100 40-100 93-100  
Jansen et al. 2011 [36] Fall history (> = 1) unknown length, or TUG >15 s, or Tinetti <=24 F (n = 40, 80.6 ± 5.4),
NF (n = 40, 79.0 ± 5.0)
A single Tri-axial Accelerometer Sacrum Straight line walking 22 acceleration features 5 groups (step count, step time, step length, step symmetry and step RMS) NB,MLP,SVM, LWL, Decision Tree, NEAT Ten-fold cross validation (Max value) 61-82 62-84 58-80  
Liu et al. 2011 [37] Fall history (> = 1) in past 1-year OA (n = 68, 80.1 ± 4.4; MF/NMF = 9/59) MF (> = 2 falls) A Tri-axial Accelerometer Waist TUG,AST,STS5 126 features (temporal, energy, spectral) Linear multiple regression, Leave-one-out cross validation 78 90 59  
Marschollek et al. 2011 [38] 1-year prospective fall occurrence (> = 1) OA (n = 46, 81.3; F/NF = 19/27) A Tri-axial Accelerometer Waist TUG, Straight line walking Kinetic Energy,
Pelvis Sway,
Gait variability,
Step time/length, number of steps for TUG, spectral density parameters
Decision tree, logistic regression Ten-fold cross validation (mean value) 65-80 78-96 42-74 0.65-0.87
Paterson et al. 2011 [40] 1-year prospective fall occurrence (> = 1) F (n = 54, 69.0 ± 6.9)
NF (n = 43, 68.4 ± 7.3)
Two Tri-axial Accelerometers Foot mount 7 min walking on a circuit Stride dynamic (Fractal Scaling Index) Logistic regression NA 67 58.1 74.1  
Weiss et al. 2011 [39] Fall history, past 1-year (> = 2) F (n = 23, 76.0 ± 3.9)
NF (n = 18, 68.3 ± 9.1)
A Tri-axial Accelerometer Lower back TUG Duration of TUG and subtasks, acceleration range and Jerk. Number of steps for TUG, gait speed Logistic regression NA 63.4-87.8 50.0-83.3 65.2-91.3  
Yamada et al. 2011 [20] Fall history (> = 1) in past 1-year F (n = 16, 84.8 ± 10.1)
NF (n = 29, 80.2 ± 6.4)
Wii Balance Board NA Game-based measure in seated/standing Game score Discriminate analysis NA 88.6    
Greene et al. 2012 [21] 2-year prospective fall occurrence (> = 2) F (n = 83, 71.8 ± 6.9)
NF (n = 143, 71.4 ± 6.6)
Two Tri-axial Inertial sensors (accelerometer/gyroscope) Shank TUG 44 features (spatial/temporal gait parameters, angular velocity parameters, turn parameters) Discriminate classifier Ten-fold cross validation (mean value) 73-83 73-96 56-90 0.74-0.85
Greene et al. 2012 [22] Fall history (> = 2, or one fall requiring medical attention) in past 1-year F (n = 65, 74.0 ± 5.8)
NF (n = 55, 73.3 ± 5.8)
A Tri-axial Inertial sensor (accelerometer/gyroscope) Lower back L3 Standing balance (EO/semi- tandem, EC/narrow stance) RMS of AP/ML acceleration, frequency variability, spectral entropy SVM Ten-fold cross validation (mean value) 63-72 58-82 59-67  
Schwesig et al. 2012 [23] 1 year prospective fall occurrence (> = 1) OA (n = 141, 82.7; MF/NMF = 17/124, MF (> = 3 falls) Two Tri-axial Inertial sensors (accelerometer/gyroscope) Shoe-mounted Straight line walking Temporal gait parameters Logistic regression, ROC curve NA   42-61 63-100 0.66-0.7
Senden et al. 2012 [24] Tinetti <=24 F (n = 50, 79 ± 6)
NF (n = 50, 74 ± 5)
A Tri-axial Accelerometer Sacrum Straight line walking spatial-temporal gait parameters, step time symmetry, harmonic ratio, inter-stride variability, RMS acceleration Linear regression, ROC curve NA     0.67-0.85
Doheny et al. 2013 [25] Fall history, past 1-year (> = 2, or one fall requiring medical attention) F (n = 19, 74.9 ± 7.0)
NF (n = 20, 68.4 ± 6.2)
Two Tri-axial Inertial sensors (accelerometer/gyroscope) Sternum, Thigh STS5 Total Time, Sub-phase time, Spectral Edge Frequency, postural sway (RMS acceleration), Logistic regression Leave-one-out cross validation 74.4 80 68.7 0.70
Doi et al. 2013 [26] 1 year prospective fall occurrence (> = 1) F (n = 16, 84.8 ± 5.9)
NF (n = 57, 79.7 ± 8.2)
Two Tri-axial Accelerometer Upper/lower trunk Straight line walking Harmonic Ratio Logistic regression, ROC curve NA   84.2 68.8 0.81
Riva et al. 2013 [28] Fall history (> = 1) in past 1-year F (n = 44, 63.3 ± 6.4)
NF (n = 90, 62.0 ± 6.1)
Tri-axial Accelerometer Lower back Treadmill walking Harmonic Ratio, Index of harmonicity, Multiscale Entropy, Recurrence quantification analysis parameters Logistic regression NA 71-72.5 96.6 16.7-21.4  
Nishiguchi et al. 2013 [27] Fall history (> = 1) in past 1-year F (n = 41, 75.4 ± 4.6)
NF (n = 111, 73.5 ± 4.6)
Laser Range Finder NA Choice Stepping Test Step reaction time, error rate, stepping –response score Logistic regression, ROC curve NA   69.7 73.0 0.73
Colagiorgio et al. 2014 [29] Combination of (Tinetti + BBS + BESTest) < 29 / 33 OA (n = 66, 76 ± 10, F/NF = 22/44)
YA (n = 13, 26 ± 5)
Microsoft Kinect NA Standing balance(EO,EC, Nudged on firm surface or foam surface), Reaching forward, Stand-to -Sit, Sit-to- Stand, AST 80 features (COM postural sway, Chest Pitch Angle, velocity of transition, velocity of stepping) Majority Classifier,
Decision Tree, SVM,KNN, NB
.632 bootstrap technique 47.9-84.3 47.8-91.3 47.7-83.1  
Simila et al. 2014 [31] BBS < =49 OA (n = 20, 76.8 ± 5.6)
YA (n = 19, 27.5 ± 4.4)
NP (n = 15, 55.2 ± 7.3)
Tri-axial Accelerometer Lower back BBS, straight line walking Resultant acceleration in each task, gait pattern as measured by averaged acceleration in each step KNN, ROC curve NA 60.8-87.2 62-96.6 42.1-89.5 0.66-0.89
Kargar et al. 2014 [30] Physician examination OA (n = 12, 65 -90; F/NF = 7/5) Microsoft Kinect NA TUG Number of steps of TUG, step time, turn duration SVM Leave-one-out cross validation 67.4 67.5 67.3  
Kwok et al. 2015 [32] 1 year prospective fall occurrence (> = 1) F (n = 18, 70.7 ± 5.2)
NF (n = 55, 69.7 ± 7.8)
Wii balance board NA Standing balance (EO) Mean sway velocity Logistic regression, ROC curve NA     0.67-0.71
Howcroft et al. 2016 [10] Fall history (> = 1) in past 6-month F (n = 24, 76.3 ± 7.0)
NF (n = 76, 75.2 ± 6.6)
Pressure sensing insole, Tri-axial Accelerometers Head, Pelvis, Shank, Shoe Single/Dual task straight line walking COP path parameters, temporal gait parameters, Harmonic Ratio, Maximum Lyapunov exponent(local dynamic stability) MLP, NB, SVM Hold out method (75% training set, 25% test set) 72-84 73.7-100 33.3-100  
Howcroft et al. 2017 [33] 6- month prospective fall occurrence (> = 1) F (n = 28, 75.0 ± 8.2)
NF (n = 47, 75.3 ± 5.5)
Pressure sensing insole, Tri-axial Accelerometers Head, Pelvis, Shank, Shoe Single/Dual task straight line walking COP path parameters, temporal gait parameters, Harmonic Ratio, Maximum Lyapunov exponent(local dynamic stability) MLP, NB, SVM Hold out method (75% training set, 25% test set) 49.2-56.5 52.7-66.6 27.0-46.3  
  1. OA Older Adult, YA Young Adult, NP Neurological Patient, F Faller, NF Non-Faller, MF Multiple Faller; NMF Non-Multiple Faller