Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics
<p>Descriptive summary of geriatrician’s fall classification versus subjects’ gender, height, and weight. (<b>a</b>) Represents the percentage of fallers and non-fallers in each group of females and males separately; (<b>b</b>) shows the joint distribution of fallers and non-fallers with respect to subjects’ height and body mass.</p> "> Figure 2
<p>The neck, right and left foot kinematics signals of a subjects’ TUG test over time. (<b>a</b>) Acceleration signals; (<b>b</b>) angular velocity signals.</p> "> Figure 3
<p>The CNN architecture of the proposed fall-risk classification model. The 3-channel acceleration or angular velocity 3 s segments are fed into the convolutional building blocks, and the high-level kinematics feature map is extracted. The features are flattened and classified as faller/non-faller by a fully connected neural network.</p> "> Figure 4
<p>Comparison of models’ prediction performance. (<b>a</b>) Represents the models’ trade-off between sensitivity and specificity; (<b>b</b>) compares the best models’ F1-score with the traditional TUG; (<b>c</b>) illustrates the overall power of faller/non-faller discrimination for the best models and the traditional TUG.</p> ">
Abstract
:1. Introduction
- The geriatrician’s fall-risk assessment is facilitated by combining an affordable and convenient way of measuring patients’ gait and balance. This inexpensive method can provide performance comparable to the human clinician’s assessment.
- This is the first paper to compare a prediction model with a geriatrician’s assessment of fall risk, which synthesizes information on fall risk factors (medical health status, gait impairments, and fall history), rather than only relying on the fall incidents, which can increase the error of false negatives.
- Sensor location was navigated to guarantee data acquisition from three important body points that we consider relevant to fall-risk prediction. Comparison of kinematics data from three sensor locations is conducted to investigate the most effective measurement of risk factors.
2. Materials and Methods
2.1. Population
2.2. Data Acquisition
2.3. CNN Model with the Segmented Raw Signals of the TUG Test
3. Results
3.1. The Clinical Scoring Tests in Predicting Geriatrician’s Fall Classification
3.2. The CNN Prediction of Geriatrician’s Fall Classification
3.3. The CNN Prediction of the Follow-Up Falls Report
3.4. The Geriatrician’s Classification of the Follow-Up Falls Report
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Tests | Function | Measurement | Assessment | Average Completion Time |
---|---|---|---|---|
FSST | Stepping over multiple low objects in different directions. | completion time | dynamic standing stability | <5 m |
TUG test | Standing up from a chair, walking for three meters, turning, walking back to the chair, and sitting down. | completion time | gait and balance | <2 m |
FRT | Measures the maximum forward reach without moving the feet (while standing in a fixed position). | maximum forward reach | stability and balance | <2 m |
Step test | Stepping on the same foot on a stair without moving the other foot for 15 s. | number of steps | dynamic standing stability | <1 m |
BBS | Performing 14 static and dynamic balance-related tasks, including standing, sitting, turning, reaching forward. | a total score of all the tasks | stability and balance | >15 m |
4-stage balance | Standing in 4 different foot positions, in each stage, not moving the feet while keeping the balance. | total time of keeping the balance | stability and balance | <2 m |
30 sec stand | Standing up from a chair and sitting back, repeating this move for 30 s. | number of stands, age- and gender-dependent | functional lower extremity strength | <2 m |
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Features | Clinical Tools | ||||||
---|---|---|---|---|---|---|---|
FSST | Step Test | TUG | FRT | BBS | 4-Stage Balance | 30 Sec Stand | |
Time required <a couple of minutes | + | + | + | + | − | + | + |
Ease of performing | − | − | + | + | − | − | − |
Measures static stability | − | − | + | + | + | + | − |
Measures dynamic stability | + | + | + | − | + | − | + |
Gait motion | − | − | + | − | − | − | − |
Turning motion | − | − | + | − | + | − | − |
Sitting and Standing motions | − | − | + | − | + | − | + |
Reaching forward | − | − | − | + | + | − | − |
Stepping | + | + | + | − | − | − | − |
Total | 3 | 3 | 8 | 4 | 5 | 2 | 3 |
Summary Statistics | Age (Years) | Gender (Female vs. Male) | BMI (kg/m2) | # of Diagnoses | # of Movement Disorders | # of Medications | # of Psychoactive Medications | TUG (14 s or > vs. <14 s) | 4-Stage Balance (30 s or < vs. >30 s) | 30 Sec Stand (8 or < vs. >8 Stands) | SIB Score (4 or > vs. 0–3) |
---|---|---|---|---|---|---|---|---|---|---|---|
Mean (range) | 75.41 (65–96) | 49% | 28.8 (18.30–47.74) | 8.43 (1–19) | 0.47 (0–4) | 7.70 (0–21) | 0.91 (0–5) | 14.1 (7–98) | 31.4 (4–40) | 10.55 (0–23) | 3.24 (0–12) |
Odds ratio of being a faller (95% CI) | 1.09 (1.03–1.16) | 2.10 (0.94–4.67) | 1.08 (1.00–1.16) | 1.44 (1.23–1.69) | 2.56 (1.21–5.39) | 1.34 (1.17–1.55) | 1.66 (1.13–2.44) | 10.25 (3.51–29.96) | 28.66 (7.81–105.71) | 14.33 (3.96–51.87) | 44.00 (9.57–202.35) |
p-value | 0.004 | 0.070 | 0.042 | <0.001 | 0.014 | <0.001 | 0.009 | <0.001 | <0.001 | <0.001 | <0.001 |
Fall-Risk Assessment Tools (Fallers vs. Non-Fallers) | Acc (%) | Se (%) | Sp (%) | AUC | J Index | Optimal Cut-Off |
---|---|---|---|---|---|---|
TUG (14 s or > vs. <14 s) | 71.00 | 55.55 | 89.13 | 0.72 | 0.45 | 14 |
4-stage balance (30 s or < vs. >30 s) | 81.00 | 70.37 | 93.48 | 0.82 | 0.64 | 32 |
30 sec stand (8 or < vs. >8 stands) | 70.00 | 50.00 | 93.47 | 0.71 | 0.43 | 10 |
Sensor | Classification Method | Acc (%) | Se (%) | Sp (%) | J Index | F1-Score | AUC | C-Statistic (95% CI) | C-Statistic p-Value |
---|---|---|---|---|---|---|---|---|---|
- | Clinical TUG test | 70.65 (53.80, 85.78) | 56.02 (27.48, 81.64) | 88.53 (67.70, 100) | 0.44 (0.10, 0.73) | 0.67 (0.41, 0.85) | 0.72 (0.55, 0.87) | 25.70 (0.71, 0.74) | <0.001 |
Neck | SVM_gyro | 67.13 (50.00, 80.00) | 92.51 (72.72, 100) | 36.11 (11.11, 66.67) | 0.29 (0.04, 0.57) | 0.81 (0.69, 0.92) | 0.70 (0.51, 0.90) | 18.57 (0.68, 0.73) | <0.001 |
SVM_accel | 62.39 (46.87, 75.00) | 83.14 (54.54, 100) | 36.57 (4.17, 66.67) | 0.21 (0.02, 0.46) | 0.77 (0.62, 0.88) | 0.71 (0.51, 0.87) | 23.05 (0.69, 0.73) | <0.001 | |
CNN_gyro | 66.21 (50.00, 80.00) | 86.51 (56.82, 100) | 41.27 (11.11, 66.67) | 0.28 (0.05, 0.57) | 0.80 (0.67, 0.92) | 0.75 (0.54, 0.92) | 25.20 (0.73, 0.77) | <0.001 | |
CNN_accel | 63.08 (50.00, 75.00) | 75.47 (45.45, 100) | 47.93 (22.22, 66.67) | 0.25 (0.01, 0.48) | 0.75 (0.55, 0.88) | 0.73 (0.49, 0.89) | 20.32 (0.71, 0.75) | <0.001 | |
Right | SVM_gyro | 56.06 (50.00, 67.87) | 98.00 (81.82, 100) | 4.89 (0.00, 33.33) | 0.05 (0.00, 0.33) | 0.76 (0.71, 0.84) | 0.52 (0.41, 0.71) | 3.33 (0.51, 0.53) | <0.001 |
SVM_accel | 55.35 (55.00, 60.00) | 99.64 (95.22, 100) | 1.22 (0.00, 11.11) | 0.01 (0.00, 0.11) | 0.76 (0.73, 0.79) | 0.50 (0.43, 0.55) | 1.55 (0.49, 0.51) | 0.061 | |
CNN_gyro | 59.77 (45.00, 79.50) | 83.18 (46.82, 100) | 31.04 (0.00, 66.67) | 0.17 (0.00, 0.56) | 0.75 (0.59, 0.91) | 0.66 (0.47, 0.84) | 14.82 (0.64, 0.68) | <0.001 | |
CNN_accel | 58.33 (45.00, 75.00) | 79.57 (36.36, 100) | 32.37 (0.00, 66.67) | 0.16 (0.00, 0.44) | 0.72 (0.50, 0.86) | 0.61 (0.38, 0.81) | 9.92 (0.58, 0.63) | <0.001 | |
Left | SVM_gyro | 56.65 (55.00, 65.00) | 99.36 (90.91, 100) | 4.55 (0.00, 22.22) | 0.04 (0.00, 0.22) | 0.77 (0.73, 0.82) | 0.53 (0.43, 0.66) | 4.77 (0.52, 0.54) | <0.001 |
SVM_accel | 55.00 (55.00, 55.00) | 100 (100, 100) | 0.00 (0.00, 0.00) | 0.00 (0.00, 0.00) | 0.76 (0.76, 0.76) | 0.50 (0.50, 0.50) | −1.00 (0.49, 0.50) | 0.841 | |
CNN_gyro | 60.91 (45.00, 73.50) | 81.13 (39.09, 100) | 36.20 (0.00, 66.67) | 0.19 (0.00, 0.45) | 0.75 (0.51, 0.86) | 0.68 (0.48, 0.88) | 15.71 (0.65, 0.70) | <0.001 | |
CNN_accel | 59.41 (40.00, 78.50) | 82.89 (36.36, 100) | 30.70 (0.00, 66.67) | 0.18 (0.00, 0.53) | 0.71 (0.00, 0.90) | 0.63 (0.37, 0.84) | 10.45 (0.61, 0.65) | <0.001 |
Sensor | Classification Method | Acc (%) | Se (%) | Sp (%) | J Index | F1-Score | AUC | C-Statistic (95% CI) | C-Statistic p-Value |
---|---|---|---|---|---|---|---|---|---|
Neck | SVM_gyro | 70.00 (58.19, 72.22) | 2.20 (0.00, 20.00) | 96.08 (76.92, 100.00) | −0.02 | 0.16 (0.00, 0.43) | 0.50 (0.36, 0.69) | 0.34 (0.49, 0.52) | 0.367 |
SVM_accel | 69.00 (47.08, 72.22) | 1.20 (0.00, 20.00) | 95.08 (65.19, 100) | −0.04 | 0.04 (0.00, 0.26) | 0.53 (0.36, 0.68) | 2.92 (0.51, 0.54) | 0.002 | |
CNN_gyro | 60.46 (44.44, 72.16) | 42.35 (0.00, 83.5) | 67.42 (37.11, 100) | 0.07 (−0.37, 0.42) | 0.41 (0.00, 0.69) | 0.56 (0.33, 0.74) | 4.27 (0.54, 0.58) | <0.001 | |
CNN_accel | 54.71 (27.78, 72.22) | 28.61 (0.00, 100) | 64.74 (19.61, 84.61) | −0.06 1 | 0.26 (0.00, 0.63) | 0.46 (0.16, 0.81) | −2.13 (0.43, 0.49) | 0.983 | |
Right | SVM_gyro | 71.78 (66.67, 72.22) | 1.40 (0.00, 20.00) | 98.77 (88.27, 100) | 0.00 (−0.08, 0.12) | 0.11 (0.00, 0.31) | 0.49 (0.37, 0.60) | −0.25 (0.49, 0.51) | 0.599 |
SVM_accel | 70.50 (61.11, 72.22) | 0.60 (0.00, 10.50) | 97.38 (84.61, 100) | −0.02 1 | 0.12 (0.00, 0.29) | 0.49 (0.31, 0.66) | −0.12 (0.48, 0.51) | 0.548 | |
CNN_gyro | 50.38 (26.11, 72.22) | 44.00 (0.00, 100) | 54.10 (0.00, 88.84) | −0.12 1 | 0.52 (0.00, 1) | 0.48 (0.26, 0.68) | −1.83 (0.45, 0.50) | 0.966 | |
CNN_accel | 49.72 (27.78, 72.22) | 43.05 (0.00, 100) | 52.11 (0.00, 84.61) | −0.14 1 | 0.32 (0.00, 0.53) | 0.46 (0.18, 0.68) | −2.48 (0.43, 0.49) | 0.993 | |
Left | SVM_gyro | 71.61 (66.67, 72.22) | 0.00 (0.00, 0.00) | 99.15 (92.31, 100) | −0.01 1 | 0.00 (0.00, 0.00) | 0.49 (0.34, 0.66) | −0.82 (0.48, 0.51) | 0.794 |
SVM_accel | 71.06 (61.11, 72.22) | 1.00 (0.00, 20.00) | 98.00 (84.62, 100.00) | −0.01 1 | 0.21 (0.00, 0.33) | 0.51 (0.37, 0.62) | 1.37 (0.49, 0.51) | 0.085 | |
CNN_gyro | 47.15 (27.78, 77.78) | 64.32 (0.00, 100) | 40.54 (0.00, 84.61) | 0.05 (−0.27, 0.45) | 0.54 (0.32, 0.81) | 0.41 (0, 0.70) | 3.06 (0.51, 0.57) | <0.001 | |
CNN_accel | 49.91 (27.78, 72.22) | 38.02 (0.00, 100) | 54.48 (3.84, 84.61) | −0.08 1 | 0.28 (0.00, 0.62) | 0.44 (0.19, 0.70) | −3.95 (0.41, 0.46) | >0.999 |
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Roshdibenam, V.; Jogerst, G.J.; Butler, N.R.; Baek, S. Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics. Sensors 2021, 21, 3481. https://doi.org/10.3390/s21103481
Roshdibenam V, Jogerst GJ, Butler NR, Baek S. Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics. Sensors. 2021; 21(10):3481. https://doi.org/10.3390/s21103481
Chicago/Turabian StyleRoshdibenam, Venous, Gerald J. Jogerst, Nicholas R. Butler, and Stephen Baek. 2021. "Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics" Sensors 21, no. 10: 3481. https://doi.org/10.3390/s21103481
APA StyleRoshdibenam, V., Jogerst, G. J., Butler, N. R., & Baek, S. (2021). Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics. Sensors, 21(10), 3481. https://doi.org/10.3390/s21103481