4.4 Discussion
4.4.2 Fall classification models
In order to assess fall risk, our study covered the tests systematically that were associated with different aspects of balance control, which was measured by inertial sensors in most of the tests. Most previous studies (Seeing Table4.11) only used some tests to build the classification models for assessing the fall risks, such as postural stability in quiet standing (Greene et al.,2010a), timed up and go test and 20-meter walking (Marschollek et al.,2011a,b), a directed-routine movement test consisting of a timed up and go test, a sit-to-stand five times test, and an alternate step test (Liu et al.,2011). As falls could be caused by many different factors, some tests only associated with parts of balance functions might not reflect the real reasons of falls. Systematic factors from different aspects could have more advantages on improving the accuracy of a model. For example, Marschollek et al. (2009) utilized two models to assess fall risks, where model 1 only used clinical assessment data consisting of Stratify score and Barthel index while model 2 added the sensor data from timed up and go test. Their results showed the accuracy of model 1 increased from 83.6% to the model 2 accuracy of 90%.
Our study applied the 10-folder cross validation to assess the accuracy of models for the model selection.
In previous studies, many models were proposed to assess fall risk by using inertial sensors. A review study (Howcroft et al., 2013) reported half of geriatric fall risk research paper used derived models instead of correlating a variable with fall risk. However, 50% of these did not employ cross validation to evaluate the performance of their models. Thereby it limited the model’s applicability beyond the train population. Due to the differences of evaluation methods, the model accuracy was also different.
So we intended to compare the accuracy of models using the similar cross validation methods. Liu et al. (2011) and Caby et al. (2011) used leave-one-out cross-validation and Marschollek et al. (2011a;
2011b) and Greene et al. (2012) used ten-folder cross-validation for evaluating the fall classification model performance.
In the previous studies (Seeing Table4.11), the frequently used classification models were logistic re- gression (Marschollek et al.,2011b), decision tree (Marschollek et al.,2011a), support vector machine (Greene et al.,2010a), neural network (Caby et al.,2011), naive Bayesian classfiers (Caby et al.,2011), etc. In this study, the typical classification models with different flexibilities were conducted. Among the models, linear models had relatively low accuracy but advance tree models and support vector machine (SVM) showed excellent accuracy on classifying fallers and non-fallers. In terms of the flexibility of models, linear models had more restrictive assumption, basic tree model had relatively lower flexibility, and advance tree models and SVM had THE highest flexibility. High flexibility of a model is associated with high accuracy of the model (James et al.,2014). The accuracy of models ranged from 0.68 to 0.88, where linear models had lower accuracy and advance model showed high accuracy. Compared with previous study, models with lower flexibility showed the similar accuracy with previous studies. Our linear models including logistic regression and linear discrimination also showed the similar accuracy with the studies of Marschollek et al (2011a) and Liu et al.(2011) at the accuracy around 0.70. The basic decision tree with accuracy of 0.79 also present similar accuracy with the decision tree with accuracy of 0.78-0.80 in the study of Marschollek et al.(2011). The possible reason was that linear models and basic decision tree have restrictive assumptions, so more measures were added to the models but it could not help to improve the model accuracy. On the contrary, advance models with high flexibility showed relatively higher accuracy compared with models in previous studies. Greene et al. (2012) used SVM model to classify fallers and non-fallers based on timed up and go test and the accuracy of the model was evaluated using ten-fold cross validation and showed the accuracy of 71.5%. Our study showed
excellent accuracy of 88.5%. This result might be explained by the fact that we included significant measures from different aspects of a balance system, while their studies only used the measures from timed up and go test. Due to the high flexibility of these advance models, more measures from different aspects of a balance system could improve the model accuracy efficiently.
TABLE4.11:Inertialsensorbasedfallriskassessmentmodelsinpreviousstudies. AuthorSamplesizeTask&featuresModelReferences forthefallModel validation
Accurac (%) Cabyetal. (2011)
20(14femaleand 6male):age80.85 ±5.18 25mwalk:time,steps,step frequency,spectralentropy, medianfrequency,etc.
Radialbasisfunction neuralnetwork,support vector,k-nearest neighbor,andnaive Bayesianclassifiers
Clinical assessmentLeave-one-out cross-validation75–100 Marschollek etal. (2009)
110(81femaleand 29male)in patients:age80.
1.Timedupandgotest:pelvic sway,steplength,numberof steps;2.STRATIFYscore;3. Barthelindex.
Decisiontree(CART)Retrospective fallhistory
Stratified ten-foldcross validation83–90 Liuetal. (2011)
68(47femaleand 21male):age 72–91 1.Timedupandgotest:total time,stepfrequency,etc.;2. sit-to-standfivetimestest:time, dissimilarityofsit-to-stand cycle,etc.;3.alternatesteptest: time,dissimilarity,etc.
Linearregression,linear discriminantclassifierClinical assessmentLeave-one-out cross-validation71 Greeneet al.(2012)
120(63femaleand 57male):age73.7 ±5.8 Quietstanding:RMS accelerations,angularvelocity; medianfrequency,etc.SupportvectormachineRetrospective fallhistory
Stratified ten-foldcross validation71.5 Marschollek etal. (2011b)
50(37femaleand 13male)in patients:age81.3 1.Timedupandgotest:kinetic energy,pelvicsway,steplength, etc.2.activitiesofdailylife Logisticregression, decisiontree(CART)Prospective falls
Stratified ten-foldcross validation65–80 Marschollek etal. (2011a)
50(37femaleand 13male)in patients:age81.3 1.Timedupandgotest:kinetic energy,pelvicsway,step duration,steplength,etc.2. 20mwalk;3.STRATIFYscore; 4.Barthelindex
LogisticregressionProspective falls Stratified ten-foldcross validation70–72