factors in our study with previous studies, factors in our study were associated with special functions of balance system (E.g. visual system), but factors in previous studies were associated with composite per- formance such as disability, ADL limitations. If the causes of high fall risk were composite performance, the exact reasons of high fall risks would still be unknown. Moreover, fall risk factors used in our study were also modifiable. However, in previous studies, many factors used were unmodifiable such as age (Yamashita et al.,2012), education (Stel et al.,2003b). If unmodifiable factors were the causes of high fall risk, it would be useless on fall prevention even if they were identified as the causes. Considering the fall related factors used in our study, our CART model was more advanced than models in previous studies on identifying the causes of high risks of falling for preventing falls.
In the profile assessment method, fall related factors covered physiological, psychological, and inte- grated functions. Physiological function (Winter, 1995) was related to the sensory system including somatosensory, visual, and vestibular systems, central nervous system of cognitive function, and motor function. Psychological function contained the fear of falling. Integrated function (Horak et al.,2009) was associated with biomechanical constraints, sensory orientation, stability limits, anticipatory postural response, and stability in gait. However, the study that was done by Lord et al. (2003b) only used the physiological factors which contained vision, vestibular function, peripheral sensation, reaction time and muscle force. They also acknowledged the omission of other important factors in their approach as one of the limitations of their study. Compared with previous study, our profile assessment method could identify more possible causes of high risks of falling. Therefore, our combined method that combines the CART model and the profile assessment method could have a higher probability on identifying the factors resulting in high risks of falling than previous studies. Also since these factors were modifiable, the results could be useful on fall prevention.
In the decision tree, for the classification and regression tree model, the splitting variables and the cutoff values in the tree were determined by the Gini impurity. However, the Gini impurity was sensitive to changing variables and sample size (Last et al.,2002). The decision tree could be affected significantly due to the changes of training sample population. Additionally, we found that short FES-I score that measures the fear of falling, information processing speed of central nervous system, sit-to-stand five times jerk, and sensory system including visual and vestibular systems, functional reach angular velocity were most important measures on classifying fallers and non-fallers among all significant measures in the tests (around 40 measures). Falls efficacy scale measures how much do old people concerns of falls
while performing special activities (Tinetti et al., 1990). It is the integrated self-evaluations of their abilities that were associated with falls systematically. Due to the fear of falling, people will also limit their physiological activity (Legters, 2002), which enhances to increase the fall risk. central nervous system also played a core role on balance control, for receives the stimuli from a sensory system, makes the decision and sends the control stimuli to motor control system for achieving balance or avoiding falls (Horak,2006). Visual and vestibular systems were also found to be important factors of falls (Ambrose et al.,2013). Our findings also showed that sit-to-stand five times test and functional reach may be the efficient tests for classifying fallers and non-fallers.
Some limitations still existed in this study. First, the decision tree separated population into subgroups recursively by the independent variables. Some terminal tree node could have small sample sizes. The fall risk of each end tree node was based on the percentage of fallers. If the sample size is too small in the tree end node, the fall probability is not stable and affected by few data significantly. We are still conducting additional experiment to increase the sample size for generating stable results. The other was that we used two methods to identify the underlying causes of high fall risks. We intend to combine results from two methods to analyze the real causes. However, in the current stage, we didn’t propose the methods to validate our results. One standard reference could be the evaluation results from the doctors in the hospital.
5.5 Conclusion
In this study, we proposed CART-PA method to identify the underlying causes of high fall risks. Since many factors from different aspects were considered, the possible causes of high fall risks could be identified effectively. Additionally, fall evaluation results could be helpful for fall prevention, because these factors were measurable and modifiable. Additionally, CART-PA method was integrated by the CART model and profile assessment method. The CART model built a tree based relationship between fall related factors and fall and interactions among factors, which was effective on identifying the causes of high fall risks. But the tree structure was unstable could be affected by small variance. On the other hand, the profile assessment was developed based on the normal distribution of fall related factors. So it was useful on identifying abnormal factors but may ignore sensitive risk factors. Therefore, CART-PA method could generate reinforce results of the factors of high fall risks.
Development of an inertial sensor based fall risk assessment system
6.1 Objective
The purpose of this study was to develop an inertial sensor based prototype system for fall risk assess- ment. The system was designed to realize our proposed fall risk assessment methods, which included the hardware and software. The hardware consisted of five low-cost individual wireless inertial sensors and a wireless transmission device; it was developed to collect the data simultaneously. Computer-based software was developed to process the raw data obtained from the sensors, calculate the measures for each test in the protocol, and build fall risk assessment models.
6.2 Overview of fall risk assessment system
Figure6.1presents the overview of the developed fall risk assessment system, which mainly includes the hardware for data acquisition and the software for fall classification and evaluation. The data acquisition hardware contains five wireless inertial sensors and one Bluetooth USB adapter for wireless transmission device. These five sensors were attached to the pelvis and the upper and lower parts of both legs. Data from the sensors were then transferred to a personal computer (PC) through the wireless transmission
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device. A multi-sensor management software (Shimmer Inc.) in a PC or laptop was used for sensor communication and data collections. Then we developed graphical user interface (GUI)-based software to process the raw data from sensors, calculate measures based on raw data in different tests, and build fall risk assessment models for fall classification and evaluation.