## 3 Experiment with More Detailed Information

Table 1: Statistical information on simulated behavior of rehabilitation training performed by each participant. Table 2 compares our proposed method with state-of-the-art fall prediction algorithms. The proposed algorithm uses an accelerometer attached to the chest and waist belt force sensors in conjunction with the rehabilitation robot for data acquisition. The prediction algorithm identifies a fall or other gait abnormalities based on the off-line threshold value set by the PNN neural network. The prediction time when using the multisensor fusion approach is approximately 205 milliseconds ahead of approaches using a single sensor. The sensitivity is 99.37%, and the threshold is lower than that of single-sensor approaches. The curves for the accelerometer data show that each type of activity has significant distinctions. The tension data curves have abrupt changes, and the peak sequence has an implicit law. After a long period of experimental study, we found that the most significant peak value of the tension sensor in one direction indicates that the fall is occurring in the opposite direction. Figure 6: Threshold comparison between multisensor fusion and single-sensor models. Detailed and correct functional assessment is the basis of formulating a therapeutic schedule [4]; therefore, further detailed identification concerning falls and abnormal gaits is required. The second SVM-RBF-KNN classifier is intended to classify specific types of falls or abnormal gaits in a supervised manner. The data extracted by the first-stage classifier are transmitted to the second-stage classifier. Let \(\text{X}_{\text{i}}=[\text{A}_{\text{svm}}\), \(\text{T}_{\text{svm}}\), d (\(\text{T}_{1}\), \(\text{T}_{4}\)), d (\(\text{T}_{2}\), \(\text{T}_{3}\)), d (\(\text{T}_{3}\), \(\text{T}_{1}\)), d (\(\text{T}_{4}\), \(\text{T}_{2}\)), P\({}_{1}\), P\({}_{3}\), P\({}_{4}\), P\({}_{6}\)] denote the input data, and \(\text{C}_{\text{i}}\) denotes the corresponding cluster center. We set seven types for the final output; \"c1\" denotes a normal gait, \"c2\" denotes an abnormal gait, \"c3\" denotes a vertical fall, \"c4\" denotes a fall to the left, \"c5\" denotes a fall to the right, \"c6\" denotes a backward fall, and \"c7\" denotes a frontward fall. First, we adopted a gradient descent algorithm for the obtained data cluster center, which starts with few group data. Then, other samples are treated as falls or abnormal patterns, and RBF-BP is trained on each of them. This step is followed by an iterative procedure that involves merging a similar cluster (reclassifying) and retraining the remaining RBF-BP until no more merging occurs. Finally, the RBF is applied to train the neural network, and some randomly selected samples are used to test the performance of the neural network. This study was conducted with the MATLAB-2016a 64-bit version installed on Windows 7 Ultimate 64-bit with an Intel Core i7 (8 M cache, up to 2.7 GHz) and 8 GB of RAM. The final clustering centers are as follows:  
\[\left[ \begin{array}{c}\text{C}_{1}\\ \text{C}_{2}\\ \text{C}_{3}\\ \text{C}_{4}\\ \text{C}_{5}\\ \text{C}_{6}\\ \text{C}_{7}\end{array} \right]=\left[ \begin{array}{c}948.47\\ 952.35\\ 952.14\\ 952.34\\ 953.27\\ 951.72\\ 953.45\\ 953.45\\ 950.32\\ \end{array} \right]=\left[ \begin{array}{c}948.47\\ 952.35\\ 952.14\\ 954.73\\ 954.73\\ 955.27\\ 956.7\\ 957.2\\ 958.7\\ 959.32\\ \end{array} \right]=\left[ \begin{array}{c}948.47\\ 956.98\\ 952.35\\ 958.11.6\\ 957.14\\ 958.11\\ 959.32\\ \end{array} \right]=\left[ \begin{array}{c}948.47\\ 952.35\\ 951.16\\ 952.14\\ 953.27\\ 951.72\\ \end{array} \right]=\left[ \begin{array}{c}948.47\\ 952.35\\ 952.35\\ 953.27\\ 951.72\\ 953.45\\ 954.73\\ \end{array} \right]=\left[ \begin{array}{c}948.47\\ 952.40\\ 952.40\\ 953.27\\ 954.48\\ 955.02\\ 950.32\\ \end{array} \right]=\left[ \begin{array}{c}948.47\\ 952.35\\ 958.11.6\\ 957.16\\ 958.11\\ 959.32\\ \end{array} \right]=\left[ \begin{array}{c}948.47\\ 952.35\\ 952.14\\ 953.27\\ 951.72\\ \end{array} \right]=\left[