## 3 Experiment with More Detailed Information  
To verify the feasibility and efficiency of our proposed system, we designed fall prediction experiments that were conducted using the MCLLRTR. A total of 54 young, healthy student volunteers (including 36 males and 18 females; 22 +- 4 years old; weight: 64 +- 20 kg) performed simulated falls and abnormal gaits as well as some other training activities (the numbers of falls, abnormal gaits, and normal gait events are presented in Table 1). All volunteers provided informed consent before the simulated experiments, and were paid for their participation. Exclusion criteria included severe hypertension (BP \(>\)180/100 mmHg) and severe cardiopulmonary disease. Each participant was informed about how to operate the rehabilitation robot, and each participant completed the entire experiment. They performed various fall-type actions and rehabilitation tasks using the protective robot. Figure 6 shows the simulation of different activities in the training process, such as (a) normal gait, (b1-b2) abnormal gait, and (c1-c5) falls. Accelerations along the x, y, and z axes are denoted as A\({}_{x}\), A\({}_{y}\), and A\({}_{z}\), respectively, and their norm (the resultant acceleration) is \(A_{svm}=\sqrt{A_{X}^{2}+A_{y}^{2}+A_{z}^{2}}\). The tension signal is stored on the control board using an A/D converter with a sampling interval of \(\Delta t\) = 20 ms. The photodiode signals consist of digital signals, either a \"0\" or \"1\", which reflect the foot landing point distribution. After the experiment, the data must be processed. First, the two-stage activity recognition classifier mentioned above needs to be trained; then, the trained classifier can be used to classify the rehabilitation training activities. During neural network classifier training, the output needs to be labeled. After training, the classification threshold is determined. When setting the threshold, the values of the inertia sensor and tension sensor were transformed by SVM kernel functions (Asvm and Tsvm) and drawn by MATLAB (as shown in Figure 6). When setting the thresholds of the first-level PNN network for training to reduce the number of calculations, we used only accelerometer and tension sensor data for fall prediction to simplify the rules for fall prediction. These rules can be expressed as follows: if \(|\)A\({}_{\text{svm}}\)\(|\) exceeds a threshold b\({}_{1}\) and T\({}_{\text{svm}}\) exceeds a threshold b\({}_{2}\), then, the event is determined to be a 1 (a fall event); otherwise, the event is determined to be a 0 (a nonfall event). The trigger thresholds for a falling event are shown in Figure 6. To achieve high accuracy, the threshold value determined by a single sensor must be 28.6% higher than that determined by multisensor fusion. Moreover, the prediction time by multisensor fusion is approximately 205 milliseconds faster than that of a single sensor. As Figure 6 shows, setting a single threshold value for the acceleration and tension features fails. The multisensor system incorporates multiple thresholds to avoid the effects of single-sensor noise. By integrating multiple single weak classifiers into a robust classifier, the multisensor fusion approach also achieves a high detection rate, while reducing the false alarm rate. From Figure 6, we can see that a single sensor's data indicate only some activity features, and are sensitive in only some aspects. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Type** & **Activity** & **Number of Activity** \\ \hline \multirow{5}{*}{Fall} & (c1) Vertical fall (soft fall) & 5 \\  & (c2) Fall to left & 5 \\  & (c3) Fall to right & 5 \\  & (c4) Fall frontward & 5 \\  & (c5) Fall backward & 5 \\ \hline \multirow{5}{*}{(b) Abnormal gait} & Rond de jembe a terre at left & 5 \\  & Rond de jembe a terre at right & 5 \\ \cline{1-1}  & Dragging gait & 5 \\ \cline{1-1}  & Forward gait & 5 \\ \hline (a) Normal gait & Normal gait & 5 \\ \hline \hline \end{tabular}
\end{table}