## 4 Discussion  
In rehabilitation training, activity recognition is essential for the implementation and control of a rehabilitation robot and in the functional assessments of patients. However, there is currently no standard and detailed classification identification method for rehabilitation activity recognition. In addition, to prevent injuries due to falls, timely and accurate fall prediction is of the utmost importance. Unfortunately, very few studies have been conducted in this area [42; 43]. Achieving timely prediction and making good trade-offs between sensitivity and specificity are also crucial for activity recognition, and thus are worth studying. To improve the accuracy of activity recognition and fall prediction during rehabilitation, we proposed a multisensor fusion system by combining heterogeneous sensor data for reasoning and analysis to solve complex activity recognition problems. We employed an asynchronous deviation obtained via tension sensors and individual conversion to estimate the motor capabilities of patients and adopted the deviation as a modulating factor to compute a corresponding assistance torque with ultimate adjustable torque bounds. Then, an assistance strategy can be generated. We also introduced a two-stage fall detection method in which a probabilistic neural network (PNN) is trained on routine activities. Subsequently, it identifies potential falls and passes them to an SVM-RBF-KNN algorithm, which predicts specific fall types or abnormal gaits in a supervised manner. The first stage allows fall prediction to occur as early as possible, while the second stage determines the fall details or the type of abnormal gait. These detailed classifications are stored and later provided to a therapist for functional assessment and the subsequent formulation of the therapeutic schedule. During rehabilitation training, falls are most commonly caused by intrinsic factors such as muscle weakness, impaired balance, and declining cognition. These intrinsic falls usually do not show dramatic changes in acceleration values, and are therefore difficult to identify with general kinematic sensors. To solve this problem, we implemented a unique design in the multisensor system, which utilizes force dimensional information to avoid missed alarms caused by the lack of dramatic changes in kinematics values. This dimensional force information comes from the bodyweight support sling, which can reflect \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **[PERSON] [39]** & **[PERSON] [40]** & **[PERSON] [41]** & **Our Work** \\ \hline Sensor type & Tri-accelerometer & Tri-accelerometer and Gyro & Tri-accelerometer and Gyro & kinematic, tension, and photoelectric \\ Kinematic sensor location & Waist & Chest and thigh & Neck & Chest and robot \\ Fall type & \(\times\) & \(\circ\) & \(\times\) & \(\circ\) \\ Recognition expect fall & \(\times\) & \(\times\) & \(\times\) & \(\circ\) \\ Learning & KNN & Decision tree & Threshold-based & SVM-RBF-KNN \\ Specificity & NA & NA & 100\% & 100\% \\ Sensitivity & 89.4\% & 99.45\% & 80\% & 99.37\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison between the proposed algorithm and state-of-the-art fall detection systems. KNN: k-nearest neighbors, RBF: radial basis function, SVM: support vector machine. the potential falls caused by intrinsic factors in a timely manner. Previous studies [19; 21; 22; 26; 27] have also employed multicriteria algorithms to detect falls, but these algorithms perform fall identification in a parallel manner, and involve only explicit kinematics and posture information. In contrast, our multisensor system processes not only explicit information such as kinematics and spatial distribution information, but also implicit information such as kinetics and pulse information. Both the explicit and implicit information are analyzed in one discriminant function to obtain a detailed and accurate recognition result.