## 1 Introduction
In terms of detection algorithms, the methods used in fall and abnormal behavior detection are mainly threshold-based and rely on machine learning (pattern matching) classifiers, such as the support vector machine (SVM) [20; 21; 22], k-nearest neighbor (KNN) [14], backpropagation (BP) and single-class minimax probability machine (SCMPM) [23]. These methods usually require a threshold value to be set manually or through a semisupervised process to distinguish abnormal from standard samples. Threshold-based systems are simple to implement and computationally inexpensive, but they do not provide a good trade-off between false positives (if the threshold value is too low, the system could generate false alarms, specificity \(<\)100%) and false negatives (if the threshold value is too high, the system could miss real alarms, sensitivity \(<\)100%) because soft falls due to paralysis or fatigue of the gluteus medius are characterized by kinematic peaks similar to those of normal gaits, and small perturbations or \"wiggly duck gait\" are characterized by kinematic peaks similar to those of falls. In other words, an extrinsic fall [9; 24] can be identified by a high threshold because extrinsic falls caused by trips and slips are often accompanied by sharp changes in acceleration. In contrast, intrinsic falls caused by dizziness, impaired balance, or gait [9; 24] usually do not exhibit a sharp change in acceleration, and can easily go undetected. In addition, these methods cannot perform detailed classifications of falls or abnormal gaits. In addition, existing abnormal gait and fall detection research faces other problems, such as missed alarms and false alarms [25; 26; 27] and inadequate understanding of falls and abnormal gait [28; 29]. Currently, no standardized methodology in terms of sensor types, feature engineering [24], or standard classification samples exists for identifying these problems. Although many studies on activity recognition and fall detection exist, these studies tend to use a single sensor (usually a kinematic sensor) for pattern recognition, largely for cost reasons. However, the potential problem of this approach is that it is difficult for a single dimension input to fully reflect the gait characteristics; thus, there is some risk when using a single type of sensor for pattern recognition. In addition, the sensors are easily disturbed by noise or unexpected situations, which inevitably results in uncertainty. Under interference from noise, it is difficult to achieve accurate determination and identification. Kinematic features such as falling speed, stride time, acceleration coordinates, posture information, inactivity periods, and angular velocity vary by individual, especially for different age groups. Moreover, most of these studies are intended to raise alarms only after a fall occurs. Some studies [19] determined that a fall occurs when the acceleration threshold is triggered, and the wearer subsequently remains in a quiet position for a short duration. These studies have been applied to improve the safety of elderly people, but they are not applicable during rehabilitation training, because when a patient is at risk of falling, the robot cannot make a timely judgment; however, if the robot continues to operate, it is likely to cause serious harm to the patient. Therefore, from the perspective of fall injury prevention, timeliness is as crucial as accuracy [30]. During the identification process, a faster yet accurate judgment allows better subsequent measures to be implemented. However, kinematic-sensor-based approaches often require high thresholds to achieve high accuracy, and high thresholds correspond to later predictions. To make accurate judgments in the minimum time, it is worth studying sensor selection and other features to compensate for the defects of kinematic sensors for recognizing complex activities.