## 2 Methods and System Setup
### Sensor Locations  
It is challenging to identify diverse falls and abnormal motor patterns from routine activities because there are many similar features between falls and the activities of daily life (ADLs). Thus, it is challenging to achieve a good trade-off between false positives (when the threshold value is too low, the system may generate false alarms, specificity <100%) and false negatives (when the threshold value  
Figure 2: The control system of MCLLRTR. is too high, the system may miss real alarms, sensitivity \(<\)100%) when setting thresholds using a single sensor. To solve this problem, we proposed a multisensor system (as shown in Figure 3) that obtains multiple features for classifying activities from different dimensions. This system board is designed with an STM32 microprocessor powered by a 5-V battery. The photoelectric sensors collect the spatial distribution of the gait data, the tension sensors collect the interactive forces between the patient and the rehabilitation robot, and the accelerometer sensor collects the patient's kinematic features. As shown in Figure 3, the multisensor system includes an accelerometer, a pulse sensor, and two photoelectric sensors, which include four tension sensors and seven photoelectric sensors. The photoelectric sensors include a speed-detecting sensor. The accelerometer monitors the patients' spatial motion from the kinematic dimension. The pulse sensor is used to detect physical load. The tension sensor monitors the motion homogeneity between the robot and patient from the kinetics dimension, and the photoelectric sensors monitor gait from the spatial distribution characteristics. At the same time, to meet the needs of the rehabilitation assessment, we introduce asynchronous deviation and pulse monitoring based on the multisensor system. The tension sensors mainly obtain the synchronous deviation, and the tolerance degree of the patients is mainly obtained by synthesizing the data of the pulse sensor and the idle duration. The activity recognition system also provides data support for dynamic control. The spatial distributions of normal and abnormal gaits (such as the wiggly duck gait) are different. Based on this characteristic, the OMRON (E3 JK-RAMA, Japan) photoelectric sensors are applied to this type of detection. We located the photoelectric sensors below the ankle and above the knee in a vertical direction to detect whether the lower leg is beyond the normal gait range or at a healthy gait boundary in the horizontal direction (as shown in Figures 1 and 3). These sensors detect whether the foot's landing point is beyond the range of a normal gait. The photoelectric sensors include a transmitter, a receiver, and a detection circuit. The transmitter continuously emits light; that is,  
Figure 3: Multisensor fusion system. the light is received by the receiver or blocked by patients. The receiver converts the received optical signal into an electric signal, and the detection circuit is used to detect the electrical signals received by the receiver. Signal impurities are removed, leaving the valid signals; then, the valid electric signals are transmitted to the main control board. The spatial distribution of a normal gait is shown in Figure 4 (the blue area). Six photoelectric sensors were installed on the left and front side of the rehabilitation robot. The second set of photoelectric sensors was installed in the middle position on the left side of the robot, and is used to detect the number of training steps. The first and third sets of photoelectric sensors were installed on the front and back sides of the second set of photoelectric sensors, and are used to determine whether the patient's gait exhibits a normal travel direction. The fourth, fifth, and sixth sets of photoelectric sensors were installed on the front side of the robot, and are used to detect whether the patient's gait is normal in the lateral direction. The second set of photoelectric sensors is triggered when the patient walks and counts the normal gait steps.