## 1 Introduction
Moreover, instrumented mattresses embedded with sensors may not be cost-effective or feasible in clinical settings, and wearable devices can present compliance issues, particularly for patients with dementia and agitated behaviors [24]. The Bed-Ex occupancy monitoring system utilizes weight-sensitive sensor mats attached to the bed to detect when a patient leaves. An alert is triggered on the inpatient ward and the central nursing station when a certain loss of weight is detected [25]. However, this type of virtual restraint system functions as a threshold decision system that only detects danger when people exit the bed. Falls can still occur when people engage in risky behaviors on the bed, such as leaning or hanging on the railings, which the weight sensor may still detect. Therefore, a virtual restraint technique capable of continuously tracking the users' state is needed, rather than simply activating an alarm once they exit. The presence of caregivers performing routine services around the bed can introduce distortion to the sensing system, leading to false alarms. Infrared fences and pressure mats need to be turned off before conducting services, which increases the risk of forgetting to restart them and causing misalignment. In terms of wearable sensors, an IMU (Inertial Measurement Unit) is an electronic device designed to be worn on a specific part of the body. It combines accelerometer and gyroscope sensors, and sometimes a magnetometer, to measure angular rate and magnetic fields in the vicinity of the body [26]. The recent progress in MEMS (Micro-Electro-Mechanical Systems) technology has enabled the development of smaller and lighter sensors, allowing for continuous tracking of human motion and device orientation [27]. Electromyography (EMG) has found extensive application in human-machine interaction (HMI) tasks. In recent times, deep learning techniques have been utilized to address various EMG pattern recognition tasks, including movement classification and joint angle prediction [28]. Deep learning has gained significant popularity in EMG-based HMI systems. However, most studies have primarily focused on evaluating offline performance using diverse datasets. It is crucial to give due consideration to online performance in real-world applications, such as prosthetic hand control and exoskeleton robot operation [29]. Similarly, in indoor applications, such as hospitals and hotels, Wi-Fi is considerably more practical than video or wearable technology. [PERSON] et al. developed a Wi-Fi-based human activity recognition method using adaptive antenna elimination, which required minimal computational resources to distinguish falls from other human activities based on machine learning [30]. [PERSON] et al. [31] introduced a model called WiFall that has the capability to detect falls in elderly individuals, along with monitoring certain activities. WiFall utilizes Channel State Information (CSI) for wireless motion detection. The model employs machine learning algorithms to learn patterns in CSI signal amplitudes. Initially, a Support Vector Machine (SVM) is used to extract features, and Random Forest (RF) is applied to enhance the system's performance. The results demonstrate that WiFall achieves a satisfactory level of ability in fall detection. The approach achieves a detection precision of 90% with a false-alarm rate of 15% when using the SVM classifier. When the RF algorithm is employed, the accuracy is further improved and the false-alarm rate is reduced. However, it is important to note that this approach focuses on monitoring a single individual's motion. Overall, the academic community is additionally highly engaged in innovative sensor exploration for human activity recognition and behavior recognition, which involves novel sensors for HAR/HBR, creative designs and usages of traditional sensors, the utilization of non-traditional sensor categories that are applicable to HAR/HBR, etc. [32].