# Regressive Machine Learning for Real-Time Monitoring of Bed-Based Patients
###### Abstract
Today, inpatient and long-term care use multiple observation modalities to prevent falls. These methods include the use of human resources, technological resources, or even both. The most traditional and well-known method is one-to-one observation, wherein hospital staff are assigned to a patient to stay nearby and monitor them at all times [11]. Although a well-used modality, the efficacy of one-to-one observation depends on the abilities of the staff to both keep a constant watch on the patient and to make clinical predictions regarding what actions or movements may lead to falls. Additionally, employing one-to-one as a monitoring method is costly and resource consuming [12]. By assigning a member of the hospital staff to constantly monitor a single patient, there are fewer staff on the floor available to perform other tasks for other patients. Reducing the number of staff that can serve other patients increases the workload on other staff members and decreases the care said staff can deliver to each individual patient. As the cost of one-to-one is that which pays the hospital staff, it is a costly modality. As technology has advanced, inpatient settings have been able to implement cameras in patient monitoring [11]. The accessibility of the camera has offered an efficient and less costly method of patient monitoring [13]. With it, a single member of the hospital staff is no longer required to dedicate their entire day to a single patient. As such, there are more staff available to work on the floors and the work can be distributed more effectively. Additionally, a single--or just a few--staff member(s) can be utilized to monitor multiple patients at once. Although they may need to be trained to utilize the technology as well as possible, staff can integrate their own creative and clinical decision-making skills to optimize monitoring. The largest issue with camera monitoring, however, is the inability to take rapid action, making it overall a more passive modality [14]. As staff are monitoring a patient online, if a patient does experience a fall, the monitoring staff is unable to take action themselves and must instead inform other staff on the respective floor to help the patient. Additional costs for camera monitoring are attributed mostly to the initial investment as well as the maintenance of camera equipment. Hence, the proposed model overcomes the specific limitations of previous fall detection methods, thereby enhancing its applicability in real-world healthcare settings. Unlike video monitoring methods, which pose privacy issues due to constant surveillance, our model employs sensor technology, ensuring patient privacy. The sensor-based approach not only facilitates automated detection, a significant advancement over video methods, but also reduces the need for healthcare professionals to continuously monitor a screen. Most importantly, while other studies have concentrated solely on fall prevention, our model broadens the scope by detecting six different types of movements commonly performed by bed-based patients. This detection capability not only improves patient safety but also contributes to more efficient and effective patient care in healthcare settings.