# Regressive Machine Learning for Real-Time Monitoring of Bed-Based Patients
###### Abstract  
This study introduces an ensemble model designed for real-time monitoring of bedridden patients. The model was developed using a unique dataset, specifically acquired for this study, that captures six typical movements. The dataset was balanced using the Synthetic Minority Oversampling Technique, resulting in a diverse distribution of movement types. Three models were evaluated: a Decision Tree Regressor, a Gradient Boosting Regressor, and a Bagging Regressor. The Decision Tree Regressor achieved an accuracy of 0.892 and an R\({}^{2}\) score of 1.0 on the training dataset, and 0.939 on the test dataset. The Boosting Regressor achieved an accuracy of 0.908 and an R\({}^{2}\) score of 0.99 on the training dataset, and 0.943 on the test dataset. The Bagging Regressor was selected due to its superior performance and trade-offs such as computational cost and scalability. It achieved an accuracy of 0.950, an R\({}^{2}\) score of 0.996 for the training data, and an R\({}^{2}\) score of 0.959 for the test data. This study also employs K-Fold cross-validation and learning curves to validate the robustness of the Bagging Regressor model. The proposed system addresses practical implementation challenges in real-time monitoring, such as data latency and false positives/negatives, and is designed for seamless integration with hospital IT infrastructure. This research demonstrates the potential of machine learning to enhance patient safety in healthcare settings. machine learning; model; regressor; ensemble; patient safety; automated detection +
Footnote †: journal:use of bed alarms, and positioning techniques to reduce fall risk. Employee involvement and scenario-based training, such as workshops, are also key to successful fall prevention measures [3]. Falls are the leading cause of injury among adults aged \(\geq\) 65 years (i.e., older adults) in the United States [4]. Falls are preventable, and healthcare providers can help their older patients reduce their risk for falls. Screening older patients for fall risk, assessing modifiable risk factors (e.g., use of psychoactive medications or poor gait and balance), and recommending interventions to reduce this risk (e.g., medication management or referral to physical therapy) can prevent older adult falls [4]. Falls can lead to serious consequences such as fractures, head injuries, reduced mobility, premature long-term care admissions, and even death, especially considering that 30% of fillers will fall again [5]. Gait and balance disorders are multifactorial and common causes of falls, especially in older adults with other comorbidities and underlying medical conditions [6]. Cognitive impairment is also associated with an increased risk of falls [6], including impaired visuospatial function, psychomotor speed, executive function, and attention [7]. This can be attributed to neurologic diseases such as Parkinson disease or neurocognitive disorders such as dementia [8]. The current literature does not document sufficient evidence for effective fall prevention intervention in this population [9]. Frailty and deconditioning, due to a mix of factors such as unintentional weight loss, muscle weakness, and low physical activity, are associated with falls [8]. Polypharmacy and the use of certain medications, such as opioids, vasodilators, antihistamines, antidepressants, and B-blockers, are associated with increased fall risk [6]. With older populations, medication reconciliation becomes increasingly important as managing multiple comorbidities and medical problems leads to conflicting side effect profiles. These factors are often exacerbated by each other--the effect of psychotropic medications on gait and balance is known, along with wandering behavior and poor situational awareness associated with impaired cognition, impulsivity, and poor balance control [7]. Nonmedical reasons, such as unsafe home environments and poor footwear, can also contribute [4;5]. Adherence to fall prevention recommendations and referrals is a factor that should be explored further [10].