Search for: All records

Introduction:There is increasing interest in developing mathematical and computational models to forecast adverse events in physiological systems. Examples include falls, the onset of fatal cardiac arrhythmias, and adverse surgical outcomes. However, the dynamics of physiological systems are known to be exceedingly complex and perhaps even chaotic. Since no model can be perfect, it becomes important to understand how forecasting can be improved, especially when training data is limited. An adverse event that can be readily studied in the laboratory is the occurrence of stick falls when humans attempt to balance a stick on their fingertips. Over the last 20 years, this task has been extensively investigated experimentally, and presently detailed mathematical models are available. Methods:Here we use a long short-term memory (LTSM) deep learning network to forecast stick falls. We train this model to forecast stick falls in three ways: 1) using only data generated by the mathematical model (synthetic data), 2) using only stick balancing recordings of stick falls measured using high-speed motion capture measurements (human data), and 3) using transfer learning which combines a model trained using synthetic data plus a small amount of human balancing data. Results:We observe that the LTSM model is much more successful in forecasting a fall using synthetic data than it is in forecasting falls for models trained with limited available human data. However, with transfer learning, i.e., the LTSM model pre-trained with synthetic data and re-trained with a small amount of real human balancing data, the ability to forecast impending falls in human data is vastly improved. Indeed, it becomes possible to correctly forecast 60%–70% of real human stick falls up to 2.35 s in advance. Conclusion:These observations support the use of model-generated data and transfer learning techniques to improve the ability of computational models to forecast adverse physiological events. 

This paper explores the personalization of smartwatch-based fall detection models trained using a combination of deep neural networks with ensemble techniques. Deep neural networks face practical challenges when used for fall detection, which in general tend to have limited training samples and imbalanced datasets. Moreover, many motions generated by a wrist-worn watch can be mistaken for a fall. Obtaining a large amount of real-world labeled fall data is impossible as fall is a rare event. However, it is easy to collect a large number of non-fall data samples from users. In this paper, we aim to mitigate the scarcity of training data in fall detection by first training a generic deep learning ensemble model, optimized for high recall, and then enhancing the precision of the model, by collecting personalized false positive samples from individual users, via feedback from the SmartFall App. We performed real-world experiments with five volunteers and concluded that a personalized fall detection model significantly outperforms generic fall detection models, especially in terms of precision. We further validated the performance of personalization by using a new metric for evaluating the accuracy of the model via normalizing false positive rates with regard to the number of spikes of acceleration over time. 

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.