自由生活临床环境中的多模态体力活动预测：寻找及时干预的机会
Multimodal Physical Activity Forecasting in Free-Living Clinical Settings: Hunting Opportunities for Just-in-Time Interventions
AI总结：
本研究开发了MoveSense多模态LSTM预测系统，利用可穿戴设备数据提前24小时预测患者步数，在糖尿病和睡眠数据集上表现优于传统基线，为临床及时行为干预提供了有效支持。
摘要：
目的：本研究旨在开发一种名为MoveSense的生活方式干预系统，该系统通过预测患者的活动行为，以便在真实世界的临床环境中实现早期和个性化的干预。方法：我们开展了两项临床研究，涉及58名糖尿病前期退伍军人和60名阻塞性睡眠呼吸暂停患者，使用可穿戴设备收集多模态行为数据。我们开发了多模态长短期记忆（LSTM）网络模型，该模型能够通过分析活动和参与度模态的数据，提前最多24小时预测患者的步数。此外，我们设计了基于目标的预测模型，用于预测一个人次日的步数是否会超过特定阈值。结果：在糖尿病前期数据集上，采用早期融合的多模态LSTM的平均绝对误差分别比线性回归和ARIMA低33%和37%。在睡眠数据集上，LSTM也以13%和32%的优势优于线性回归和ARIMA。在基于目标的预测任务中，多模态预测模型在糖尿病前期数据集和睡眠数据集上分别达到了72%和79%的准确率。结论：我们的实验得出结论，采用早期融合的多模态LSTM模型优于采用晚期融合的多模态LSTM模型和单模态LSTM模型，同时也优于ARIMA和线性回归模型。意义：我们解决了非受控环境中时间序列预测这一重要且具挑战性的任务。有效预测个人的体力活动有助于设计自适应行为干预，以保持用户的参与度并使其遵循规定的日常计划。
英文摘要：
Objective: This research aims to develop a lifestyle intervention system, called MoveSense, that forecasts a patient's activity behavior to allow for early and personalized interventions in real-world clinical environments. Methods: We conducted two clinical studies involving 58 prediabetic veterans and 60 patients with obstructive sleep apnea to gather multimodal behavioral data using wearable devices. We develop multimodal long short-term memory (LSTM) network models, which are capable of forecasting the number of step counts of a patient up to 24 hours in advance by examining data from activity and engagement modalities. Furthermore, we design goal-based forecasting models to predict whether a person's next-day steps will be over a certain threshold. Results: Multimodal LSTM with early fusion achieves 33% and 37% lower mean absolute errors than linear regression and ARIMA respectively on the prediabetes dataset. LSTM also outperforms linear regression and ARIMA with a margin of 13% and 32% on the sleep dataset. Multimodal forecasting models also perform with 72% and 79% accuracy on the prediabetes dataset and sleep dataset respectively on goal-based forecasting. Conclusion: Our experiments conclude that multimodal LSTM models with early fusion are better than multimodal LSTM with late fusion and unimodal LSTM models and also than ARIMA and linear regression models. Significance: We address an important and challenging task of time-series forecasting in uncontrolled environments. Effective forecasting of a person's physical activity can aid in designing adaptive behavioral interventions to keep the user engaged and adherent to a prescribed routine.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
