Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy.
Sensors (Basel). 2024 Mar 15;24(6):1900. doi: 10.3390/s24061900.
This work aims to compare the performance of Machine Learning (ML) and Deep Learning (DL) algorithms in detecting users' heartbeats on a smart bed. Targeting non-intrusive, continuous heart monitoring during sleep time, the smart bed is equipped with a 3D solid-state accelerometer. Acceleration signals are processed through an STM 32-bit microcontroller board and transmitted to a PC for recording. A photoplethysmographic sensor is simultaneously checked for ground truth reference. A dataset has been built, by acquiring measures in a real-world set-up: 10 participants were involved, resulting in 120 min of acceleration traces which were utilized to train and evaluate various Artificial Intelligence (AI) algorithms. The experimental analysis utilizes K-fold cross-validation to ensure robust model testing across different subsets of the dataset. Various ML and DL algorithms are compared, each being trained and tested using the collected data. The Random Forest algorithm exhibited the highest accuracy among all compared models. While it requires longer training time compared to some ML models such as Naïve Bayes, Linear Discrimination Analysis, and K-Nearest Neighbour Classification, it keeps substantially faster than Support Vector Machine and Deep Learning models. The Random Forest model demonstrated robust performance metrics, including recall, precision, F1-scores, macro average, weighted average, and overall accuracy well above 90%. The study highlights the better performance of the Random Forest algorithm for the specific use case, achieving superior accuracy and performance metrics in detecting user heartbeats in comparison to other ML and DL models tested. The drawback of longer training times is not too relevant in the long-term monitoring target scenario, so the Random Forest model stands out as a viable solution for real-time ballistocardiographic heartbeat detection, showcasing potential for healthcare and wellness monitoring applications.
本工作旨在比较机器学习 (ML) 和深度学习 (DL) 算法在智能床上检测用户心跳的性能。智能床采用 3D 固态加速度计，以实现非侵入式、连续的睡眠期间的心率监测。加速度信号通过 STM 32 位微控制器板进行处理，并传输到 PC 进行记录。同时检查光电容积脉搏传感器作为基准。通过在真实环境中采集测量数据构建了一个数据集：10 名参与者参与，产生了 120 分钟的加速度轨迹，用于训练和评估各种人工智能 (AI) 算法。实验分析利用 K 折交叉验证确保在数据集的不同子集上进行稳健的模型测试。比较了各种 ML 和 DL 算法，每个算法都使用收集的数据进行训练和测试。随机森林算法在所有比较模型中表现出最高的准确性。虽然与一些 ML 模型（如朴素贝叶斯、线性判别分析和 K-最近邻分类）相比，它需要更长的训练时间，但与支持向量机和深度学习模型相比，它的速度仍然快得多。随机森林模型展示了稳健的性能指标，包括召回率、精度、F1 分数、宏平均、加权平均和整体准确性均高于 90%。该研究突出了随机森林算法在特定用例中的更好性能，在检测用户心跳方面优于其他测试的 ML 和 DL 模型，实现了更高的准确性和性能指标。训练时间较长的缺点在长期监测目标场景中并不太相关，因此随机森林模型作为实时心冲击图心跳检测的可行解决方案脱颖而出，展示了在医疗保健和健康监测应用中的潜力。
