Atrial fibrillation (AF) is the type of arrhythmia that raises possibility of severe health problems such as heart failure and stroke and it is known that a major risk factor of AF includes overweight and obesity. Based on this association between such health-related indicators, we propose a smart scale that is capable of measuring weight and electrocardiography (ECG) simultaneously. The scale was developed using Arduino Uno, a Wheatstone bridge load cell, and ECG sensors. The ECG signals were processed to compute heart rate (in other words, RR interval). The smart scale was evaluated with four healthy volunteers in terms of reliability showing high agreement with a commercial device for ECG monitoring. In addition, it implements Atrial Fibrillation (AF) detection using machine-learning classifiers including a k-Nearest Neighbor (kNN) method, a Decision Tree (DT), and a Neural Network (NN) on relatively short recordings of ECG obtained while using the scale. The root mean square of the successive differences between heart beats (RMSSD) and the Shannon entropy of the RR interval (ECG features) were extracted from ECG signals for AF detection. Performance of AF detection was tested with patients who were treated at a Cardiology Center after balancing data by applying over- and under-sampling techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and the Tomek Link (T-Link) algorithm. After addressing the data imbalance, the AF detection performance of each classifier (kNN, DT, and NNs) was 98.9%, 97.8%, and 98.9% respectively. This work has successfully demonstrated weight and cardio activity monitoring features while using a scale that may help keep the records of sensitive health related indexes on a daily basis.

心房颤动（AF）是一种心律不齐的类型，增加了严重的健康问题（如心力衰竭和中风）的可能性，众所周知，AF的主要危险因素包括超重和肥胖。基于这些与健康相关的指标之间的这种关联，我们提出了一种能够同时测量体重和心电图（ECG）的智能秤。该秤使用Arduino Uno，惠斯通电桥称重传感器和ECG传感器开发。处理ECG信号以计算心率（换句话说，RR间隔）。智能量表由四名健康志愿者进行了评估，显示出其可靠性与与用于ECG监测的商用设备高度一致。此外，它使用机器学习分类器（包括k最近邻（kNN）方法）实现心房颤动（AF）检测，决策树（DT）和神经网络（NN），在使用量表时获得相对较短的ECG记录。从心电图信号中提取出心跳（RMSSD）和RR间隔的香农熵（ECG特征）之间的连续差的均方根，以进行AF检测。通过应用过采样和欠采样技术（例如，综合少数族裔过采样技术（SMOTE）和Tomek Link（T-Link）算法）来平衡数据后，对在心脏病学中心接受治疗的患者进行了AF检测的性能测试。解决数据不平衡问题后，每个分类器（kNN，DT和NN）的AF检测性能分别为98.9％，97.8％和98.9％。