###### Abstract  
This study proposes a novel heart rate estimation method that detects subtle cardiac-induced vibrations propagated through the cardiovascular system based on the ballistocardiography (BCG) principle, using a six-axis heart rate sensing device that integrates a three-axis accelerometer and a three-axis gyroscope. To validate the effectiveness of the proposed method, a comparative analysis was conducted against heart rate measurements obtained from photoplethysmography (PPG) sensors, which are widely used in conventional heart rate monitoring. Experiments were conducted on 20 adult participants, and frequency domain analysis was performed using different time windows of 30 s, 20 s, 8 s, and 4 s. The results showed that the 4 s window provided the highest accuracy in heart rate estimation, demonstrating that the proposed method can effectively capture fine cardiac-induced vibrations. This approach offers a significant advantage by utilizing inertial sensors commonly embedded in wearable devices for heart rate monitoring without the need for additional optical sensors. Compared to optical-based systems, the proposed method is more power-efficient and less affected by environmental factors such as ambient lighting conditions. The findings suggest that heart rate estimation using the six-axis heart rate sensing device presents a reliable, continuous, and non-invasive alternative for cardiovascular monitoring.  
heart rate; BCG; ballistocardiogram; ECG; electrocardiogram 2020 ac  
An IMU typically consists of a three-axis accelerometer and a three-axis gyroscope, capable of detecting fine body movements and vibrations. These micro-movements, induced by cardiac activity, can be interpreted as ballistocardiography (BCG) signals. By analyzing these signals, it becomes possible to estimate various cardiovascular indicators such as heart rate (HR), pulse transit time (PTT), and heart rate variability (HRV) [5,6].  
Recent studies have demonstrated the potential of extracting biosignals from IMU data using advanced signal processing and deep learning techniques. For example, transformer-based and Bi-LSTM-based models have been applied to detect heart rate from BCG signals with promising results [7,8]. However, this research area remains in its early stages, and further investigation is necessary to enhance signal quality and measurement accuracy. Compared to optical PPG-based methods, IMU-based approaches are often more robust to motion artifacts and impose less computational load, making them better suited for mobile and wearable environments.  
This study proposes a novel heart rate estimation method based solely on IMU data from wearable devices, without relying on additional physiological sensors. The proposed technique utilizes three-axis accelerometer and gyroscope data collected from the user's head or wrist to extract features of the BCG signal and continuously estimate heart rate using advanced signal processing techniques. The effectiveness of the proposed approach is validated through comparative experiments with commercially available PPG-based wearable devices.  
The remainder of this paper is organized as follows. Section 2 reviews prior studies on BCG and IMU-based biosignal monitoring. Section 3 presents the proposed heart rate estimation methodology. Section 4 details the experimental setup and performance evaluation. Finally, Section 5 concludes the paper and discusses future research directions.