## 2 Related Work  
In recent years, ballistocardiography (BCG) has gained considerable attention as a non-invasive technique for monitoring cardiovascular health. Various studies have explored various sensor types and signal processing methods to improve the accuracy and feasibility of BCG-based heart rate and blood pressure estimation.  
[PERSON] et al. [9] introduced a BCG measurement approach using Polymer Optical Fiber (POF) sensors and evaluated different signal extraction methods, including Hilbert Transform, Power Spectrum Density (PSD), and Savitzky-Golay filtering. [PERSON] et al. [10] developed a wearable full-body BCG sensor utilizing a soft bladder array connected to a barometric sensor, demonstrating its potential for continuous blood pressure monitoring via pulse transit time (PTT) estimation.  
Other studies have adopted optical fiber-based solutions. [PERSON] et al. [11] proposed an envelope extraction-based method using an optical fiber sensor for estimating heart rate variability (HRV) and PTT, while [PERSON] et al. [12] leveraged a back-mounted optical fiber sensor to detect early diastolic ventricular vibrations, enabling the estimation of left atrial pressure in patients with heart failure.  
Contact-based pressure sensors have also been explored. [PERSON] [6] investigated how BCG signal quality is affected by the position of a pressure sensor tube under various cushion conditions, finding that placement beneath the neck yielded the most effective measurements. [PERSON] et al. [13] extracted BCG signals using a digital accelerometer and demonstrated its feasibility as an alternative to electrocardiography (ECG).  
Non-contact methods were explored by [PERSON] et al. [14], who used video photoplethysmography (PPG) and microwave sensors to estimate blood pressure by analyzing BCG-derived PTT, achieving a moderate correlation with systolic pressure (r = \(-0.64\)).  
Machine learning and deep learning techniques have recently been integrated into BCG signal processing. For instance, [PERSON] et al. [15] used a Transformer-based network to estimate heart rate from BCG signals, while [PERSON] et al. [16] adopted a Bi-directional Long Short-Term Memory (Bi-LSTM) network for real-time regression-based heart rate estimation.  
In parallel, increasing attention has been given to the use of inertial measurement unit (IMU) sensors for heart rate monitoring. Some studies focus on sensor fusion techniques. [PERSON] et al. [17] and [PERSON] [18] combined PPG with IMU data to eliminate motion artifacts and enhance real-time heart rate estimation. Conversely, [PERSON] et al. [19] demonstrated that BCG signals can be effectively derived using a single accelerometer, enabling accurate heart rate monitoring without ECG. [PERSON] et al. [20] proposed a low-power IMU-based regression model suited for wearable use cases.  
More recent efforts by [PERSON] [21] and [PERSON] [22] showed that IMU-only systems, empowered by deep learning models such as convolutional neural networks (CNNs) and LSTMs, can achieve reliable heart rate estimation without the need for optical sensors. However, these approaches depend heavily on high-quality training data and often require model retraining to generalize across diverse user conditions.  
In summary, while sensor fusion approaches involving PPG and IMU provide high accuracy, they entail increased system complexity and power consumption. In contrast, IMU-only methods offer advantages in terms of hardware simplicity and lower cost, albeit with trade-offs in accuracy and robustness. Against this background, our study proposes a heart rate estimation technique that exclusively utilizes IMU sensors commonly embedded in wearable headsets. This method aims to balance signal fidelity and system simplicity, potentially paving the way for lightweight and scalable BCG-based cardiovascular monitoring in wearable devices.