Two bandpass filters (with frequency ranges 0.5-5 Hz and 10-20 Hz, respectively) were used to extract the low- and high-frequency components of FCG and SCG, respectively. Similarly, in [35], the authors presented a novel piezoelectric sensor capable of simultaneously recording heart vibrations and respiratory signals. A lead-zirconate-titanate (PZT) piezoelectric disk was employed for FCG measurement. The signals from the ECG and electro-resistive respiration band (ERB) were recorded simultaneously on six healthy volunteers and used as a reference. The respiratory component, identified as forcerespirography (FRG), was extracted from the raw signals using a third-order Savitzky-Golay filter with a window length of about 1.5 s, and afterwards subtracted from the raw signal to isolate the FCG component. The same processing was adapted with the signal provided by the ERB. Finally, the FCG signals produced by the two sensors were extremely similar, demonstrating that the innovative PZT sensor can be useful for monitoring both heart and breathing activities. Artefacts induced by body movements represent the main limitation of approaches based on strain sensors for detecting biosignals. The inertial data can be properly combined with those provided by other sensors to remove the motion artefacts [36; 37]. For instance, A-spiro, a wearable sensing technology that estimates respiratory flow and volume, in addition to the respiratory rate, is presented in [38]. It consists of a chest band equipped with a capacitive strain or stretch sensor that measures the chest's expansion and contraction and an inertial sensor. The body and breathing motions were discerned using the Hilbert-Huang Transform (HHT), overcoming the limitations provided by Fourier analysis and comparable techniques. Moreover, the signal was decomposed into a finite and small number of single oscillatory modes, due to the Empirical Mode Decomposition technique. After isolating the breathing cycles, the RR was determined, by counting the number of breaths per minute. A-spiro was tested on 20 people of diverse ethnicities and ages, determining the respiratory rate, tidal flow, and minute ventilation estimates. A-spiro's model demonstrated 93% accuracy across six activities compared to a spirometer. Finally, information from strain and inertial sensors can be jointly processed to improve the detection of breathing parameters or dangerous conditions. In [39], [PERSON] et al. presented a new sensor for detecting breathing patterns related to sudden infant death incidents. The chest band combines a strain sensor realized by conductive fabric and an inertial unit; the collected data are employed to train a multi-layer neural network (12 neurons in the hidden layers). The test results demonstrated that the proposed system reached an 89.9% overall classification rate, 89.3% true positive rate (TPR), 91.5% true negative rate (TNR), 1.7% false positive rate (FNR), and 8.5% false negative rate (FNR). In Table 1, a summary of the strengths and limitations of the devices presented in the previous scientific works is presented. Compared to the presented devices, the proposed chest band is characterized by a small size and absence of moving parts, making it discreet and unobtrusive. In addition, the proposed device performs the conditioning, acquisition, and processing of data directly onboard without the need for an external processing unit. Unlike many devices based on piezoresistive and piezoelectric sensors, the presented device reduces the effects of artefacts induced by body motions by combining the strain sensor data with inertial data provided by an onboard accelerometer. Other functional and performance comparisons are reported in Section 4.2.