Metrological Characterisation of Movella DOT Wearable Sensors for Measuring Induced and Transmitted Vibrations

2. Materials and Methods

The dynamic behaviour of off-the-shelf micro-electro-mechanical system (MEMS) transducers is characterised using a structured, three-phase experimental and analytical framework conducted within a controlled laboratory environment. The methodology first addresses sensor characterisation and the investigation of signal consistency, in which a metrological assessment of three triaxial Movella DOT accelerometers is performed under varying vibration levels and frequency conditions to evaluate repeatability, reproducibility, linearity, directional variability, and acceleration data stability in the time domain. Then, the analysis is expanded to a cross-batch sensor for results generalisation and for dynamic behaviour modelling and optimisation, aiming to validate the obtained results. In this section, the experimental setup and protocols are described, followed by comprehensive data analyses in both the time and frequency domains, specifically utilising the Fast Fourier Transform (FFT) and Frequency Response Function (FRF) modulus computations. Furthermore, these steps feed into the numerical evaluation of the filter performance and the dynamic behaviour of the tested accelerometers.

2.1. Movella DOT Description

The Movella DOT is a compact, lightweight, wireless MEMS inertial measurement unit (IMU) designed for high-accuracy motion tracking and orientation estimation, characterised by a rectangular-shaped case of 36.3 mm × 30.35 mm × 10.8 mm and a mass of 11.2 g [31,32]. The internal architecture integrates a 3-axis accelerometer, a 3-axis gyroscope, and a 3-axis magnetometer (9 axes of data fusion). Kinematic data analysis during sporting activities has demonstrated significant potential, validating the deployment of these wearable devices for robust kinematic evaluations, particularly in real-world outdoor applications [22,23]. Furthermore, focusing on the accelerometer specification of interest for the presented metrological assessment, the Movella DOT features an integrated triaxial MEMS accelerometer characterised by a Full-Scale (FS) range of 160 m∙s^-2.

Specifically, the Movella DOT acquires at a maximum available sampling rate of 120 Hz, in single mode, even if the stated maximum sampling frequency for the raw data, acquired before orientation assessment processing based on the SDI (strap-down integration) method, is higher (up to 800 Hz), as stated in [31,32]. Anyway, the device provides a maximum acceleration sampling rate of 120 Hz for internal logging, which drops to 60 Hz during real-time wireless streaming.

2.2. Calibration Setup and Characterisation Procedure

The calibration process of the three-axis accelerometers of the Movella DOT devices was carried out, aiming to verify the sensor characteristics within the range of interest, limited in the intended applications up to 55 Hz, as shown by the literature [33] and thus to cover the bandwidth of the device, acquired at 120 Hz in internal logging mode.

The experimental setup is illustrated in Figure 1. The Movella DOT to be assessed is mounted on a mechanical interface on an electrodynamic shaker (LDS V830, by Brüel & Kjær, Nærum, Denmark), as the controlled vibration source (acting along vertical direction Z), a piezoelectric reference accelerometer (PCB 333B30, with a nominal sensitivity of 10.2 mV/m/s², measurement range equal to ±490 m/s² pk, by PCB Piezoelectronics, NY, USA) for the controller, and a laser vibrometer (model OFV-5000 Xtra Laser Vibrometer, OFV-505 Sensor Head, by Polytec GmbH, Germany) for acceleration acquisition and data comparison. The local reference system of the Movella DOT is reported. In the illustrated scenario, a Movella DOT (labelled as A1) was tested along the x-axis, aligned with the vertical direction Z. The sampling frequency of the laser vibrometer was fixed at 2048 Hz in order to verify any deviation from the nominal excitation frequency, while the Movella DOT was set at its maximum, i.e., 120 Hz. Throughout all test sessions, the excitation axis in the global reference system was kept fixed in the vertical direction. The Movella DOT sensors were mounted to receive mono-axial excitation (i.e., with their local axis parallel to the excitation axis), allowing each acceleration axis to be assessed individually, as shown in Figure 1.

The experimental campaign was structured into seven distinct test sessions to systematically characterise the performance of four wearable sensors, as summarised in Table 1. The first batch comprises three identical units (A1, A2, and A3), while a fourth sensor (B1) was selected from a second batch to assess batch-to-batch reproducibility.

For test sessions #1 through #4, the excitation profile consisted of a stepped-sine wave at a fixed acceleration level (limited between 1g and 15g) and a discrete frequency. The procedure aimed to target the nominal sensor bandwidth, within the device’s bandwidth, applying excitation at specific frequencies, i.e. 5, 10, 18, 25, 30, 35, 38, 40, 42, and 45 Hz. In detail, the repeatability and reproducibility were assessed by test session #1, which investigated the performance of the three units of the primary batch (labelled respectively as A1, A2, and A3) under a nominal mono-axial excitation of 1g (g is meant to be gravitational acceleration), for at least 1 minute of excitation input for each trial. Here, intra-sensor repeatability was assessed by comparing the sensors to three identical, consecutive test trials under constant excitation conditions, as described in detail in Table 1. For each trial, continuous time-history data were recorded from which three 1-minute time windows were extracted for subsequent statistical analysis in the frequency domain.

This test session was followed by the linearity and level variability evaluation (session #2), where linearity was evaluated with mono-axial harmonic excitation on sensor A1 across multiple levels (1g, 2g, 3g, and 15g) and on sensors A2 and A3 up to 3g maximum sine amplitude. Moreover, the session also allowed the quantification of the inter-sensor and intra-batch reproducibility by comparing three separate sensors from the same production lot under an identical excitation level.

To assess the dynamic behaviour of the sensors across the tri-axes, the directional variability session (#3) was dedicated to additional testing of sensor A1 along its local y and z-directions under a 1g stepped-sine profile.

The reproducibility between batches was evaluated in test session #4 by comparing these baseline results against the secondary batch unit (i.e., sensor B1), under identical 1g mono-axial conditions (x-direction). Long-term signal behaviour was instead evaluated during the stability check session (test session #5), which subjected sensor A1 to continuous operation at 1g for 10 min. In order to verify the presence of aliasing error, the three sensors A1, A2 and A3 were tested at frequencies (i.e. 70, 90, 110, and 130 Hz) higher than the Nyquist frequency. Concluding the procedure, test session #7 introduced a 10-minute random excitation profile spanning a continuous 8 to 40 Hz bandwidth at an overall energy level of 1g of RMS (Root Mean Square) input acceleration value. This latter session allowed for validation of the sensors’ behaviour with a limited-band dynamic signal.

2.3. Data Analysis Processing

2.3.1. FRF Moduli Evaluation

In order to characterise the FRF moduli in the analysed condition, the following steps were applied to the acquired time histories. As previously described, two independent acquisition systems were used for the vibrometer and the Movella DOT accelerometers, thus requiring the resampling of the accelerometer signal at the same sampling frequency (i.e., 120 Hz) as the Movella DOT sensors.

After resampling the laser vibrometer data, the frequency analyses of the Movella DOT accelerometer and the reference laser vibrometer data were evaluated by extracting the harmonic content from the acquired time series. Then, a 10 s time window was extracted for a single FFT evaluation.

Furthermore, the transfer function of the sensor was characterised by computing the experimental Frequency Response Function (FRF, labelled as H_e) modulus, as shown in Equation 1, which was calculated as the direct ratio of the peak spectral amplitudes of the Movella DOT sensor (A_M) to the frequency-scaled acceleration amplitude spectrum of the vibrometer reference (labelled as A_v). Because the laser vibrometer measures point velocity rather than acceleration, the frequency-domain differentiation was applied, which corresponds to a multiplication by jω of the velocity amplitude spectrum V_v, where j is the imaginary unit, and ω is the angular frequency of the input:

$$\left|H_e\left(f\right)\right|={\displaystyle \frac{\max A_M\left(f\right)}{\max A_v\left(f\right)}}={\displaystyle \frac{\max A_M\left(f\right)}{\mathsf{\omega *max}{V}_v\left(f\right)}}$$

(1)

The moduli ratio was evaluated across all tested discrete frequencies to reconstruct the experimental modulus of the FRF, also for the aliased and stability conditions (test sessions #5 and #6).

Robustness of the obtained results was verified by computing the mean and standard deviation of FRF modulus for each excitation frequency, considering three time histories of 15s to be used for computing the FRF. Indeed, the metrics served to assess also the intra-sensor repeatability (A1), inter-sensor repeatability (A1, A2, and A3), consistency across various acceleration levels and directions, and reproducibility across distinct sensor batches (A1 vs B1 comparison).

Then, to assess measurement stability, ten windows lasting 10s each were extracted at 1-minute intervals across the stability testing acquisition, lasting 10 min. The FRF amplitude was computed, and the system stability was quantified by evaluating the FRF's variability.

2.3.2. FRF Optimisation

Using the first production batch dataset (i.e. Sensors A1, A2, and A3), a numerical optimisation was executed within the 5–55 Hz bandwidth to establish the sensor dynamic model. To account for high-frequency distortions, out-of-band aliased conditions (from 70 Hz to 130 Hz) were integrated into a secondary step of the evaluation. Because the experimental FRF data contained only amplitude values, the optimisation minimised the difference between the measured and numerical amplitudes to identify equivalent transfer-function models for the Movella DOT sensor. This approach would allow for recovering the phase, not measurable with the implemented approach, given the large uncertainty of the synchronisation between the vibrometer and the Movella DOT signals due to the limitation of the sampling rate of the Movella DOT. The optimisation problem is formulated by minimising the squared difference between the experimental and numerical transfer functions, as shown in Equation 2:

$$J\left(\theta \right)={\displaystyle \sum _{i=1}^N}{(\left|H_e\left({\omega }_i\right)\right|-\left|H_m\left({\omega }_i\right)\right|)}^2$$

(2)

where θ represents the model parameters, $\left|H_e\right|$ is the measured FRF amplitude, and $\left|H_m\right|$ is the amplitude response of the fitted transfer function, and ω_i is the i-th considered angular frequency.

The optimisation problem was implemented in MATLAB using the lsqcurvefit function as a numerical solver, characterised by the trust-region reflective algorithm. Tight convergence tolerances and high iteration limits were used to improve convergence and reduce numerical fitting errors. In particular, the maximum number of iterations and function evaluations were both set to 20000. The step tolerance was set to 10^-14, while the function tolerance was set to 10^-10. For the time-constant models, each time constant was constrained between 10^-6 s and 10 s to prevent nonphysical or numerically ill-conditioned parameter estimates.

Two modelling strategies were considered. First, physically interpretable time-constant cascade models were developed using the dataset within the Nyquist frequency. These stable, low-pass filtering structures—ranging from first- to third-order —are defined in Equations 3, 4, and 5, respectively, where the time constants (${\tau }_i,$i=1,..,3) were the optimisation variables.

$$H_{m,1}\left(j\omega \right)={\displaystyle \frac{1}{({\tau }_{1}j\omega +1)}}$$

(3)

$$H_{m,2}\left(j\omega \right)={\displaystyle \frac{1}{({\tau }_{1}j\omega +1)({\tau }_{2}j\omega +1)}}$$

(4)

$$H_{m,3}\left(j\omega \right)={\displaystyle \frac{1}{({\tau }_{1}j\omega +1)({\tau }_{2}j\omega +1)({\tau }_{3}j\omega +1)}}$$

(5)

Furthermore, a higher-order general transfer function was modelled using the full dataset, including the results of the aliasing testing phase.

The numerical model of interest is shown in Equation 6, where the angular frequency variable is scaled to improve numerical stability (Equation 7): ${\omega }_{max}$ is the maximum angular frequency in the fitted dataset, whereas q is the normalised angular frequency. The model utilised a sixth-order numerator and a seventh-order denominator.

$$H_{m,4}\left(jq\right)={\displaystyle \frac{b_6{\left(jq\right)}^6+b_5{\left(jq\right)}^5+b_4{\left(jq\right)}^4{+b}_3{\left(jq\right)}^3{+b}_2{\left(jq\right)}^2+b_1\left(jq\right)+b_0}{{{a_7\left(jq\right)}^7+a}_6{\left(jq\right)}^6+a_5{\left(jq\right)}^5+a_4{\left(jq\right)}^4{+a}_3{\left(jq\right)}^3{+a}_2{\left(jq\right)}^2+a_1\left(jq\right)+a_0}}$$

(6)

$$q={\displaystyle \frac{\omega }{{\omega }_{max}}}$$

(7)

The presented optimisation settings (i.e., maximum number of iterations, function evaluations, step tolerance, and function tolerance) were applied consistently to the first-, second-, and third-order time-constant models and to the general transfer-function model; then, the coefficients of the general transfer-function model were estimated without explicit lower or upper bounds.

Finally, to evaluate the accuracy of the numerical models, the residual Root Mean Square Error (RMSE) was computed based on the differences between the numerical predictions and the experimental dataset, within the instrument’s bandwidth.

2.3.3. PSD Measurement and FRF Model Validation

Random vibration data were processed using the previously described procedure, which first converts time-domain signals into the frequency domain via Fast Fourier Transform (FFT). Then, the Power Spectral Density (PSD) was determined by taking the ensemble average of these 100 power spectra and dividing by the frequency bandwidth. Furthermore, to evaluate the performance of the data acquisition system, the numerical models derived from the calibration phase were applied directly to the Movella DOT’s PSD, according to the linear system relation [34]:

$${PSD}_c={PSD}_e/{\left|H_{m,i}\right|}^2,$$

(8)

where H_m,i is the i-th modelled FRF, as for paragraph 2.3.2, and PSD_e is the measured PSD by the Movella DOT sensor.

The resulting corrected PSDs (PSD_c) were then compared to the experimental PSD_v extracted from the vibrometer. Finally, the discrepancy between the vibrometer and corrected data from the Movella DOT was quantified by calculating the mean PSD values and their standard deviations across the reference, uncorrected, and corrected signals.

Hence, the objective of this analysis is to compensate for the limited measurement bandwidth of the Movella DOT sensor by using a reference vibrometer to validate a dynamic correction model, thereby restoring the underestimated PSD to its correct values.

3. Results

3.1. FRFs Repeatability, Reproducibility and Linearity

In this section, the measured FRFs are shown, presenting the repeatability, reproducibility, variability in the measurement directions and non-linearity analyses.

Figure 2 shows the measured acceleration by sensor A1, considering three subsequent tests: in particular, the graph shows the accelerometer response at 1g excitation level and 10 Hz frequency, along the x-direction. Figure 3 shows an example of the 10s-time windows for the spectrum computation for the accelerometer A1, whereas Figure 4 summarises the spectra of the accelerometer and of the vibrometer for the selected time window.

Table 2 for sensor A1, tested along the x-direction. Also, the mean values and the standard deviation (1σ), expressed as a percentage, are presented below. Table 3 and Table 4 present the measured values for sensors A2 and A3, respectively.

For sensor-to-sensor reproducibility, the measured FRF amplitudes and the corresponding coefficients of variation across the three sensors are reported in Table 5. Then, the moduli of the presented FRFs are shown in the frequency domain, illustrated in Figure 5.

Linearity (test session #2) was assessed by considering the effect of excitation amplitude. Table 6 reports the FRF repeatability results for the three sensors under the 1g, 2g, and 3g excitation levels. The 15g scenario was included to assess the sensor behaviour close to its upper measurement range (FS), and the corresponding results are also reported below. In addition, the moduli of the presented FRFs are shown in the frequency domain plot in Figure 6. For the 15g test case, 5 Hz and 10 Hz excitations were omitted due to the displacement limits of the shaker, while the data for the 45 Hz excitation were corrupted. Nevertheless, these missing data points do not compromise the overall validity of the analysis.

Repeatability along the different measurement directions (test session #3 of the testing protocol) was performed by fixing the excitation amplitude and using sensor A1 to evaluate the FRF responses. Results are collected in Table 7. Also, Figure 7 illustrates the experimental FRFs for testing session #3.

To further evaluate manufacturing reproducibility, sensors from different production batches were tested under the same excitation condition.

Results of the testing session #4, in which sensors from different batches were compared, are summarised in Table 8. Figure 8 shows the related experimental FRFs.

3.3. Sensor Stability

For the stability assessment, a 10-minute acquisition was performed at 10 Hz using both the Movella DOT sensor and the laser vibrometer. As shown in Figure 9, both signals remained highly consistent over the 600 s duration with no visible amplitude drift. As summarised below (Table 9), the FFT peak amplitudes for both sensors exhibit negligible fluctuations across the ten intervals. The resulting FRF amplitudes maintain a stable average of 0.986, yielding a coefficient of variation of 0.019%, which satisfies the <0.05% stability threshold.

3.2. Acquisition in aliased conditions

Frequency domain analyses were also performed to investigate aliasing behaviour. The sensor was subjected to stepped-sine excitation at nominal frequencies ranging from 70 to 255 Hz (as defined in Table 1), with each test condition repeated three times. However, test repeatability was not available for excitation frequencies exceeding 170 Hz. Figure 10 shows a representative amplitude spectrum with $130\mathrm{Hz}$ excitation. Since the IMU samples at $120\mathrm{Hz}$ (Nyquist frequency of $60\mathrm{Hz}$), the $130\mathrm{Hz}$ signal is folded back and appears as an aliased peak at $10\mathrm{Hz}$. The spectrum of the Movella DOT also displays a static component (i.e., gravity acceleration), as for the previous acquisitions.

All the computed frequency response function (FRF) values are summarised in Table 10.

3.4. FRF Optimisation Results

Table 11 collects the identified time constants, corresponding cutoff frequencies, and RMSE values for the three models, computed by means of the experimental dataset of FRF amplitudes within the sensor’s bandwidth. Table 12 shows the coefficients of the general transfer function.

Figure 11 compares the measured FRF amplitude with the fitted time-constant models and the general transfer function.

3.5. Random Excitation Results

Figure 12 illustrates the experimental Power Spectral Densities (PSDs) of the reference laser vibrometer (PSD_v) and the Movella DOT sensor (PSD_e) under a 1g RMS random excitation, compared against the corrected responses (PSD_c) obtained by using the models extracted from the experimental results.

Furthermore, the PSD of each signal was compared against the reference vibrometer PSD_v. The resulting statistics (mean values and standard deviations) within the 8–40 Hz bandwidth are summarised in Table 13. Here, the fifth column compares the uncorrected Movella (${\mathrm{PSD}}_e$) and corrected (${\mathrm{PSD}}_c$) values against the reference (${\mathrm{PSD}}_v$), showing the percentage deviation from the reference.

4. Discussion

The calibration protocol, based on comparing the Movella DOT against the laser vibrometer reference by computing the FRF amplitude, serves to characterise the dynamic response of the tested devices. The experimental results demonstrate, in general, strong repeatability and reproducibility across the amplitudes of the Frequency Response Functions (FRFs), providing a robust dataset for modelling the dynamic behaviour of the Movella DOT. In particular, the computed FRF amplitudes in the repeatability assessment showed a worst-case standard deviation of less than 0.1%, as summarised in Table 2, Table 3 and Table 4 for three sensors of the same batch (A1, A2 and A3, respectively).

Considering the variability between the sensors (as shown in Table 5), the obtained repeatability showed a worst-case standard deviation limited to 0.2%, validating the consistency of the measured dynamic response. The measured trends for the three tested accelerometers are shown in Figure 5: the general trend of the FRF amplitudes shows a reduction of about 23% near the Nyquist limit, highlighting the need for a dynamic correction for accurate dynamic measurements.

The sensor linearity was verified by testing at different excitation levels. Indeed, as shown in Table 6, the measured FRFs provide repeatable trends (as depicted in Figure 6), with a worst-case standard deviation limited to about 0.6%. Cross-axis evaluation confirmed directional consistency with a maximum variation of 0.2% between the accelerometer triaxial directions (as shown in Table 7). Moreover, the general reduction trend was confirmed as shown in Figure 7, where the three FRFs are shown. Batch-to-batch repeatability was confirmed as well, given that the comparison between tested batches (shown in Figure 8 and Table 8) showed worst-case variability limited to 0.2%. Eventually, sensor stability was confirmed by the dedicated testing session, as shown in Figure 9 and summarised by Table 9: indeed, negligible variability was found (1σ % less than 0.02%), confirming stability of the sensor for the tested time, i.e., 10 min.

The evaluation, initially conducted within the standard sensor bandwidth, was extended into aliased conditions to characterise the attenuation profile of higher-frequency components: although the devices' dynamic response limits aliasing effects, signals above the Nyquist limit result in aliased components. This is quite evident when analysing the measured spectra shown in Figure 10, where the reference spectra (measured by the vibrometer) show the given input, whereas the signal measured in the aliased condition by the tested sensors shows an amplitude reduction and an apparent frequency symmetrised with respect to the Nyquist one. This is a limitation for the dynamic behaviour of the sensor, because in the case that the bandwidth of the measured signal cannot be controlled, the spectral measurements are corrupted by the aliasing error. The aliasing results agree with the FRF evaluation shown so far, as summarised by Table 10: in fact, the decreasing trend is confirmed, with a reduction of about 97% of the amplitude at 130 Hz. The data highlight a progressive attenuation of the sensor's response as the frequency increases from 70 Hz to 120 Hz, where the mean FRF reaches a minimum value of 0.004. It can be noticed that the amplitude never drops to a true null value under any tested condition; as the FRF amplitude approaches the system's noise floor, causing a signal-to-noise ratio degradation, resulting in an increase of the measurement uncertainty to about 23% at that frequency.

Regarding the numerical modelling of the sensor’s dynamic behaviour, the optimisation results showed that the first-order model captured the general low-pass trend of the measured FRF but showed higher discrepancies in the transition region due to sharper attenuation. Introducing an additional dynamic contribution (τ₂) in the second-order model enhanced the agreement with experimental data. The third-order model achieved the highest fidelity among the time-constant models, reducing the RMSE by approximately 50% compared to the first-order model (as shown in Table 11). The general transfer function model, whose coefficients are shown in Table 12, has the best performance, providing an overall RMSE which is the minimum among all the models. Indeed, the obtained reduction with the first-order modelling is about 90%.

Below 60 Hz, the time-constant models showed good agreement with the measured response, highlighting that the sensor behaviour within its bandwidth can be represented by a low-pass filter, as shown in similar devices [4]. When the full frequency range was considered, extending the analysis to the aliasing condition, the general transfer function provided the best global fitting performance, as also highlighted by the discrepancy in Figure 11 between the time-constant models and the experimental FRF amplitudes.

As mentioned before, the FRF modelling also provides the identification of the phase shift given by the device, allowing full dynamic correction. In Figure 11, the phase of all three time-constant models decreases monotonically. In contrast, the general model introduces an opposite trend, with a phase change near 120 Hz, corresponding to a theoretical null amplitude of the Movella DOT FRF. Indeed, the obtained results should be validated experimentally, solving the synchronisation issue between the two instruments, i.e., the Movella DOT and the laser vibrometer. Considering the general amplitude trend of the device (i.e., similar to a low-pass filtering for the FRF amplitude), it is suggested to use the phase trend of the time-constants models, assuming a general time delay between input and output.

Eventually, random testing showed that when measured input is within the bandwidth of the sensors, the vibration signal is properly recovered if the proposed dynamic correction procedure, based on the modelled FRFs, is implemented. Specifically, the PSD comparisons are summarised in Table 13. It collects the statistics of each PSD curve, i.e., PSD_v acquired from the laser vibrometer, PSD_e acquired from the Movella DOT device and PSD_c, the corrected Movella curves via the proposed numerical models. As reported, the PSD computation was carried out by means of 100 averages, which introduces a non-negligible standard deviation in the reference condition (i.e., about 9%). However, Table 13 shows that the uncorrected Movella DOT signal has a lower mean value and a higher standard deviation (in agreement with the FRF’s amplitude reduction) than the nominal laser vibrometer reference; in fact, within the 8–40 Hz range, the uncorrected signal underestimates the reference PSD by approximately 16%. Instead, applying the proposed correction models significantly improves the performance, aligning the mean PSD values and the related standard deviations with the vibrometer reference ones.

5. Conclusions

The assessment of wearable devices, i.e., the Movella Xsens DOT (Xsens) Inertial Measurement Units (IMUs), was carried out in this work, evaluating the accelerometer frequency response and accuracy during dynamic vibration profile acquisition.

By subjecting four sensors to varying excitation levels—with each condition repeated three times—the metrological characterisation demonstrated that the calibration procedure is both highly repeatable and reproducible across different sensors, different levels of excitation and across different batches.

A key finding indicates that sensor performance decreases by approximately 23% at the upper end of the acquisition bandwidth, highlighting a critical need to implement a robust dynamic correction procedure to accurately capture acceleration amplitudes across the entire frequency range. To address these limitations of using these wearables in sports biomechanics and/or in industrial scenarios, this study established a comprehensive metrological evaluation framework based on experimental Frequency Response Functions (FRFs) using a high-precision laser vibrometer as a reference.

The experimental FRFs revealed a non-uniform, decreasing frequency response. This characteristic attenuates signal amplitude as the input frequency increases, impacting both the time and frequency domains. FRF modelling was successfully performed, proposing different models for dynamic correction of the Movella DOT behaviour when the input is within the bandwidth. The modelling of the experimental FRF amplitude allows for acceleration amplitude correction and reconstruction of the phase shift behaviour for complete measurement dynamic correction. Additionally, the high-frequency limitations of the sensor were characterised by analysing its response under aliasing conditions, specifically when excitation frequencies exceeded the nominal bandwidth, in controlled aliasing conditions.

Future investigations should expand the accelerometers’ assessment framework to simultaneous multi-sensor acquisitions and validate the proposed phase recovery methods; moreover, characterising the network synchronisation latencies and potential clock drifts when multiple sensors are used would provide additional insight into the performance and possible limitations of these devices.