# Analysis of the Accuracy of Ten Algorithms for Orientation Estimation Using Inertial and Magnetic Sensing under Optimal Conditions: One Size Does Not Fit All

^{1}

^{BIO}Med Lab—Biomedical Engineering Lab and Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy

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^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Optimal Working Conditions

#### 2.2. Selected Algorithms

^{2}and the local magnetic norm, respectively). MCF is the implementation of VAC by MathWorks from Sensor Fusion and Tracking Toolbox.

#### 2.3. Experimental Setup

#### 2.4. Experimental Protocol

^{3}at a distance greater than 1 m from the floor. For this reason, the ferromagnetic disturbances could be neglected as also observed in the post processing by observing the almost constant magnetometer norm (the maximum difference was limited to 1 µT).

#### 2.5. Data Processing

#### 2.5.1. SP Data Pre-Processing and Synchronization with MIMUs Signals

#### 2.5.2. Orientation Estimation and Error Computation under Optimal Conditions

_{th2}of VAC whose lower limit was set to the value of the first accelerometer threshold (a lower value would be meaningless). The values for $uppe{r}_{1}$ and $uppe{r}_{2}$ were chosen to be large enough to ensure the exploration of all the relevant search space. In other words, errors obtained for values of ${p}_{1}$ and ${p}_{2}$ set to $uppe{r}_{1}$ and $uppe{r}_{2}$, respectively, are large. Figures in Appendix C display the values chosen for $uppe{r}_{1}$ and $uppe{r}_{2}$ for each SFA. The number of points for each parameter interval (i.e., the length of the ${\mathit{p}}_{\mathbf{1}vec}$ and ${\mathit{p}}_{\mathbf{2}vec}$ vectors) was different for each algorithm and it is a trade-off between the search space dimension and the computational cost; on average about 360 solutions (i.e., length (${\mathit{p}}_{\mathbf{1}vec}$) × length (${\mathit{p}}_{\mathbf{2}vec}$)) were explored for each SFA. Since the GCSs of the MIMU and SP were not aligned on the horizontal plane, to enable a meaningful comparison between the orientation obtained for the two systems, it was necessary to refer the latter to a common GCS. To this end, it was possible to benefit from the accurate alignment of the LCS of each system. Therefore, ${\mathit{q}}_{{A}_{\mathit{G}}}$, ${\mathit{q}}_{{B}_{\mathit{G}}}$, and ${\mathit{q}}_{\mathit{S}{\mathit{P}}_{\mathit{G}}}$ were separately referred to their initial frame to obtain ${\mathit{q}}_{\mathrm{A}}$, ${\mathit{q}}_{B}$, and ${\mathit{q}}_{\mathit{S}\mathit{P}}$, respectively, as follows (the $\otimes $ and

^{*}operators represent the product and complex conjugate operator in the quaternion algebra, respectively):

Algorithm 1. Pseudocode to detail the orientation estimation process for each SFA. |

for each pair of MIMUs (Xsens, APDM, and Shimmer)for each angular rate condition (slow, medium, fast)remove the static bias for each gyroscope compute the starting orientation for each MIMU initialize the matrix $\mathit{e}$ (#rows = length(${\mathit{p}}_{\mathbf{1}}$), #columns = length(${\mathit{p}}_{\mathbf{2}}$)) for each value ${\mathit{p}}_{\mathbf{2}}$ belonging to ${\mathit{p}}_{\mathbf{2}\mathit{v}\mathit{e}\mathit{c}}$ between [$0$, $uppe{r}_{2}$]for each value ${\mathit{p}}_{\mathbf{1}}$ belonging to ${\mathit{p}}_{\mathbf{1}\mathit{v}\mathit{e}\mathit{c}}$ between [$0$, $uppe{r}_{1}$]compute the absolute orientation of each MIMU separately with the SFA under analysis to obtain ${\mathit{q}}_{{A}_{\mathit{G}}}$ and ${\mathit{q}}_{{\mathit{B}}_{\mathit{G}}}$ refer ${\mathit{q}}_{{A}_{\mathit{G}}}$ and ${\mathit{q}}_{{\mathit{B}}_{\mathit{G}}}$ to the starting orientation to obtain ${\mathit{q}}_{\mathit{A}}$ and ${\mathit{q}}_{\mathit{B}}$, as done in (1) compute the absolute orientation error of ${\mathit{q}}_{\mathit{A}}$ and ${\mathit{q}}_{\mathit{B}}$ separately using the gold standard ${\mathit{q}}_{\mathit{S}\mathit{P}}$ to obtain $\Delta {\mathit{q}}_{\mathit{a}\mathit{b}\mathit{s}\mathit{A}}$ and $\Delta {\mathit{q}}_{\mathit{a}\mathit{b}\mathit{s}\mathit{B}}$, as done in (2) convert $\Delta {\mathit{q}}_{\mathit{a}\mathit{b}\mathit{s}\mathit{A}}$ and $\Delta {\mathit{q}}_{\mathit{a}\mathit{b}\mathit{s}\mathit{B}}$ into angular rotation errors to obtain $\Delta {\mathit{\theta}}_{\mathit{a}\mathit{b}\mathit{s}\mathit{A}}$ and $\Delta {\mathit{\theta}}_{\mathit{a}\mathit{b}\mathit{s}\mathit{B}}$ compute the average value between the two absolute errors to obtain $\Delta {\mathit{\theta}}_{\mathit{a}\mathit{b}\mathit{s}}$ compute the RMS of $\Delta {\mathit{\theta}}_{\mathit{a}\mathit{b}\mathit{s}}$ considering only the dynamic parts of the recording to obtain ${e}_{p1,p2}$ add ${e}_{p1,p2}$ to the matrix $\mathit{e}$ endendfind the optimal region of $\left({\mathit{p}}_{\mathbf{1}\mathit{v}\mathit{e}\mathit{c}},{\mathit{p}}_{\mathbf{2}\mathit{v}\mathit{e}\mathit{c}}\right)$ which correspond to the range of $\mathit{e}$ which includes its minimum (${e}_{opt}$) + 0.5 deg to obtain ${\mathit{p}}_{opt\_1}$ and ${\mathit{p}}_{opt\_2}$ find the value of $\mathit{e}$ which correspond to the default parameter values to obtain ${e}_{def}$ endend |

#### 2.6. Data Analysis

- SFA analytical formulation
- rotation rate magnitude
- different commercial products.

#### 2.6.1. Identification of the Optimal Regions and the Corresponding Errors

- Minimum absolute orientation error which corresponds to the selection of the optimal parameter values: ${e}_{opt}=\mathrm{min}\left(\mathit{e}\left({\mathit{p}}_{\mathbf{1}},{\mathit{p}}_{\mathbf{2}}\right)\right)$, where $\mathit{e}$ is the matrix of the average errors between the two MIMUs of dimensions equal to [length(${\mathit{p}}_{\mathbf{1}}$), length(${\mathit{p}}_{\mathbf{2}}$)].
- Optimal parameter region is defined as the range of parameter values for which the relevant orientation errors are equal to the minimum error plus 0.5 deg (i.e., the SP uncertainty band, as stated in Section 2.5.1). These regions are defined as: $\{{\mathit{p}}_{op{t}_{1}},{\mathit{p}}_{op{t}_{2}}\}=\{\left({\mathit{p}}_{1},{\mathit{p}}_{2}\right)|\mathit{e}\le {e}_{opt}+0.5deg\}$. An example of optimal region is illustrated in Figure 2 for the VAK filter. When only one parameter was tuned (MAD, VAC, GUO, MKF) $\mathit{e}$ was a vector and the optimal region degenerated into a 1D interval.

#### 2.6.2. Identification of the Default Errors

#### 2.6.3. Statistical Analysis to Evaluate the Influence of the SFAs and of the Experimental Factors

#### 2.6.4. Computation Time of the Different SFAs

^{®}Core™ i7-10510U CPU @ 1.80 GHz (Intel ©, Santa Clara, CA, USA)—Microsoft™ Windows 10 (Microsoft ©, Redmond, WA, USA) when processing a dataset of 25386 samples without executing any other programs.

## 3. Results

#### 3.1. Optimal and Default Errors

#### 3.2. Optimal Regions

#### 3.3. Influence of the Experimental Factors on the Absolute Accuracy

#### 3.3.1. Influence of the Specific SFA (3 Rotation Rates × 3 Commercial Products)

_{opt}values obtained by each SFA are listed in ascending order in Table 4.

#### 3.3.2. Influence of the Rotation Rate (10 SFA × 3 Commercial Products)

^{−9}) resulting from the Friedman’s test cast doubts on the validity of the null hypothesis. A multiple comparison test with Bonferroni’s correction (α = 0.05) revealed a statistically significant difference among the three distributions (Table 6).

#### 3.3.3. Influence of the Commercial Product (10 SFA × 3 Rotation Rates)

^{−7}) resulting from the Friedman’s test cast doubts on the validity of the null hypothesis. A multiple comparison test with Bonferroni’s correction (α = 0.05) revealed a statistically significant difference among the three distributions (Table 8).

#### 3.4. Computation Time of the Different SFAs

## 4. Discussion

#### 4.1. The Importance of Properly Tuning Each SFA

^{2}. This evidence can be also graphically observed in the figures in Appendix C, in fact for all algorithms but MKF there is not a common intersection among the optimal regions when varying the experimental scenario. The above-mentioned figures also prove that for some SFAs (e.g., SAB), the specific tuning for each experimental scenario is particularly critical since the overlapping among the optimal regions is very limited. At the same time the errors obtained using the default parameter values (${e}_{def}$) highlight the inadequacy to estimate the absolute orientation with the same parameter values for a given SFA under different experimental scenarios. These findings provide a further justification of the different level of accuracy reported for the different SFAs in previous studies which entailed the comparison among filters optimally tuned and filters fed with the default or non-optimal parameters (e.g., [14,16]). It is clear that any comparison carried out without a common strategy to tune the SFA parameter values would be lacking generality. In fact, for some algorithms ${e}_{def}$ can greater than 100 deg (see Table 3). It is worth pointing out that the optimal errors reported in Table 3 can be considered the lower bound for those SFAs under similar experimental conditions. Lower errors can be only obtained under less challenging scenarios and/or using higher performing MIMUs.

#### 4.2. Influence of the SFA and of the Experimental Factors on the Absolute Accuracy

#### 4.3. Computation Time of the SFAs

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Glossary

CF | complementary filter |

GCS | global coordinate system |

KF | Kalman filter |

LCS | local coordinate system |

MIMU | magneto-inertial measurement unit |

RMS | root mean square |

SFA | sensor fusion algorithm |

SP | stereo-photogrammetric system |

STD | standard deviation |

Absolute orientation | the orientation of the local coordinate system (LCS) of a system with respect to its GCS |

Absolute orientation error | the difference between the orientation of the LCS of a magneto-inertial measurement unit (MIMU) computed by a sensor fusion algorithm (SFA) and its actual orientation computed by the optical reference (SP) and expressed by the angle given by the axis-angle convention |

${e}_{def}$ | absolute orientation error corresponding to the selection of the default parameter values |

${e}_{opt}$ | minimum absolute orientation error which corresponds to the selection of the optimal parameter values |

Optimal parameter region | the range of parameter values for which the orientation errors are equal to ${e}_{opt}$ plus 0.5 deg |

## Appendix A

First Author | SFA(s) Employed | MIMU(s) Employed | Experimental Protocol | Standard | Declared Errors |
---|---|---|---|---|---|

Picerno 2011 [30] | Xsens filter (KF) | 9 Xsens MTw | MIMUs were aligned on a rigid body which was oriented in 12 different poses. Only the static orientation was considered. | none | Differences were up to 11.4 for yaw angle (in an ideal case they should be null). |

Bergamini 2014 [25] | Madgwick [9] (CF) Sabatini [34] (KF) | 1 APDM-Opal | Manual tasks (slow velocities, short time, and small capture volume). Locomotion task (larger capture volume, three minutes, no static phases). | SP | RMS errors were similar for CF and KF: from 5.5 (manual) to 21 (locomotion) tasks. |

Lebel 2015 [26] | Xsens filter (KF) APDM filter (KF) Inertial Labs filter (CF) | 4 Xsens-MTx 4 APDM-Opal 4 Inertial Labs-Osv3 | MIMUs attached to a gimbal. Rotation of the gimbal axes to obtain both planar (2D) and 3D motions at quasi-constant low and high rotation rates (90 dps and 180 dps) for 120 s. | SP | Mean errors increased up to 7 when the rotation rate increased, although this was less evident for Xsens filter. |

Ricci 2016 [27] | APDM filter (KF) Tian [46] (CF) | 6 APDM-Opal | MIMUs attached to a robot arm Static (different orientations) Dynamic (sinusoidal rotations around MIMU axis, the RMS of the angular velocity ranged from 2.1 dps to 150 dps). | Robot angles | In static the maximum errors amounted to 1 for CF and 1.6 for KF. In the dynamic trials, the KF exhibited the best performance. Errors increased when the velocity increased. |

Ludwig 2018 [28] | Madgwick [9] (CF) Mahony [6] (CF) Marins [47] (KF) | 1 embedded on a quadcopter | The quadrotor flew to perform both loop and random sequences within the volume capture (1m × 1m × 1m). | SP | RMS errors amounted to 11, 13, and 13.3 for Mahony, Madgwick and Marius, respectively. |

First Author | SFA(s) Employed | MIMU(s) Employed | Experimental Protocol | Standard | Declared Errors |
---|---|---|---|---|---|

Young 2009 [7] | Proposed CF Yun [48] (KF) | 1 Orient | Gently movements by hand (20 s). Walking with MIMU on the lower leg (five trials, 30 s each). | SP | RMS inclination (and yaw) errors. Gently movements: 3.2 (9.6) for CF and 5.4 deg (10.1) for KF. Walking: 4 deg (10.9) for CF and 11.9 (31.8) for KF. |

Fourati 2014 [8] | Proposed CF Xsens filter (KF) | 1 Xsens-Mti 1 Xsens-MTi-G | Manual (3 straight translations along each MIMU axis and a free 3 D motion). | none | Average inclination (and yaw) difference between the estimates of the CF and KF ranged between [1,2,3] ([2,3,4,5]). |

Madgwick [9] | Proposed CF Xsens filter (KF) | 1 Xsens-MTx | Static. Dynamic (manual motions). | SP | RMS errors in dynamic amounted to 1.1 for the proposed and 1.3 for KF. |

Valenti 2015 [2] | Proposed CF Sabatini [34] (KF) Madgwick [9] (CF) | PhidgetSpatial 3/3/3 embedded in a quadrotor | The vehicle flew in a volume of 10 m × 10 m × 10 m to perform loop trajectories. | SP | RMS inclination (and yaw) errors amounted to 1.7 (16.6), 2.5 (20.3), and 3 (76.2) for the proposed CF, the KF, and the CF by Madgwick. |

Marantos 2016 [14] | Proposed CF St iNEMO filter (KF) Mahony [6] (CF) | Designed | MIMU mounted on a gimbal which was subjected to a combined motion in all axes including strong acceleration and ferromagnetic disturbances. | Gimbal encoders | Mean inclination (and yaw) errors amounted to 1.0 (2.5), 5.5 (10.5), and 4.9 (11.5) for the proposed CF, the CF from Mahony and KF. |

Olivares 2016 [15] | Two proposed KFs (optimal approach vs. algebraic solution) | 1 Wagyromag (designed) | MIMU mounted on a device moved at 3 speeds including high accelerations and magnetic disturbances. | Potentiometer—mounted on the hinge. | Mean RMS errors: 1.5 for KF with optimal approach and 2.1 for KF with algebraic solution. |

Seel 2017 [16] | Proposed CF Madgwick [9] (CF) | none | Simulated magnetic field environment. | Synthetic ground-truth. | Inclination errors for Madgwick CF were higher than 4. |

Guo 2017 [17] | Proposed KF Fourati 2014 [8] (CF) Marantos 2016 [14] (CF) Valenti 2016 [2] (KF) | 1 MicroStrain 3DM- GX3-25 | Random movements carried out by hand. | Provided by the proprietary algorithm (not properly a gold standard). | Mean inclination (and yaw) errors amounted to 0.1 (0.1), 0.2 (0.7), 1.3 (0.6), and 2.2 (3.7) for the proposed KF, the CF by Fourati, the CF by Marantos, and the KF by Valenti. |

Fan 2018 [18] | Basic proposed CF Finite-state proposed CF | 1 Xsens-MTw | Fast MIMU movement up and down (range of 60 cm) close to a big ferromagnetic box. | SP | RMS inclination (and yaw) errors 6 (7) and 1 (1) for basic CF and finite-state CF during large acceleration. RMS Yaw errors 7 and 1 for basic CF and finite-state CF during magnetic disturbances. |

Weber 2020 [22] | Proposed neural-network See1 2017 [16] (CF) | 1 Myon AG-aktos-t | Roto-translation movements at different speeds including pauses. | SP | RMS errors equal to 1.4 for the neural network and to 2.8 for the CF. |

## Appendix B

STD | Accelerometer (mg) | Gyroscope (deg/s) | Magnetometer (µT) | ||||||
---|---|---|---|---|---|---|---|---|---|

x | y | z | x | y | z | x | y | z | |

Xsens-MTx #1 | 0.86 | 0.80 | 0.85 | 0.38 | 0.39 | 0.37 | 0.06 | 0.04 | 0.04 |

Xsens-MTX #2 | 0.82 | 0.86 | 0.80 | 0.44 | 0.40 | 0.40 | 0.05 | 0.06 | 0.06 |

APDM-OPAL #1 | 0.38 | 0.33 | 0.38 | 0.16 | 0.23 | 0.11 | 0.26 | 0.23 | 0.20 |

APDM-OPAL #2 | 0.34 | 0.32 | 0.35 | 0.16 | 0.27 | 0.19 | 0.26 | 0.25 | 0.20 |

Shimmer-Shimmer 3 #1 | 1.06 | 0.97 | 1.26 | 0.09 | 0.08 | 0.09 | 0.84 | 0.84 | 0.69 |

Shimmer-Shimmer 3 #2 | 1.12 | 1.09 | 1.29 | 0.06 | 0.06 | 0.06 | 0.97 | 0.97 | 0.58 |

**Table A4.**Sensor Gyroscope Biases during the First 60 s of Static Acquisition before the Movements (before) and Difference (diff) with the Bias Record at the End of Experiments.

Gyroscope Bias (deg/s) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|

Slow | Medium | Fast | ||||||||

x | y | z | x | y | z | x | y | z | ||

Xsens-MTx #1 | before | −0.24 | −1.70 | −0.32 | −0.26 | −1.70 | −0.33 | −0.25 | −1.69 | −0.33 |

diff | 0.00 | −0.05 | 0.00 | −0.01 | 0.00 | −0.02 | −0.01 | −0.01 | −0.02 | |

Xsens-MTX #2 | before | −0.26 | 0.76 | 0.42 | −0.26 | 0.76 | 0.43 | −0.28 | 0.74 | 0.41 |

diff | 0.01 | 0.01 | 0.02 | 0.03 | 0.00 | 0.03 | −0.01 | −0.02 | −0.02 | |

APDM-OPAL #1 | before | 0.78 | −0.57 | 0.34 | 0.59 | −0.80 | 0.36 | 0.74 | −0.78 | 0.37 |

diff | 0.08 | 0.04 | −0.02 | −0.12 | −0.02 | 0.00 | −0.02 | 0.12 | −0.01 | |

APDM-OPAL #2 | before | −1.10 | −0.06 | −0.71 | −1.20 | −0.05 | −0.48 | −1.11 | 0.17 | −0.48 |

diff | 0.07 | 0.01 | −0.03 | −0.17 | −0.17 | −0.05 | −0.09 | −0.03 | −0.10 | |

Shimmer-Shimmer 3 #1 | before | −0.03 | −0.06 | −0.01 | −0.02 | −0.05 | 0.01 | −0.03 | −0.07 | 0.02 |

diff | −0.01 | −0.03 | 0.00 | −0.01 | 0.00 | 0.00 | 0.00 | −0.01 | 0.00 | |

Shimmer-Shimmer 3 #2 | before | −0.06 | −0.03 | 0.09 | −0.06 | −0.03 | 0.08 | −0.06 | −0.05 | 0.10 |

diff | −0.01 | −0.03 | 0.01 | −0.01 | −0.03 | 0.01 | −0.01 | −0.03 | 0.03 |

**Table A5.**Sensor Specifications. Reprinted with permission from ref. [31]. Copyright 2020 IEEE.

Range | A/D Resolution | Alignment Error | |
---|---|---|---|

Xsens-MTx | |||

Accelerometer | ±50 m/s^{2} | 16 bits | 0.1 deg |

Gyroscope | ±1200 deg/s | 16 bits | 0.1 deg |

Magnetometer | ±75 µT | 16 bits | 0.1 deg |

APDM-OPAL | |||

Accelerometer | ±16 m/s^{2} | 14 bits | |

Gyroscope | ±2000 deg/s | 16 bits | |

Magnetometer | ±800 µT | 12 bits | |

Shimmer-Shimmer3 | |||

Accelerometer | ±16 m/s^{2} | 16 bits | |

Gyroscope | ±2000 deg/s | 16 bits | |

Magnetometer | ±400 µT | 16 bits |

## Appendix C

**Figure A1.**Optimal regions for each SFA and for each experimental condition. (

**a**) MAH, (

**b**) SAB, (

**c**) MAD, (

**d**) LIG, (

**e**) VAC, (

**f**) VAK, (

**g**) SEL, (

**h**) GUO, (

**i**) MCF, (

**j**) MKF.

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**Figure 1.**Board equipped with six magneto-inertial measurement units (MIMUs) (relevant local coordinate system (LCS) in blue) and the eight reflective markers. The three central markers were used to define the stereo-photogrammetric (SP) system LCS (in green). Board axes (in red) are coincident with MIMUs and SP system LCSs. Reprinted with permission from ref. [31]. Copyright 2020 IEEE.

**Figure 2.**Optimal regions (one for each experimental scenario) for Valenti et al., 2016 (VAK). σ

_{acc}values are exponentially spaced.

**Figure 3.**Rotation rate effect: slow, medium, and fast ${e}_{opt}$ distributions (10 SFAs × 3 commercial products). It is possible to assess that errors obtained at the fast rotation rate are worse than those at slow rotation rate and the three distributions statistically differ across all of them.

**Figure 4.**Commercial product effect: Xsens, APDM, and Shimmer ${e}_{opt}$ distributions (10 SFAs × 3 rotation rates). It is possible to assess that APDM and Shimmer distributions statistically differ from that of Xsens.

**Table 1.**Details of each Sensor Fusion Algorithm (SFA) considered. The # Params column reports the total number of parameters of each SFA. The p

_{1}and p

_{2}report the description of the parameter tuned to detect the optimal values. a.u. = arbitrary units.

CF | # Params | ${\mathit{p}}_{\mathbf{1}}$ | Default | ${\mathit{p}}_{\mathbf{2}}$ | Default | ||

MAH | 2 | k_{p}—inverse gyroscope weight | 1 | rad/s | k_{i}—weight for online bias estimation | 0.3 | rad/s |

MAD | 1 | β—inverse gyroscope weight | 0.1 | rad/s | / | / | |

VAC | 9 | g_{mag}—magnetometer weight | 0.01 | a.u. | a_{th2}—threshold for accelerometer vector selection | 0.2 | a.u. |

SEL | 4 | τ_{acc}—accelerometer time constant | 1 | s | τ_{mag}—magnetometer time constant | 3 | s |

MCF | 2 | g_{mag}—magnetometer weight | 0.01 | a.u. | / | / | |

KF | # Params | ${\mathit{p}}_{\mathbf{1}}$ | Default | ${\mathit{p}}_{\mathbf{2}}$ | Default | ||

SAB | 6 | σ_{gyr}—inverse gyroscope weight | 0.007 | rad/s | a_{th}—threshold for accelerometer vector selection | 40 | mg |

LIG | 6 | σ_{gyr}—inverse gyroscope weight | 1 | rad/s | c_{b}—Gauss-Markov parameter of the prediction model to set the variance of external acceleration and ferromagnetic disturbances | 1 | a.u. |

VAK | 3 | σ_{gyr}—inverse gyroscope weight | 0.004 | rad/s | σ_{acc}—inverse accelerometer weight | 0.014 | m/s^{2} |

GUO | 3 | σ_{gyr}—inverse gyroscope weight | 0.001 | rad/s | / | / | |

MKF | 8 | σ^{2}_{gyr}—inverse gyroscope weight | 9.14 × 10^{−5} | (rad/s)^{2} | / | / |

**Table 2.**Statistical analysis plan to evaluate the influence of SFAs, rotation rate, and commercial product on the errors.

Influencing Factor | Number of Distributions | Number of Values for Each Distribution |
---|---|---|

SFA | 10 (one for each SFA) | 9 (=3 rotation rates × 3 commercial products) |

Rotation rate | 3 (one for each rotation rate) | 30 (=10 SFAs × 3 commercial products) |

Commercial product | 3 (one for each commercial product) | 30 (=10 SFAs × 3 rotation rates) |

**Table 3.**The Optimal Errors Are Reported with the Absolute Errors Obtained Using the Default Parameter Values.

CF | ${\mathit{e}}_{\mathit{o}\mathit{p}\mathit{t}}$ | ${\mathit{e}}_{\mathit{d}\mathit{e}\mathit{f}}$ | KF | ${\mathit{e}}_{\mathit{o}\mathit{p}\mathit{t}}$ | ${\mathit{e}}_{\mathit{d}\mathit{e}\mathit{f}}$ | ||
---|---|---|---|---|---|---|---|

Xsens | Slow | MAH | 2.5 | 4.2 | SAB | 2.2 | 67.9 |

Medium | 2.4 | 11.9 | 2.1 | 96.6 | |||

Fast | 4.0 | 13.0 | 2.4 | 53.9 | |||

APDM | Slow | 3.8 | 3.9 | 5.0 | 77.5 | ||

Medium | 4.8 | 17.7 | 5.7 | 62.6 | |||

Fast | 8.2 | 12.3 | 8.3 | 9.9 | |||

Shimmer | Slow | 3.4 | 5.9 | 4.5 | 71.1 | ||

Medium | 4.6 | 38.2 | 4.9 | 14.5 | |||

Fast | 7.6 | 17.0 | 8.5 | 30.0 | |||

Xsens | Slow | MAD | 2.7 | 4.7 | LIG | 1.9 | 3.7 |

Medium | 2.5 | 5.2 | 2.0 | 3.9 | |||

Fast | 4.0 | 6.8 | 2.9 | 4.8 | |||

APDM | Slow | 3.8 | 4.1 | 3.6 | 3.6 | ||

Medium | 4.6 | 4.6 | 4.9 | 5.0 | |||

Fast | 8.1 | 8.2 | 4.6 | 4.6 | |||

Shimmer | Slow | 3.9 | 4.3 | 4.4 | 4.4 | ||

Medium | 4.9 | 5.2 | 4.0 | 4.2 | |||

Fast | 8.8 | 8.9 | 6.3 | 6.5 | |||

Xsens | Slow | VAC | 4.0 | 4.1 | VAK | 1.2 | 22.3 |

Medium | 5.0 | 5.9 | 1.6 | 21.4 | |||

Fast | 7.2 | 10.0 | 2.5 | 72.8 | |||

APDM | Slow | 3.5 | 3.6 | 3.6 | 29.6 | ||

Medium | 6.1 | 11.8 | 6.0 | 30.4 | |||

Fast | 8.3 | 15.1 | 9.2 | 81.9 | |||

Shimmer | Slow | 3.8 | 3.8 | 4.0 | 32.6 | ||

Medium | 10.2 | 19.2 | 4.4 | 48.8 | |||

Fast | 11.5 | 23.6 | 8.2 | 100.1 | |||

Xsens | Slow | SEL | 3.1 | 4.0 | GUO | 2.3 | 3.7 |

Medium | 2.5 | 4.6 | 2.3 | 4.9 | |||

Fast | 5.1 | 6.7 | 5.7 | 10.6 | |||

APDM | Slow | 3.7 | 3.8 | 4.2 | 4.5 | ||

Medium | 7.1 | 7.3 | 5.1 | 5.3 | |||

Fast | 8.0 | 8.8 | 9.4 | 12.0 | |||

Shimmer | Slow | 3.4 | 3.5 | 4.0 | 4.0 | ||

Medium | 5.0 | 8.4 | 5.1 | 5.7 | |||

Fast | 9.4 | 11.8 | 13.7 | 16.7 | |||

Xsens | Slow | MCF | 3.3 | 4.5 | MKF | 4.2 | 4.9 |

Medium | 6.1 | 6.2 | 4.8 | 8.7 | |||

Fast | 6.6 | 7.8 | 6.7 | 10.9 | |||

APDM | Slow | 3.8 | 4.2 | 3.6 | 4.8 | ||

Medium | 12.3 | 12.3 | 5.3 | 14.3 | |||

Fast | 7.9 | 9.3 | 7.2 | 10.7 | |||

Shimmer | Slow | 5.0 | 5.2 | 3.9 | 5.8 | ||

Medium | 10.0 | 10.1 | 8.4 | 45.2 | |||

Fast | 8.6 | 12.0 | 9.9 | 19.0 |

LIG | VAK | MAH | MAD | SAB | SEL | GUO | MKF | VAC | MCF | |
---|---|---|---|---|---|---|---|---|---|---|

${e}_{opt}$ | 3.8 ± 1.4 | 4.5 ± 2.8 | 4.6 ± 2.1 | 4.8 ± 2.2 | 4.8 ± 2.4 | 5.3 ± 2.4 | 5.8 ± 3.7 | 6.0 ± 2.2 | 6.6 ± 2.9 | 7.1 ± 2.9 |

(deg) | Slow | Medium | Fast |
---|---|---|---|

${e}_{opt}$ | 3.5 ± 0.9 | 5.2 ± 2.5 | 7.3 ± 2.6 |

**Table 6.**Results of Friedman’s Test with Bonferroni’s Correction to Investigate the Differences among the Three Rotation Rate Conditions.

Scenario | Optimal Conditions |
---|---|

Slow vs. fast | Significantly different (p < 1× 10^{−4}) |

Slow vs. medium | Significantly different (p < 1× 10^{−3}) |

Fast vs. medium | Significantly different (p = 0.013) |

(deg) | Xsens-MTx | APDM-Opal | Shimmer-Shimmer 3 |
---|---|---|---|

${e}_{opt}$ | 3.5 ± 1.7 | 6.0 ± 2.3 | 6.5 ± 2.8 |

**Table 8.**Results of Friedman’s Test with Bonferroni’s Correction to Investigate the Differences among the Three Commercial Product Conditions.

Scenario | Optimal Conditions |
---|---|

Xsens vs. APDM | Significantly different (p < 1× 10^{−5}) |

Xsens vs. Shimmer | Significantly different (p < 1× 10^{−6}) |

APDM vs. Shimmer | Not significantly different (p = 1) |

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**MDPI and ACS Style**

Caruso, M.; Sabatini, A.M.; Laidig, D.; Seel, T.; Knaflitz, M.; Della Croce, U.; Cereatti, A.
Analysis of the Accuracy of Ten Algorithms for Orientation Estimation Using Inertial and Magnetic Sensing under Optimal Conditions: One Size Does Not Fit All. *Sensors* **2021**, *21*, 2543.
https://doi.org/10.3390/s21072543

**AMA Style**

Caruso M, Sabatini AM, Laidig D, Seel T, Knaflitz M, Della Croce U, Cereatti A.
Analysis of the Accuracy of Ten Algorithms for Orientation Estimation Using Inertial and Magnetic Sensing under Optimal Conditions: One Size Does Not Fit All. *Sensors*. 2021; 21(7):2543.
https://doi.org/10.3390/s21072543

**Chicago/Turabian Style**

Caruso, Marco, Angelo Maria Sabatini, Daniel Laidig, Thomas Seel, Marco Knaflitz, Ugo Della Croce, and Andrea Cereatti.
2021. "Analysis of the Accuracy of Ten Algorithms for Orientation Estimation Using Inertial and Magnetic Sensing under Optimal Conditions: One Size Does Not Fit All" *Sensors* 21, no. 7: 2543.
https://doi.org/10.3390/s21072543