# Criterion Validity of Linear Accelerations Measured with Low-Sampling-Frequency Accelerometers during Overground Walking in Elderly Patients with Knee Osteoarthritis

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

^{®}; Copenhagen, Denmark) designed for measuring physical activities in the healthcare sector and for research projects. We investigated whether the accelerations measured with small low-frequency accelerometers in patients with knee OA were valid representations of the true accelerations for kinematic gait assessment. We compared the accelerations measured with SENS sensors at ~12.5 Hz with a standard inertial-sensor-based motion-capture system as the gold standard to determine the criterion validity of accelerometers with low sampling frequencies. We also evaluated the test–retest reliability of the measurements obtained with low-sampling-frequency sensors.

- The accuracy and precision of the measurements of low-sampling-frequency accelerometers were determined to evaluate the performance of these sensors in the time and frequency domains and with respect to different coordinate axes;
- By employing musculoskeletal modeling, we could compare the accelerations of two systems at an exact location. We could compare the accelerations in close-to-real-life situations and without needing sophisticated motion and gait laboratories;
- In addition, we evaluated the test–retest repeatability of the accelerations measured with these sensors and compared them with the criterion system.

## 2. Materials and Methods

#### 2.1. Participants

^{2}), a recent history of operations in the lower limbs, neurological movement disorders, and inflammatory arthritis were excluded from the study. The study was approved by Regional Committee on Health Research Ethics (journal number 2021-000438).

#### 2.2. Equipment

#### 2.2.1. Xsens

#### 2.2.2. SENS

^{®}, Copenhagen, Denmark) only contain a 3D accelerometer. These sensors are medically approved devices designed for the long-term monitoring of patients’ physical activities. In addition, they have an internal memory for storing up to 14 days of data and cloud connectivity via a mobile telephone. Following the manufacturer’s instructions, we attached two SENS sensors on the lateral distal side of each thigh about 10 cm above the lateral femoral epicondyle (Figure 1B).

#### 2.3. Data Collection

#### 2.4. Data Processing

_{x}, acc

_{y}, and acc

_{z}along the three perpendicular local axes, x, y, and z, respectively (Figure 1C). In addition, the length of the acceleration vector, n, was also calculated.

#### 2.5. Statistical Analysis

#### 2.5.1. Test–Retest Reliability

#### 2.5.2. Time-Domain Comparison

_{s}) was calculated for the correlations between the differences and Criterion to demonstrate the stability of the bias across the range of Criterion values.

_{acc}– min

_{acc}).

#### 2.5.3. Frequency-Domain Comparison

## 3. Results

#### 3.1. Participants

#### 3.2. Test–Retest Reliability

#### 3.3. Time-Domain Comparison

#### 3.4. Frequency-Domain Comparison

_{s}were −0.07, 0.15, 0.23, and 0.06 with respect to the x-, y-, and z-axes, and the accelerations’ magnitude (n), respectively.

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Spitaels, D.; Mamouris, P.; Vaes, B.; Smeets, M.; Luyten, F.; Hermens, R.; Vankrunkelsven, P. Epidemiology of knee osteoarthritis in general practice: A registry-based study. BMJ Open
**2020**, 10, e031734. [Google Scholar] [CrossRef] [PubMed] [Green Version] - da Costa, B.R.; Vieira, E.R.; Gadotti, I.C.; Colosi, C.; Rylak, J.; Wylie, T.; Armijo-Olivo, S. How Do Physical Therapists Treat People with Knee Osteoarthritis, and What Drives Their Clinical Decisions? A Population-Based Cross-Sectional Survey. Physiother. Can.
**2017**, 69, 30–37. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Artz, N.; Elvers, K.T.; Lowe, C.M.; Sackley, C.; Jepson, P.; Beswick, A.D. Effectiveness of physiotherapy exercise following total knee replacement: Systematic review and meta-analysis. BMC Musculoskelet. Disord.
**2015**, 16, 15. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Jiang, S.; Xiang, J.; Gao, X.; Guo, K.; Liu, B. The comparison of telerehabilitation and face-to-face rehabilitation after total knee arthroplasty: A systematic review and meta-analysis. J. Telemed. Telecare
**2018**, 24, 257–262. [Google Scholar] [CrossRef] - Odole, A.C.; Ojo, O.D. Is telephysiotherapy an option for improved quality of life in patients with osteoarthritis of the knee? Int. J. Telemed. Appl.
**2014**, 2014, 903816. [Google Scholar] [CrossRef] [Green Version] - Mundt, M.; Thomsen, W.; David, S.; Dupré, T.; Bamer, F.; Potthast, W.; Markert, B. Assessment of the measurement accuracy of inertial sensors during different tasks of daily living. J. Biomech.
**2019**, 84, 81–86. [Google Scholar] [CrossRef] - Trojaniello, D.; Cereatti, A.; Pelosin, E.; Avanzino, L.; Mirelman, A.; Hausdorff, J.M.; Della Croce, U. Estimation of step-by-step spa-zio-temporal parameters of normal and impaired gait using shank-mounted magneto-inertial sensors. J. Neuroeng. Rehabil.
**2014**, 11, 1–12. [Google Scholar] [CrossRef] [Green Version] - Picerno, P. 25 years of lower limb joint kinematics by using inertial and magnetic sensors: A review of methodological approaches. Gait Posture
**2017**, 51, 239–246. [Google Scholar] [CrossRef] - Karas, M.; Bai, J.; Strączkiewicz, M.; Harezlak, J.; Glynn, N.W.; Harris, T.; Zipunnikov, V.; Crainiceanu, C.; Urbanek, J.K. Accelerometry Data in Health Research: Challenges and Opportunities: Review and Examples. Stat. Biosci.
**2019**, 11, 210–237. [Google Scholar] [CrossRef] - Zhou, L.; Fischer, E.; Tunca, C.; Brahms, C.M.; Ersoy, C.; Granacher, U.; Arnrich, B. How we found our imu: Guidelines to IMU selection and a comparison of seven IMUs for pervasive healthcare applications. Sensors
**2020**, 20, 4090. [Google Scholar] [CrossRef] - Bartholdy, C.; Gudbergsen, H.; Bliddal, H.; Kjærgaard, M.; Lykkegaard, K.L.; Henriksen, M. Reliability and Construct Validity of the SENS Motion
^{®}Activity Measurement System as a Tool to Detect Sedentary Behaviour in Patients with Knee Osteoarthritis. Arthritis**2018**, 2018, 6596278. [Google Scholar] [CrossRef] [PubMed] - Pedersen, B.S.; Kristensen, M.T.; Josefsen, C.O.; Lykkegaard, K.L.; Jønsson, L.R.; Pedersen, M.M. Validation of Two Activity Monitors in Slow and Fast Walking Hospitalized Patients. Rehabil. Res. Pract.
**2022**, 2022, 9230081. [Google Scholar] [CrossRef] [PubMed] - Portney, L.G. Concepts of Measurement. In Foundations of Clinical Research: Applications to Evidence-Based Practice; F.A. Davis: Philadelphia, PA, USA, 2020; pp. 106–177. [Google Scholar]
- Blair, S.; Duthie, G.; Robertson, S.; Hopkins, W.; Ball, K. Concurrent validation of an inertial measurement system to quantify kicking biomechanics in four football codes. J. Biomech.
**2018**, 73, 24–32. [Google Scholar] [CrossRef] [PubMed] - Khurelbaatar, T.; Kim, K.; Lee, S.; Kim, Y.H. Consistent accuracy in whole-body joint kinetics during gait using wearable inertial motion sensors and in-shoe pressure sensors. Gait Posture
**2015**, 42, 65–69. [Google Scholar] [CrossRef] - Zhang, J.-T.; Novak, A.; Brouwer, B.; Li, Q. Concurrent validation of Xsens MVN measurement of lower limb joint angular kinematics. Physiol. Meas.
**2013**, 34, N63. [Google Scholar] [PubMed] - Ferrari, A.; Cutti, A.G.; Garofalo, P.; Raggi, M.; Heijboer, M.; Cappello, A.; Davalli, A. First in vivo assessment of “Outwalk”: A novel protocol for clinical gait analysis based on inertial and magnetic sensors. Med. Biol. Eng. Comput.
**2010**, 48, 1–15. [Google Scholar] [CrossRef] - Robert-Lachaine, X.; Mecheri, H.; LaRue, C.; Plamondon, A. Validation of inertial measurement units with an optoelectronic system for whole-body motion analysis. Med. Biol. Eng. Comput.
**2016**, 55, 609–619. [Google Scholar] [CrossRef] - Al-Amri, M.; Nicholas, K.; Button, K.; Sparkes, V.; Sheeran, L.; Davies, J.L. Inertial measurement units for clinical movement analysis: Reliability and concurrent validity. Sensors
**2018**, 18, 719. [Google Scholar] [CrossRef] [Green Version] - Kohn, M.D.; Sassoon, A.A.; Fernando, N.D. Classifications in Brief: Kellgren-Lawrence Classification of Osteoarthritis. Clin. Orthop. Relat. Res.
**2016**, 474, 1886–1893. [Google Scholar] [CrossRef] [Green Version] - Roos, E.M.; Lohmander, L.S. Knee injury and Osteoarthritis Outcome Score (KOOS): From joint injury to osteoarthritis. Health Qual. Life Outcomes
**2003**, 1, 64. [Google Scholar] [CrossRef] [Green Version] - Creaby, M.W.; Bennellm, K.L.; Hunt, M.A. Gait Differs Between Unilateral and Bilateral Knee Osteoarthritis. Arch. Phys. Med. Rehabil.
**2012**, 93, 822–827. [Google Scholar] [CrossRef] - Winter, D.A. Biomechanics and Motor Control of Human Movement, 4th ed.; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2009; Chapter 3: Kinematics; pp. 70–73. ISBN 978-0-470-39818-0. [Google Scholar]
- Yang, C.-C.; Hsu, Y.-L.; Shih, K.-S.; Lu, J.-M. Real-Time Gait Cycle Parameter Recognition Using a Wearable Accelerometry System. Sensors
**2011**, 11, 7314. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Lin, L.-K. A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometrics
**1989**, 45, 255. [Google Scholar] [CrossRef] [PubMed] - Guo, Y.; Manatunga, A.K. Non-parametric estimation of the concordance correlation coefficient under univariate cen-soring. Biometrics
**2007**, 63, 164–172. [Google Scholar] [CrossRef] [PubMed] - Bland, J.M.; Altman, D.G. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet
**1986**, 327, 307–310. [Google Scholar] [CrossRef] - Bland, J.M.; Altman, D.G. Measuring agreement in method comparison studies. Stat. Methods Med. Res.
**1999**, 8, 135–160. [Google Scholar] [CrossRef] - Salvador, S.; Chan, P.-F. FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space. In KDD Workshop on Mining Temporal and Sequential Data; Citeseer: State College, PA, USA, 2004. [Google Scholar]
- Chen, S.; Ma, B.; Zhang, K. The normalized similarity metric and its applications. In Proceedings of the 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM, Fremont, CA, USA, 2–4 November 2007; pp. 172–177. [Google Scholar]
- Skejø, S.D.; Lund, M.E.; Stensvig, M.; Kaae, N.M.; Rasmussen, J. Running in circles: Describing running kinematics using Fourier series. J. Biomech.
**2021**, 115, 110187. [Google Scholar] [CrossRef] - Liang, J.; Duan, H.; Li, J.; Sun, H.; Sha, X.; Zhao, Y.; Liu, L. Accurate Estimation of Gait Altitude Using One Wearable IMU Sensor. In Proceedings of the 2018 IEEE 1st International Conference on Micro/Nano Sensors for AI, Healthcare, and Robotics (NSENS), Shenzhen, China, 5–7 December 2018; pp. 64–67. [Google Scholar] [CrossRef]
- Sung, J.; Han, S.; Park, H.; Cho, H.-M.; Hwang, S.; Park, J.W.; Youn, I. Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network. Sensors
**2022**, 22, 53. [Google Scholar] [CrossRef] - Antonsson, E.K.; Mann, R.W. The frequency content of gait. J. Biomech.
**1985**, 18, 39–47. [Google Scholar] [CrossRef] - Cappozzo, A. Low frequency self-generated vibration during ambulation in normal men. J. Biomech.
**1982**, 15, 599–609. [Google Scholar] [CrossRef] - Koo, T.K.; Li, M.Y. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J. Chiropr. Med.
**2016**, 15, 155. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Tirosh, O.; Orland, G.; Eliakim, A.; Nemet, D.; Steinberg, N. Repeatability of tibial acceleration measurements made on children during walking and running. J. Sci. Med. Sport
**2019**, 22, 91–95. [Google Scholar] [CrossRef] [PubMed] - Liikavainio, T.; Bragge, T.; Hakkarainen, M.; Jurvelin, J.S.; Karjalainen, P.A.; Arokoski, J.P. Reproducibility of Loading Measurements with Skin-Mounted Accelerometers During Walking. Arch. Phys. Med. Rehabil.
**2007**, 88, 907–915. [Google Scholar] [CrossRef] - Franco, P.S.; Moro, C.F.; Figueiredo, M.M.; Azevedo, R.R.; Ceccon, F.G.; Carpes, F.P. Within and between-days repeatability and variability of plantar pressure measurement during walking in children, adults and older adults. Adv. Rheumatol.
**2018**, 58, 15. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Na, A.; Buchanan, T.S. Validating Wearable Sensors Using Self-Reported Instability among Patients with Knee Osteo-arthritis. PM R
**2021**, 13, 119–127. [Google Scholar] [CrossRef] - Turcot, K.; Aissaoui, R.; Boivin, K.; Pelletier, M.; Hagemeister, N.; de Guise, J. New accelerometric method to discriminate between asymptomatic subjects and patients with medial knee osteoarthritis during 3-D gait. IEEE Trans. Biomed. Eng.
**2008**, 55, 1415–1422. [Google Scholar] [CrossRef] [PubMed] - Hussain, I.; Park, S.J. Prediction of Myoelectric Biomarkers in Post-Stroke Gait. Sensors
**2021**, 21, 5334. [Google Scholar] [CrossRef] - Zeng, X.; Ma, L.; Lin, Z.; Huang, W.-H.; Huang, Z.; Zhang, Y.; Mao, C. Relationship between Kellgren-Lawrence score and 3D kinematic gait analysis of patients with medial knee osteoarthritis using a new gait system. Sci. Rep.
**2017**, 7, 4080. [Google Scholar] [CrossRef] - Petersen, E.; Rytter, S.; Koppens, D.; Dalsgaard, J.; Hansen, T.; Larsen, N.; Andersen, M.; Stilling, M. Patients with knee osteoarthritis can be divided into subgroups based on tibiofemoral joint kinematics of gait–an exploratory and dynamic radiostereometric study. Osteoarthr. Cartil.
**2021**, 30, 249–259. [Google Scholar] [CrossRef] - Mills, K.; Hunt, M.A.; Ferber, R. Biomechanical deviations during level walking associated with knee osteoarthritis: A systematic review and meta-analysis. Arthritis Care Res.
**2013**, 65, 1643–1665. [Google Scholar] [CrossRef] [Green Version]

**Figure 1.**(

**A**) Simultaneous application of SENS and Xsens sensors (Xsens sensors are shown on top of clothing for illustrative purposes). (

**B**) Placement of a SENS sensor on the distal lateral side of the thigh. (

**C**) Coordinate axes of the SENS sensor.

**Figure 2.**Reconstruction of continuous SENS signal using the Fourier method. The gray dots show the accelerations recorded by the SENS sensors along the x-axis as an example of one gait cycle. The blue line shows the continuous signal reconstructed using the Fourier method.

**Figure 3.**An overview of the signal processing protocol used in this study: (

**A**) Simultaneous recording of the gait signals obtained with SENS and Xsens during two overground gait trials. (

**B**) Inspection of the PSD (power spectral density) of the signals and filtering of the signals after determining a cutoff frequency of 4 Hz using a fourth-order zero-lag low-pass Butterworth filter. (

**C**) Temporal matching of the signals using the cross-correlation method. (

**D**) Segmentation of the gait into five individual gait cycles. (

**E**) Averaging and normalization of the gait cycles into gait cycle percentages with respect to different coordinate axes and the magnitude vector.

**Figure 4.**(

**Left column**): scatterplots demonstrating the correlation between the SENS and Criterion accelerations with respect to different axes. The black line depicts the line of equality (SENS acc = Criterion acc). (

**Right column**): Bland–Altman plots demonstrating the agreement between the SENS and Criterion accelerations. The upper and lower LoAs (limits of agreement) are shown as the upper and lower dashed lines corresponding to the 2.5th and 97.5th percentiles of the differences. (Data from the right-side sensor are marked in blue, and those from the left-side sensor is marked in red).

**Figure 5.**(

**Left column**): scatterplots demonstrating the correlation between the frequencies of the peaks of the PSDs of SENS and Criterion with respect to different axes. The solid black line depicts the line of equality (SENS

_{Freq}== Criterion

_{Freq}). (

**Right column**): corresponding Bland–Altman plots demonstrating the agreement between the frequencies of the peaks of the PSDs of SENS and Criterion. The upper and lower LoAs are shown as the upper and lower dashed lines corresponding to the 2.5th and 97.5th percentiles of the differences. (Data from the right-side sensor are marked in blue, and those from the left-side sensor are marked in red).

**Figure 6.**(

**Left column**): scatterplots demonstrating the correlation between the powers of the peaks of the PSDs of SENS and Criterion with respect to different axes. The solid black line depicts the line of equality (SENS

_{power}== Criterion

_{power}). (

**Right column**): corresponding Bland–Altman plots demonstrating the agreement between the powers of the peaks of the PSDs of SENS and Criterion. The upper and lower LoAs are shown as the upper and lower dashed lines corresponding to the 2.5th and 97.5th percentiles of the differences. (Data from the right-side sensor are marked in blue, and those from the left-side sensor are marked in red).

**Figure 7.**(

**Left column**): scatterplots demonstrating the correlation between the Fourier coefficients of SENS and Criterion with respect to different axes. The solid black line depicts the line of equality (SENS coefficients == Criterion coefficients). (

**Right column**): corresponding Bland–Altman plots demonstrating the agreement between the Fourier coefficients of the peaks of the PSDs of SENS and Criterion. The upper and lower LoAs are shown as the upper and lower dashed lines corresponding to the 2.5th and 97.5th percentiles of the differences. (Data from the right-side sensor are marked in blue, and those from the left-side sensor are marked in red).

SENS | Xsens | |
---|---|---|

Dimension | 47 × 22 × 4.5 mm | 47 × 30 × 13 mm |

Weight | 7 gr | 16 gr |

Sampling frequency | 12.5 Hz | 60 Hz |

3D accelerometer | ±4 G | ±16 G |

Battery life | 15 weeks | 6 h |

Attachment | 3 M patches | Velcro straps |

Variable | Value |
---|---|

Sex (n (%)) | |

Female | 25 (57) |

Male | 19 (43) |

Median age (years) [range] | 65.6 [48.1–85.4] |

Height (cm) | 172.8 ± 8.7 |

Weight (kg) | 80.9 ± 14.3 |

BMI (kg/m^{2}) | 27.0 ± 3.7 |

Gait cadence (steps/min) | 110 ± 11 |

Painful knee (n (%)) | |

Left | 15 (34) |

Right | 17 (39) |

No | 12 (27) |

Severity of knee OA ^{1} (n (%)) | |

0 | 12 (27.3) |

I | 4 (9.1) |

II | 9 (20.5) |

III | 13 (29.5) |

IV | 6 (13.6) |

KOOS ^{2} Score | |

Pain | 62.5 [47.2–97.9] |

Symptoms | 67.9 [52.7–91.1] |

ADL ^{3} | 69.1 [55.5–97.4] |

Sport/Rec ^{4} | 32.5 [13.8–86.3] |

QOL ^{5} | 40.6 [31.3–89.1] |

^{1}Based on Kellgren–Lawrence classification.

^{2}Knee injury and Osteoarthritis Outcome Score.

^{3}Function in daily living.

^{4}Function in sports and recreation.

^{5}Knee-related quality of life.

**Table 3.**ICC values and 95% confidence intervals for test–retest reliability of the measured accelerations obtained with SENS and Criterion.

Variable | SENS | Criterion |
---|---|---|

acc_{x} | 0.94 [0.93–0.94] | 0.97 [096–0.97] |

acc_{y} | 0.93 [0.93–0.94] | 0.96 [0.95–0.96] |

acc_{z} | 0.96 [0.95–0.96] | 0.98 [0.98–0.98] |

n | 0.94 [0.93–0.94] | 0.97 [0.97–0.97] |

**Table 4.**Correlation and similarity-analysis results between SENS and Criterion signals in the time domain.

Variable | Side | CCC ^{1} | NSI ^{2} |
---|---|---|---|

acc_{x} | Right | 0.90 [0.87–0.92] | 0.94 [0.92–0.96] |

Left | 0.91 [0.88–0.93] | 0.94 [0.93–0.95] | |

acc_{y} | Right | 0.89 [0.88–0.90] | 0.96 [0.94–0.97] |

Left | 0.90 [0.89–0.92] | 0.96 [0.95–0.97] | |

acc_{z} | Right | 0.81 [0.80–0.83] | 0.84 [0.73–0.89] |

Left | 0.83 [0.82–0.84] | 0.80 [0.72–0.86] | |

n | Right | 0.90 [0.89–0.91] | 0.95 [0.93–0.96] |

Left | 0.90 [0.90–0.91] | 0.96 [0.94–0.97] |

^{1}Concordance correlation coefficient.

^{2}Normalized similarity index.

**Table 5.**Bland–Altman LoAs and biases for SENS measurements compared with Criterion (95% confidence intervals are provided in square brackets).

Variable | Side | Lower LoA ^{1} | Upper LoA ^{2} | Bias |
---|---|---|---|---|

acc_{x} | Right | −0.18 [−0.20–−0.17] | 0.22 [0.21–0.23] | −0.007 [−0.01–−0.003] |

Left | −0.17 [−0.18–−0.16] | 0.21 [0.19–0.23] | −0.004 [−0.007–−0.0006] | |

acc_{y} | Right | −0.34 [−0.37–−0.32] | 0.34 [0.31–0.37] | −0.008 [−0.01–−0.003] |

Left | −0.32 [−0.35–−0.30] | 0.32 [0.31–0.34] | 0.001 [−0.003–0.007] | |

acc_{z} | Right | −0.21 [−0.22–−0.19] | 0.24 [0.22–0.28] | −0.005 [−0.009–−0.002] |

Left | −0.21 [−0.22–−0.19] | 0.24 [0.23–0.27] | −wew0.005 [−0.006–0.004] | |

n | Right | −0.23 [−0.25–−0.22] | 0.21 [0.20–0.22] | 0.008 [0.004–0.012] |

Left | −0.23 [−0.24–−0.21] | 0.22 [0.20–0.23] | 0.007 [0.004–0.009] |

^{1}Lower limit of agreement.

^{2}Upper limit of agreement.

**Table 6.**Correlations of the frequencies and powers of the peaks of the PSDs and the Fourier coefficients between SENS and Criterion measured by concordance correlation coefficients (CCCs).

Variable | Axis | CCC ^{1} |
---|---|---|

Frequency of PSD peaks | x | 0.99 [0.99–0.99] |

y | 0.98 [0.97–0.98] | |

z | 0.99 [0.98–0.99] | |

n | 0.99 [0.99–0.99] | |

Power of PSD peaks | x | 0.91 [0.90–0.93] |

y | 0.87 [0.84–0.89] | |

z | 0.43 [0.34–0.52] | |

n | 0.87 [0.85–0.89] | |

Fourier coefficient | x | 0.98 [0.98–0.99] |

y | 0.92 [0.91–0.93] | |

z | 0.86 [0.85–0.87] | |

n | 0.99 [0.99–0.99] |

^{1}Concordance correlation coefficient.

**Table 7.**Correlation and agreement of the frequencies and powers of the PSD peaks and the Fourier coefficients between SENS and Criterion.

Axis | Lower LoA ^{1} | Upper LoA ^{2} | Bias | |
---|---|---|---|---|

Frequency of PSD peaks | x | 0.00 [−0.06–0.00] | 0.17 [0.00–0.35] | 0.00 [0.00–0.00] |

y | −0.13 [−0.59–0.00] | 0.61 [0.27–0.84] | 0.00 [0.00–0.00] | |

z | −0.23 [−0.46–−0.08] | 0.36 [0.00–0.70] | 0.00 [0.00–0.00] | |

n | 0.00 [−0.29–0.00] | 0.28 [0.17–0.35] | 0.00 [0.00–0.00] | |

Power of PSD peaks | x | −0.20 [−0.22–−0.17] | 0.25 [0.19–0.29] | 0.005 [−0.004–0.02] |

y | −0.19 [−0.23–−0.15] | 0.33 [0.22–0.38] | 0.008 [0.001–0.02] | |

z | −0.41 [−0.48–−0.31] | 0.45 [0.38–0.53] | 0.002 [−0.02–0.02] | |

n | −0.21 [−0.22–−0.20] | 0.34 [0.28–0.40] | −0.004 [−0.02–0.005] | |

Fourier coefficient | x | −0.05 [−0.06–−0.05] | 0.05 [0.05–0.06] | −0.002 [−0.003–−0.001] |

y | −0.08 [−0.09–−0.07] | 0.07 [0.06–0.08] | 0.0002 [−0.0007–0.001] | |

z | −0.06 [−0.06–−0.05] | 0.05 [0.04–0.06] | −0.0005 [−0.001–0.0005] | |

n | −0.06 [−0.06–−0.05] | 0.05 [0.04–0.05] | −0.001 [−0.003–−0.0003] |

^{1}Lower limit of agreement.

^{2}Upper limit of agreement.

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**MDPI and ACS Style**

Ghaffari, A.; Rahbek, O.; Lauritsen, R.E.K.; Kappel, A.; Kold, S.; Rasmussen, J.
Criterion Validity of Linear Accelerations Measured with Low-Sampling-Frequency Accelerometers during Overground Walking in Elderly Patients with Knee Osteoarthritis. *Sensors* **2022**, *22*, 5289.
https://doi.org/10.3390/s22145289

**AMA Style**

Ghaffari A, Rahbek O, Lauritsen REK, Kappel A, Kold S, Rasmussen J.
Criterion Validity of Linear Accelerations Measured with Low-Sampling-Frequency Accelerometers during Overground Walking in Elderly Patients with Knee Osteoarthritis. *Sensors*. 2022; 22(14):5289.
https://doi.org/10.3390/s22145289

**Chicago/Turabian Style**

Ghaffari, Arash, Ole Rahbek, Rikke Emilie Kildahl Lauritsen, Andreas Kappel, Søren Kold, and John Rasmussen.
2022. "Criterion Validity of Linear Accelerations Measured with Low-Sampling-Frequency Accelerometers during Overground Walking in Elderly Patients with Knee Osteoarthritis" *Sensors* 22, no. 14: 5289.
https://doi.org/10.3390/s22145289