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. 2021 Sep 15;21(18):6179.
doi: 10.3390/s21186179.

The Accuracy and Precision of Gait Spatio-Temporal Parameters Extracted from an Instrumented Sock during Treadmill and Overground Walking in Healthy Subjects and Patients with a Foot Impairment Secondary to Psoriatic Arthritis

Roua Walha  1 , Karina Lebel  2   3 , Nathaly Gaudreault  1 , Pierre Dagenais  1 , Andrea Cereatti  4 , Ugo Della Croce  5   6 , Patrick Boissy  1   2
Affiliations

The Accuracy and Precision of Gait Spatio-Temporal Parameters Extracted from an Instrumented Sock during Treadmill and Overground Walking in Healthy Subjects and Patients with a Foot Impairment Secondary to Psoriatic Arthritis

Roua Walha et al. Sensors (Basel). .

Abstract

The objectives of this study were to assess the accuracy and precision of a system combining an IMU-instrumented sock and a validated algorithm for the estimation of the spatio-temporal parameters of gait. A total of 25 healthy participants (HP) and 21 patients with foot impairments secondary to psoriatic arthritis (PsA) performed treadmill walking at three different speeds and overground walking at a comfortable speed. HP performed the assessment over two sessions. The proposed system's estimations of cadence (CAD), gait cycle duration (GCD), gait speed (GS), and stride length (SL) obtained for treadmill walking were validated versus those estimated with a motion capture system. The system was also compared with a well-established multi-IMU-based system for treadmill and overground walking. The results showed a good agreement between the motion capture system and the IMU-instrumented sock in estimating the spatio-temporal parameters during the treadmill walking at normal and fast speeds for both HP and PsA participants. The accuracy of GS and SL obtained from the IMU-instrumented sock was better compared to the established multi-IMU-based system in both groups. The precision (inter-session reliability) of the gait parameter estimations obtained from the IMU-instrumented sock was good to excellent for overground walking and treadmill walking at fast speeds, but moderate-to-good for slow and normal treadmill walking. The proposed IMU-instrumented sock offers a novel form factor addressing the wearability issues of IMUs and could potentially be used to measure spatio-temporal parameters under clinical conditions and free-living conditions.

Keywords: IMUs; free-living measures; gait parameters; wearable systems.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Conditions and measurement systems used for data acquisition. (a) Participants performed three 2 min treadmill walking trials at slow, normal, and fast walking speeds and three 10 m overground walking trials at a self-selected comfortable speed. (b) A motion capture system was used as the gold standard to validate the estimations of the spatio-temporal parameters for treadmill walking based on the IMU-instrumented sock and the Mobility Lab system.
Figure 2
Figure 2
Concurrent validity. Bland–Altman plots of the differences between (a) the TEADRIP algorithm applied to the IMU-instrumented sock recordings and the Mocap system, and between (b) the Mobility Lab system and the Mocap system during two-minute treadmill walking at different speeds in HP (n = 25, 2 sessions). Cadence (CAD), gait cycle duration (GCD), gait speed (GS), stride length (SL), and Motion capture system (Mocap). The solid lines indicate the mean test-retest differences (bias) and the dashed lines indicate the upper and lower 95% limits of agreement (1.96 SD of the bias). Dashed green, red, and blue squares represent the observations for slow, normal, and fast speeds, respectively.
Figure 3
Figure 3
Concurrent validity. Bland–Altman plots of the differences between (a) the TEADRIP algorithm applied to the IMU-instrumented sock recordings and the Mocap system, and between (b) the Mobility Lab system and the Mocap system during two-minute treadmill walking at different speeds in PsA patients (n = 21, one session). Cadence (CAD), gait cycle duration (GCD), gait speed (GS), stride length (SL), and Motion capture system (Mocap). The solid lines indicate the mean test-retest differences (bias) and the dashed lines indicate the upper and lower 95% limits of agreement (1.96 SD of the bias). Dashed green, red, and blue squares represent the observations for slow, normal, and fast speeds, respectively.
Figure 4
Figure 4
Accuracy comparison between the TEADRIP algorithm applied to the IMU-instrumented sock recordings and the Mobility Lab system estimations of the spatio-temporal parameters in (a) healthy and (b) PsA participants. Mean absolute errors and relative errors (%) in CAD, GCD, GS, and SL estimations obtained from the IMU-instrumented sock and the Mobility Lab system during 2 min treadmill walking at different speeds. Cadence (CAD), gait cycle duration (GCD), gait speed (GS), stride length (SL), Psoriasic arthritis (PsA). *: p < 0.05.
Figure 5
Figure 5
Agreement between the TEADRIP applied to the IMU-instrumented sock recordings and the Mobility Lab system. Bland–Altman plots of the differences between the IMU-instrumented sock and the Mobility Lab system in the spatio-temporal estimations measured for overground walking in (a) healthy and (b) PsA participants. The solid lines indicate the mean test-retest differences (bias) and the dashed lines indicate the upper and lower 95% limits of agreement (1.96 SD of the bias).
Figure 6
Figure 6
Agreement and Bland–Altman plots of the differences between the TEADRIP algorithm applied to the IMU-instrumented sock recordings and the stopwatch, and between the Mobility Lab system and the stopwatch in GS estimations for overground walking in (a) healthy and (b) participants.
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