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Capturing the Cranio-Caudal Signature of a Turn With Inertial Measurement Systems: Methods, Parameters Robustness and Reliability

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Capturing the Cranio-Caudal Signature of a Turn With Inertial Measurement Systems: Methods, Parameters Robustness and Reliability

Karina Lebel et al. Front Bioeng Biotechnol.

Abstract

Background: Turning is a challenging mobility task requiring coordination and postural stability. Optimal turning involves a cranio-caudal sequence (i.e., the head initiates the motion, followed by the trunk and the pelvis), which has been shown to be altered in patients with neurodegenerative diseases, such as Parkinson's disease as well as in fallers and frails. Previous studies have suggested that the cranio-caudal sequence exhibits a specific signature corresponding to the adopted turn strategy. Currently, the assessment of cranio-caudal sequence is limited to biomechanical labs which use camera-based systems; however, there is a growing trend to assess human kinematics with wearable sensors, such as attitude and heading reference systems (AHRS), which enable recording of raw inertial signals (acceleration and angular velocity) from which the orientation of the platform is estimated. In order to enhance the comprehension of complex processes, such as turning, signal modeling can be performed.

Aim: The current study investigates the use of a kinematic-based model, the sigma-lognormal model, to characterize the turn cranio-caudal signature as assessed with AHRS.

Methods: Sixteen asymptomatic adults (mean age = 69.1 ± 7.5 years old) performed repeated 10-m Timed-Up-and-Go (TUG) with 180° turns, at varying speed. Head and trunk kinematics were assessed with AHRS positioned on each segments. Relative orientation of the head to the trunk was then computed for each trial and relative angular velocity profile was derived for the turn phase. Peak relative angle (variable) and relative velocity profiles modeled using a sigma-lognormal approach (variables: Neuromuscular command amplitudes and timing parameters) were used to extract and characterize the cranio-caudal signature of each individual during the turn phase.

Results: The methodology has shown good ability to reconstruct the cranio-caudal signature (signal-to-noise median of 17.7). All variables were robust to speed variations (p > 0.124). Peak relative angle and commanded amplitudes demonstrated moderate to strong reliability (ICC between 0.640 and 0.808).

Conclusion: The cranio-caudal signature assessed with the sigma-lognormal model appears to be a promising avenue to assess the efficiency of turns.

Keywords: IMU; attitude and heading reference system; deficit; inertial motion capture; signature; turn.

Figures

Figure 1
Figure 1
Setup, protocol, and methodology. (A) Spatial schematic of a 10-m Timed-Up-and-Go (TUG) task. Participants initiate the task sitting on a chair. Upon signal, the participant stands-up, walk out for 10 m, turn around when the 10-m mark is reached, walks back toward the chair and sits down. The turn portion of the TUG is targeted for the present study. (B) Participants are equipped with a suit comprised of sensors which position are illustrated this diagram. Signals from the double-marked sensors (head and trunk) were used for the signature recognition. (C) Sensors used are composed of 3-axis accelerometer, gyroscope, and magnetometer to measure linear acceleration, angular velocity, and magnetic field. All of the data are passed on to the fusion algorithm embedded in the sensor estimate the orientation of the module, expressed in an Inertial reference frame. (D) Global workflow of the algorithm to recognize the cranio-caudal signature of a turn.
Figure 2
Figure 2
Sigma-log normal model conceptual framework. (A) Upon initiation of a turn, a first command is sent to the neuromuscular system (NMS) to initiate the head motion. A second command is sent to initiates the movement of the trunk. The difference of the NMS impulse responses generates the head to trunk velocity profile corresponding to the cranio-caudal signature. (B) The NMS impulse response is characterized by an asymmetric bell-shaped curved which can be characterized by the delay between command initiation and the median of the velocity as well as the response time. (C) Parameters of the sigma-lognormal profile can be estimated through the localization of specific points on the curve. (D) The sigma-lognormal model estimates the parameters of the two lognormal signal phases from which the velocity profile is estimated.
Figure 3
Figure 3
Cranio-Caudal Signature Determination. The proposed cranio-caudal signature approach is composed of both the analysis of the relative head to trunk angle achieved during the turn and the head to trunk relative angular velocity profile, modeled with the sigma-lognormal approach. (A) Change in head to trunk relative angle during a normal turn. The maximum angle reached is identified as a signature variable. (B) The blue curve illustrates the relative head to trunk angular velocity profile during the turn, as derived from the attitude and heading reference system measurement. The red dotted line illustrates the reconstructed profile, using the sigma-lognormal model. The parameters used to achieve the reconstruction are listed as inserts.
Figure 4
Figure 4
Turn cranio-caudal signature for a normal pace (A,C) and a fast pace Timed-Up-and-Go (TUG) (B,D), executed by the same healthy participant. (A,B) Relative head to trunk angle variation captured during the turns. (C,D) Measured and estimated relative head to trunk angular velocity profile captured during the turns along with the computed signature parameters.
Figure 5
Figure 5
Turn signature metric dispersion per trial velocity.
Figure 6
Figure 6
Tradition turn metric dispersion per trial velocity.

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References

    1. Akram S. B., Frank J. S., Fraser J. (2010). Effect of walking velocity on segment coordination during pre-planned turns in healthy older adults. Gait Posture 32, 211–214.10.1016/j.gaitpost.2010.04.017 - DOI - PubMed
    1. Anderson J., Wagner J., Bessesen M., Williams L. C. (2012). Usability testing in the hospital. Hum. Fac. Ergon. Manuf. Serv. Indus. 22, 52–63.10.1002/hfm.20277 - DOI
    1. Ayachi F., Nguyen H., Goubault E., Boissy P., Duval C. (2016a). The use of empirical mode decomposition-based algorithm and inertial measurement units to auto-detect daily living activities of healthy adults. IEEE Trans. Neural Syst. Rehabil. Eng. 24, 1060–1070.10.1109/TNSRE.2016.2519413 - DOI - PubMed
    1. Ayachi F. S., Nguyen H. P., Lavigne-Pelletier C., Goubault E., Boissy P., Duval C. (2016b). Wavelet-based algorithm for auto-detection of daily living activities of older adults captured by multiple inertial measurement units (IMUs). Physiol. Meas. 37, 442.10.1088/0967-3334/37/3/442 - DOI - PubMed
    1. Carbonneau E., Fontaine R., Smeesters C. (2013). “A practical approach to determine appropriate cutoff frequencies for motion analysis data,” in 37th Annual Meeting of the American Society of Biomechanics, Omaha, NE.

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