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

Karina Lebel, Hung Nguyen, Christian Duval, Réjean Plamondon and Patrick Boissy

Article (2017)

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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 degrees 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.

Uncontrolled Keywords

Imu; attitude and heading reference system; deficit; inertial motion capture; signature; turn

Subjects: 2500 Electrical and electronic engineering > 2500 Electrical and electronic engineering
2600 Robotics > 2605 Pattern analysis and machine intelligence
Department: Department of Electrical Engineering
Research Center: Other
Funders: Ecological Mobility in Aging and Parkinson (EMAP), Canadian Institute of Health Research (CIHR) team in Mobility in Aging grant, Fonds de recherche du Québec — Santé (FRQS), Research Centre on Aging
PolyPublie URL: https://publications.polymtl.ca/3593/
Journal Title: Frontiers in Bioengineering and Biotechnology (vol. 5)
Publisher: Frontiers
DOI: 10.3389/fbioe.2017.00051
Official URL: https://doi.org/10.3389/fbioe.2017.00051
Date Deposited: 02 Mar 2020 12:14
Last Modified: 15 May 2023 01:20
Cite in APA 7: Lebel, K., Nguyen, H., Duval, C., Plamondon, R., & Boissy, P. (2017). Capturing the Cranio-Caudal Signature of a Turn with Inertial Measurement Systems: Methods, Parameters Robustness and Reliability. Frontiers in Bioengineering and Biotechnology, 5. https://doi.org/10.3389/fbioe.2017.00051


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