The analysis of the stability of human gait may be effectively performed when estimates of the base of support are available. The base of support area is defined by the relative position of the feet when they are in contact with the ground and it is closely related to additional parameters such as step length and stride width. These parameters may be determined in the laboratory using either a stereophotogrammetric system or an instrumented mat. Unfortunately, their estimation in the real world is still an unaccomplished goal. This study aims at proposing a novel, compact wearable system, including a magneto-inertial measurement unit and two time-of-flight proximity sensors, suitable for the estimation of the base of support parameters. The wearable system was tested and validated on thirteen healthy adults walking at three self-selected speeds (slow, comfortable, and fast). Results were compared with the concurrent stereophotogrammetric data, used as the gold standard. The root mean square errors for the step length, stride width and base of support area varied from slow to high speed between 10-46 mm, 14-18 mm, and 39-52 cm2, respectively. The mean overlap of the base of support area as obtained with the wearable system and with the stereophotogrammetric system ranged between 70% and 89%. Thus, this study suggested that the proposed wearable solution is a valid tool for the estimation of the base of support parameters out of the laboratory.


    Access

    Download


    Export, share and cite



    Title :

    Base of Support, Step Length and Stride Width Estimation during Walking Using an Inertial and Infrared Wearable System



    Publication date :

    2023-01-01



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




    Reliability of stride length estimation in self-pace and brisk walking with an inertial measurement unit on shank

    Kasai, Ritaro / Kodama, Takuma / Gu, Zixi et al. | British Library Conference Proceedings | 2017



    Gait stride length estimation using embedded machine learning

    Verbiest, Joeri R. / Bonnechere, Bruno / Saeys, Wim et al. | BASE | 2023

    Free access