Abstract
As low cost and highly portable sensors, inertial measurements units (IMU) have become increas-ingly used in gait analysis, embodying an efficient alternative to motion capture systems. Mean-while, being able to compute reliably accurate spatial gait parameters using few sensors remains a relatively complex problematic. Providing a clinical oriented solution, our study presents a gy-rometer and accelerometer based algorithm for stride length estimation. Compared to most of the numerous existing works where only an averaged stride length is computed from several IMU, or where the use of the magnetometer is incompatible with everyday use, our challenge here has been to extract each individual stride length in an easy-to-use algorithm requiring only one inertial sensor attached to the subject shank. Our results were validated on healthy subjects and patients suffering from Parkinson’s disease (PD). Estimated stride lengths were compared to GAITRite© walkway system data: the mean error over all the strides was less than 6% for healthy group and 10.3% for PD group. This method provides a reliable portable solution for monitoring the in-stantaneous stride length and opens the way to promising applications.
Highlights
Tracking and analyzing a pathological movement is a recurrent need for clinical assessment
The algorithm presented in this study uses data from one 3D gyrometer and one 3D accelerometer embedded in an inertial measurement unit (IMU) strapped to the leg
For each subject and for each of the 4 inertial measurements units (IMU), we calculated the mean error and the standard deviation between considered IMU-based stride length estimation and corresponding stride length extracted from GAITRite (Table 1 and Table 2)
Summary
Tracking and analyzing a pathological movement is a recurrent need for clinical assessment. Due to a hampering lack of mobility in addition to a significant financial cost, this technology trends to be unusable in certain contexts, such as an embedded or outdoor use. In those cases, inertial measurement units embody an efficient alternative for movement analysis, such as in sport competition or gait disorders contexts [1] [2]. Preliminary work showed that monitoring the instantaneous stride length gave a crucial information to online detect or anticipate freezing of gait (FOG, an episodic inability to generate effective stepping) in patients suffering from Parkinson’s disease [4] [5]. Measuring the physical activity of those patients and quantifying FOG events could enable to differentiate “OFF” times, a state of decreased mobility associated to a low L-dopa level, and “ON” times, or periods when the medication is working and symptoms are controlled [6]
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