Three-dimensional continuous gait trajectory estimation using single Shank-Worn inertial measurement units and clinical walk test application

Abstract

State-of-the-art estimation methods using inertial measurement units (IMUs) for global continuous gait path and local stepwise gait trajectory during walking have been developed. However, estimation methods for continuous gait trajectory integrating both these aspects with high accuracy are almost lacking. Thus, continuous gait trajectory estimation using a single shank-worn IMU with high accuracy is proposed in this study. This method calculates three-dimensional local stepwise gait trajectory based on IMU measurement data extracted between adjacent middle points of stance phases during walking. Continuous gait trajectory is estimated by concatenating adjacent local stepwise gait trajectories based on relative angles determined according to stride vectors and shank orientations. Evaluation experiments results obtained using the optical motion capture system with 12 healthy participants demonstrated estimation errors in the stride length (− 0.027 (− 0.054 to − 0.006) m) and turning angle (0.7 (− 0.2–1.7)°), and normalized endpoint position error (0.029 (0.019–0.04) m). Comparing with previous reports, the proposed method integrally achieves a continuous gait trajectory with a low estimation error level in both local and global aspects despite the continuous measurement of multiple gait cycles. The proposed simple and low-cost method can be applied in the medical field and contribute to expansion of the application of precise gait information in daily life.

Robust spatial self-organization in crowds of asynchronous pedestrians

“…The X , Y and Z axes of the IMUs were set to the craniocaudal, anterior–posterior and medial–lateral directions, respectively. We used the timing of the heel strike to evaluate footstep synchronization and used an angular velocity in the medial–lateral direction to estimate it, as proposed in [42,43]. To do this, we first synchronized the timestamps between all IMUs and the overhead video camera as described in the previous section.…”

“…A median filter with a window length of 5 was then applied to the time series of the IMU data. Then, the timings of the heel strikes were extracted by searching for the local maximum sandwiched between the toe off and heel strike (electronic supplementary material, figure S9) (for details, see [42,43]). Local maxima were detected using the SciPy signal.find_peaks function (version 1.9.1).…”

Inertial sensors for gait monitoring and design of adaptive controllers for exoskeletons after stroke: a feasibility study

“…For the MSw, we identified the local minima in the angular velocity of the same leg with a difference in the angle between both shanks below an angle threshold. The foot was assumed to be fully flat on the ground (FF) when the MSw event of the contralateral leg occurred, as described by Uchitomi et al (2022 ). Note that FF events are commonly detected by using the magnitude of the acceleration vector measured by the IMUs ( Pierleoni et al, 2019 ), although it has been shown that angular velocity–based algorithms perform significantly better than acceleration-based algorithms, especially for pathological gait ( Arens et al, 2021 ; Laidig et al, 2021 ; Uchitomi et al, 2022 ).…”

Enhancing Wearable Gait Monitoring Systems: Identifying Optimal Kinematic Inputs in Typical Adolescents

“…Previous research has shown that combined gyroscope and accelerometer data gives higher accuracy in gait evaluation than accelerometer data alone [20]. Shank angular velocity and acceleration were successfully utilized for the prediction of freezing of gait in patients with Parkinson's disease [55], measuring spatiotemporal parameters in typical individuals, stepwise trajectory estimation in young adults [56], monitoring gait normalcy in adults [57], gait event detection in healthy adults and elderly [27,58], real-time gait event detection (swing and stance period initiation) in healthy adults and individuals with spinal cord injury [59,60], and gait phase detection in healthy adults [61] and children with cerebral palsy [9]. Some of these studies used the ML axis for AV [55,59], whereas others used all three axes [56,57].…”