SensorPositionComparison - Spatial Parameters#

Further Links:

A challenge for stride-by-stride comparison of spatial parameters on various lab tests.

General Information#

Dataset

The Sensor Position Comparison 2019 dataset [1] (usage example, download) contains 4x10m, 2x20m and a long walk (5 min) gait tests of 14 participants. The 4x10m and 2x20m tests are performed at 3 different speeds (slow, normal, fast). The dataset uses sensors at 6 different positions of each foot. However, for this challenge only the instep sensors are used.

Sensor System

The instep sensors are NilsPod IMU sensors (204.8 Hz). The two sensors are synchronized with each other and the motion capture system with sub-sample accuracy.

Reference System

A marker-based motion capture system by Qualisys (Opus 700+ Qualisys, 28 cameras, 20x30 m capture volume) at 100 Hz is used to track the foot position using 4 markers per foot (at the calcaneus (CAL), at the tip of the shoe (TOE), and on top of the first and the fifth metatarsal (MET1 and MET5). For this challenge the marker at the calcaneus is used to calculate stride length.

Implementation Recommendations#

The pipeline should use the raw data and the segmented stride list provided by the dataset. The start and end value of each stride in the provided segmented stride list follow the stride definitions by Barth et gal. [2]. This means the start and end-values are defined by the minimum in the gyr_ml axis right before the toe-off. The ground truth stride length is calculated from the marker position at the calcaneus (CAL) by calculating the distance traveled by this marker in the ground plane between the heel strike right before a stride and the heel strike within the stride. This means for the first stride in the segmented stride list and for each stride after a break, no ground truth stride length can be calculated. The pipeline should also not calculate parameters for these strides. To correctly handle these shifts in stride definition, you can use convert_segmented_stride_list or check the stride ids in the calculated ground truth parameters to remove strides without ground truth from your calculations. Further check the dataset example for more guidance on this. The final calculated parameters should match the provided stride list and should have the same stride ids. Note, that we assume that parameters are calculated for each stride. Missing strides are not handled by the evaluation.

The strides also include turning strides. Your method should be prepared to handle them.

References#

[1]

Küderle, Arne, Nils Roth, Jovana Zlatanovic, Markus Zrenner, Bjoern Eskofier, and Felix Kluge. “The Placement of Foot-Mounted IMU Sensors Does Affect the Accuracy of Spatial Parameters during Regular Walking.” PLOS ONE 17, no. 6 (June 9, 2022): e0269567. https://doi.org/10.1371/journal.pone.0269567.

[2]

Barth, Jens, Cäcilia Oberndorfer, Cristian Pasluosta, Samuel Schülein, Heiko Gassner, Samuel Reinfelder, Patrick Kugler, et al. “Stride Segmentation during Free Walk Movements Using Multi-Dimensional Subsequence Dynamic Time Warping on Inertial Sensor Data.” Sensors (Switzerland) 15, no. 3 (March 17, 2015): 6419-40. https://doi.org/10.3390/s150306419.