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. 2023 Jun 10:21:100301.
doi: 10.1016/j.vas.2023.100301. eCollection 2023 Sep.

Extraction of canine gait characteristics using a mobile gait analysis system based on inertial measurement units

Affiliations

Extraction of canine gait characteristics using a mobile gait analysis system based on inertial measurement units

M Altermatt et al. Vet Anim Sci. .

Abstract

This study aims to investigate two simple algorithms for extracting gait features from an inertial measurement unit (IMU) based canine gait analysis system. The first algorithm was developed to determine the hip/shoulder extension/flexion range of motion. The second algorithm automatically determines the stance and swing phase per leg. To investigate the accuracy of the algorithms, two dogs were walked on a treadmill and measured simultaneously with an IMU system, an optical tracking system and two cameras. The range of motion estimation was compared to the optical tracking systems, with a total of 280 steps recorded. To test the stance and swing phase detection, a total of 63 steps were manually annotated in the video recordings and compared with the output of the algorithm. The IMU's-based estimation of the range of motion showed an average deviation of 1.4° to 5.6° from the optical reference, while the average deviation in the detection of the beginning and end of the stance and swing phases ranged from -0.01 to 0.09 s. This study shows that even simple algorithms can extract relevant information from inertial measurements that are comparable to results from more complex approaches. However, additional studies including a wider subject pool need to be conducted to investigate the significance of the presented findings.

Keywords: Canine gait; Gait characteristics; Inertial measurement unit; Movement analysis; Range of motion.

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Conflict of interest statement

Two co-authors are respectively co-founder and shareholder of 4DVets AG, the manufacturer of the discussed system. All authors worked together on a research project financed by Swiss Funding Agency Innosuisse.

Figures

Fig. 1
Fig. 1
Data acquisition and system synchronization: During the measurements, three separate systems were used simultaneously to acquire information. A synchronization movement enabled synchronization between the different measurement systems. As a result, the data set consists of the acceleration and angular velocity signals measured by the IMU system, the time-dependent positions of the Optical Tracking System (OTS) reflectors, and the manual annotation of the gait cycles of ten steps per leg and measurement using video cameras.
Fig. 2
Fig. 2
Left: sensor positions and coordinate systems; Right: Sensor fixation vest.
Fig. 3
Fig. 3
Left: Measurement set up: Dog with optical markers and IMUS on treadmill. The arrows indicate placement and orientation of the reference system for the OTS; Right: OTS marker on IMU sensor: The OTS marker coordinate system and IMU sensor coordinate system are identical.

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