Fusing uncalibrated IMUs and handheld smartphone video to reconstruct knee kinematics

• URL: http://arxiv.org/abs/2405.17368v1
• Date: Mon, 27 May 2024 17:23:16 GMT
• Title: Fusing uncalibrated IMUs and handheld smartphone video to reconstruct knee kinematics
• Authors: J. D. Peiffer, Kunal Shah, Shawana Anarwala, Kayan Abdou, R. James Cotton,
• Abstract summary: We present a method to combine handheld smartphone video and uncalibrated wearable sensor data at their full temporal resolution. We validate this in a mixture of people with no gait impairments, lower limb prosthesis users, and individuals with a history of stroke.
• Score: 1.5728609542259502
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Video and wearable sensor data provide complementary information about human movement. Video provides a holistic understanding of the entire body in the world while wearable sensors provide high-resolution measurements of specific body segments. A robust method to fuse these modalities and obtain biomechanically accurate kinematics would have substantial utility for clinical assessment and monitoring. While multiple video-sensor fusion methods exist, most assume that a time-intensive, and often brittle, sensor-body calibration process has already been performed. In this work, we present a method to combine handheld smartphone video and uncalibrated wearable sensor data at their full temporal resolution. Our monocular, video-only, biomechanical reconstruction already performs well, with only several degrees of error at the knee during walking compared to markerless motion capture. Reconstructing from a fusion of video and wearable sensor data further reduces this error. We validate this in a mixture of people with no gait impairments, lower limb prosthesis users, and individuals with a history of stroke. We also show that sensor data allows tracking through periods of visual occlusion.

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