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Capturing Representative Hand Use at Home Using Egocentric Video in Individuals with Upper Limb Impairment
Published on: December 23, 2020
Portable, open-source solutions for estimating wrist position during reaching in people with stroke
Jeffrey Z Nie1,2, James W Nie3,4, Na-Teng Hung3,5
1Southern Illinois University School of Medicine, Springfield, IL, 62794, USA. jnie31@siumed.edu.
New, affordable wearable sensors (inertial measurement units and virtual reality) accurately measure arm movement in stroke survivors. These methods offer a sensitive approach for assessing neurorehabilitation outcomes in clinical settings.
Area of Science:
- Neuroscience
- Rehabilitation Medicine
- Biomedical Engineering
Background:
- Traditional optical tracking systems for measuring arm movement kinematics in stroke survivors are often impractical due to environmental limitations, cost, and calibration difficulties.
- Arm movement kinematics offer a potentially more sensitive metric for assessing neurorehabilitation outcomes compared to existing methods.
Purpose of the Study:
- To present and validate two open-source methods using inertial measurement units (IMUs) and virtual reality (Vive) sensors for accurate arm movement kinematic measurements in individuals with stroke.
- To assess the ability of these methods to track key kinematic metrics like sweep area and smoothness.
Main Methods:
- Developed and implemented two open-source systems: one utilizing IMUs and another using Vive sensors.
- Assessed the accuracy of wrist position tracking relative to the shoulder during 3D reaching movements in stroke patients.
- Evaluated the tracking of sweep area and smoothness metrics in individuals with chronic stroke.
Main Results:
- Both IMU and Vive sensor methods demonstrated high accuracy in tracking wrist position during reaching movements, with low mean signed errors compared to optical tracking.
- Kinematic data estimated by both IMU and Vive methods showed high correlation (p < 0.01).
- The methods successfully tracked kinematic metrics such as sweep area and smoothness.
Conclusions:
- Open-source methods using inexpensive wearable sensors (IMUs and Vive) provide accurate and reliable measurements of arm kinematics in stroke survivors.
- These sensor-based approaches are suitable for both laboratory and clinical environments, facilitating the development of sensitive kinematic metrics for evaluating stroke rehabilitation.
- The proposed methods enhance the feasibility of using kinematic analysis for objective assessment of neurorehabilitation progress.
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