Design and Calibration of a Passively Actuated Wearable Device for Sagittal Spine Posture Measurement

This paper introduces an enhanced design for a wearable spine measurement device and its associated calibration method. The proposed design replaces the torque motor used in the previous version with a passive actuation mechanism, which drives a series of linkages to adapt to the shape of the spine. Despite a longer setup time compared to the active version, the passive design reduces the weight by 45.5%, lowers power requirements by 80%, and mitigates both wearing discomfort and potential electrical hazards, making it more suitable for long-term measurement. Since the device is worn externally, the measured spine posture is susceptible to errors due to the uneven thickness of back muscles and spinous processes. The calibration method employs second-order regression models to establish a mapping between posture data collected from the device and X-ray images. This mapping yields personalized parameters, enabling the calibrated models to predict the actual spine posture based on external measurements for each individual. Validation results from 16 subjects demonstrate that the proposed calibration method significantly reduces errors in estimating key sagittal spine parameters. Specifically, after calibration, the root-mean-square errors for estimating thoracic kyphosis (TK), lumbar lordosis (LL), and sagittal vertical axis (SVA) are reduced to 3.3°, 3.5°, and 2.9 mm, representing reductions of 45%, 75%, and 89%, respectively, compared to the values before calibration.

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Publication Details

Journal
IEEE Transactions on Instrumentation and Measurement
Published
2025-01-01
DOI
https://doi.org/10.1109/tim.2025.3602573
Primary Topic
Ergonomics and Musculoskeletal Disorders
Type
article
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article

Design and Calibration of a Passively Actuated Wearable Device for Sagittal Spine Posture Measurement

Hsuan‐Yu Chen, Ming‐Chang Hsu, T.‐J. Yeh
IEEE Transactions on Instrumentation and Measurement
Ergonomics and Musculoskeletal Disorders
article

Design and Calibration of a Passively Actuated Wearable Device for Sagittal Spine Posture Measurement

Hsuan‐Yu Chen, Ming‐Chang Hsu, T.‐J. Yeh
article en

Abstract

This paper introduces an enhanced design for a wearable spine measurement device and its associated calibration method. The proposed design replaces the torque motor used in the previous version with a passive actuation mechanism, which drives a series of linkages to adapt to the shape of the spine. Despite a longer setup time compared to the active version, the passive design reduces the weight by 45.5%, lowers power requirements by 80%, and mitigates both wearing discomfort and potential electrical hazards, making it more suitable for long-term measurement. Since the device is worn externally, the measured spine posture is susceptible to errors due to the uneven thickness of back muscles and spinous processes. The calibration method employs second-order regression models to establish a mapping between posture data collected from the device and X-ray images. This mapping yields personalized parameters, enabling the calibrated models to predict the actual spine posture based on external measurements for each individual. Validation results from 16 subjects demonstrate that the proposed calibration method significantly reduces errors in estimating key sagittal spine parameters. Specifically, after calibration, the root-mean-square errors for estimating thoracic kyphosis (TK), lumbar lordosis (LL), and sagittal vertical axis (SVA) are reduced to 3.3°, 3.5°, and 2.9 mm, representing reductions of 45%, 75%, and 89%, respectively, compared to the values before calibration.

IEEE Transactions on Instrumentation and MeasurementVol. 74
National Taiwan University (TW), National Tsing Hua University (TW)
Openalex Percentile: Top 74%
Ergonomics and Musculoskeletal Disorders
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Design and Calibration of a Passively Actuated Wearable Device for Sagittal Spine Posture Measurement — Hsuan‐Yu Chen, Ming‐Chang Hsu, et al. · IEEE Transactions on Instrumentation and Measurement (2025) | TGRS Research Map | TGRS