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Research Papers

Assessment of Infant Movement With a Compact Wireless Accelerometer System

[+] Author and Article Information
D. Gravem

M. Singh1

 Department of Pediatrics and Institute for Clinical and Translational Science, University of California, Irvine, 101 The City Drive South, Building 25, 2nd floor Orange, Irvine, CA 92868

C. Chen, J. Rich, J. Vaughan, F. Waffarn, P. Chou, D. Cooper

 Department of Pediatrics and Institute for Clinical and Translational Science, University of California, Irvine, 101 The City Drive South, Building 25, 2nd floor Orange, Irvine, CA 92868

K. Goldberg

 Danbury Hospital, 24 Hospital Avenue, Danbury, CT 06810

D. Reinkensmeyer

 Department of Mechanical and Aerospace Engineering, Henry Samuelli School of Engineering, University of California, Irvine, 5200 Engineering Hall, Irvine, CA 92697

D. Patterson

 Department of Informatics, Donald Bren School of Information and Computer Sciences, 5084 Donald Bren Hall, Irvine, CA 92697

1

D. Gravem and M. Singh contributed equally.

J. Med. Devices 6(2), 021013 (May 14, 2012) (7 pages) doi:10.1115/1.4006129 History: Received April 27, 2011; Revised July 23, 2011; Published May 14, 2012; Online May 14, 2012

There is emerging data that patterns of motor activity early in neonatal life can predict impairments in neuromotor development. However, current techniques to monitor infant movement mainly rely on observer scoring, a technique limited by skill, fatigue, and inter-rater reliability. Consequently, we tested the use of a lightweight, wireless, accelerometer system that measures movement and can be worn by premature babies without interfering with routine care. We hypothesized that this system would be useful in assessing motor activity, in identifying abnormal movement, and in reducing the amount of video that a clinician would need to review for abnormal movements. Ten preterm infants in the NICU were monitored for 1 h using both the accelerometer system and video. A physical therapist trained to recognize cramped-synchronized general movements scored all of the video data by labeling each abnormal movement observed. The parameters of three different computer models were then optimized based on correlating features computed from accelerometer data and the observer’s annotations. The annotations were compared to the model’s prediction on unseen data. The trained observer identified cramped-synchronized general movements in 6 of the 10 infants. The computer models attained between 70% and 90% accuracy when predicting the same observer label for each data point. Our study suggests that mini-accelerometers may prove useful as a clinical tool assessing patterns of movement in preterm infants.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

Dataflow diagram

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Figure 2

Approximately 10 min of Giganti activity coding. Corrected accelerometer readings for the four limbs of one baby are shown in the top four graphs. The nurse’s annotation of the data from sample 50,000 to 60,000 is shown in the bottom graph.

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Figure 3

Average corrected acceleration magnitude over 1 h

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Figure 4

Pearson correlation for upper and lower extremities for each baby

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Figure 5

Scatter plot comparing the maximum corrected acceleration for arms versus legs for each baby

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Figure 6

Scatter plot comparing the average corrected acceleration for arms versus legs for each baby

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