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Sensors 2019, 19(2), 223; https://doi.org/10.3390/s19020223

Wearable Sensor Based Stooped Posture Estimation in Simulated Parkinson’s Disease Gaits

1
Impedance Imaging Research Center (IIRC), Kyung Hee University, Seoul 02447, Korea
2
Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul 03080, Korea
3
Electrical and Electronic Engineering department, The University of Danang—University of Technology and Education, Danang 550000, Vietnam
4
School of Electrical Engineering, University of Ulsan, Ulsan 44610, Korea
*
Author to whom correspondence should be addressed.
Received: 6 December 2018 / Revised: 3 January 2019 / Accepted: 5 January 2019 / Published: 9 January 2019
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
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Abstract

Stooping is a posture which is described as an involuntary forward bending of the thoracolumbar spine. Conventionally, the stooped posture (SP) in Parkinson’s disease patients is measured in static or limited movement conditions using a radiological or optoelectronic system. In the dynamic condition with long movement distance, there was no effective method in preference to the empirical assessment from doctors. In this research, we proposed a practical method for estimating the SP with a high accuracy where accelerometers can be mounted on the neck or upper back as a wearable sensor. The experiments with simulated subjects showed a high correlation of 0.96 and 0.99 between the estimated SP angle and the reference angles for neck and back sensor position, respectively. The maximum absolute error (0.9 and 1.5 degrees) indicated that the system can be used, not only in clinical assessment as a measurement, but also in daily life as a corrector. View Full-Text
Keywords: stooped posture; Parkinson’s disease; accelerometer stooped posture; Parkinson’s disease; accelerometer
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Dang, Q.K.; Seo, H.G.; Pham, D.D.; Chee, Y. Wearable Sensor Based Stooped Posture Estimation in Simulated Parkinson’s Disease Gaits. Sensors 2019, 19, 223.

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