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Mohamad Parnianpour

Researcher at Sharif University of Technology

Publications -  283
Citations -  8606

Mohamad Parnianpour is an academic researcher from Sharif University of Technology. The author has contributed to research in topics: Trunk & Isometric exercise. The author has an hindex of 50, co-authored 274 publications receiving 7835 citations. Previous affiliations of Mohamad Parnianpour include New York University & Hong Kong Polytechnic University.

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Using a Motion Sensor to Categorize Nonspecific Low Back Pain Patients: A Machine Learning Approach.

TL;DR: A sensor-based machine learning model was developed to classify NSLBP patients into different subgroups according to quantitative kinematic data, i.e., trunk motion and balance-related measures, in conjunction with STarT output, and demonstrated that kinematics data could successfully be used to categorize patients into high vs. low-medium risk.
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Trajectory of human movement during sit to stand: a new modeling approach based on movement decomposition and multi-phase cost function

TL;DR: A computational model to describe the task of sit to stand (STS) and not only predicted the general features of STS with a sufficient accuracy, but also showed a potential flexibility to distinguish between the movement strategies from one subject to the other.
Journal Article

Three-dimensional spinal motion measurements. Part 1: A technique for examining posture and functional spinal motion.

TL;DR: There was no statistically significant evidence of organized coupling, but a relationship between rotation and lateral motion in the thoracic region was noted, and a statistically significant weak inverse relationship between age and range of motion between sagittal and frontal planes was found.
Journal Article

Reproducibility of trunk isoinertial performances in the sagittal, coronal, and transverse planes.

TL;DR: The purpose was to establish the reproducibility of the performance parameters in each plane at all resistance levels, and to identify those parameters which gave the most reliable information for objective assessment of the low back functional state.