Inertial BSN-Based Characterization and Automatic UPDRS Evaluation of the Gait Task of Parkinsonians

@article{Parisi2016InertialBC,
  title={Inertial BSN-Based Characterization and Automatic UPDRS Evaluation of the Gait Task of Parkinsonians},
  author={Federico Parisi and Gianluigi Ferrari and Matteo Giuberti and Laura Contin and Veronica Cimolin and Corrado Azzaro and Giovanni Albani and Alessandro Mauro},
  journal={IEEE Transactions on Affective Computing},
  year={2016},
  volume={7},
  pages={258-271}
}
The analysis and assessment of motor tasks, such as gait, can provide important information on the progress of neurological disorders such as Parkinson's disease (PD). In this paper, we design a Boby Sensor Network (BSN)-based system for the characterization of gait in Parkinsonians through the extraction of kinematic features, in both time and frequency domains, embedding information on the status of the PD. The gait features extraction is performed on a set of 34 PD patients using a BSN… 

On the correlation between UPDRS scoring in the leg agility, sit-to-stand, and gait tasks for parkinsonians

  • F. ParisiG. Ferrari A. Mauro
  • Computer Science
    2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
  • 2015
A comparative investigation of the LA, S2S, and G tasks is carried out, focusing on the correlation between UPDRS values assigned to the three tasks by both an expert neurologist and the automatic system.

Body-Sensor-Network-Based Kinematic Characterization and Comparative Outlook of UPDRS Scoring in Leg Agility, Sit-to-Stand, and Gait Tasks in Parkinson's Disease

The results, based on a limited number of subjects with Parkinson's disease (PD), show poor-to-moderate correlations between the UPDRS scores of different tasks, highlighting that the patients' motor performance may vary significantly from one task to another, since different tasks relate to different aspects of the disease.

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Identification of Gait Events in Healthy and Parkinson’s Disease Subjects Using Inertial Sensors: A Supervised Learning Approach

The proposed algorithm that used linear classifiers to detect in real-time the transition between consecutive gait phases achieved similar performance as the threshold-based scheme, with the advantage of not relying on any prior knowledge of specific features for any particular inertial signal.

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Gait features are compared with scores assigned by neurologists within the Unified Parkinson's Disease Rating Scale (UPDRS), with the ultimate goal of automatically determining the UPDRS score of the Gait Task (GT) carried out by Parkinsonians.

Automatic UPDRS Evaluation in the Sit-to-Stand Task of Parkinsonians: Kinematic Analysis and Comparative Outlook on the Leg Agility Task

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Body-Sensor-Network-Based Kinematic Characterization and Comparative Outlook of UPDRS Scoring in Leg Agility, Sit-to-Stand, and Gait Tasks in Parkinson's Disease

The results, based on a limited number of subjects with Parkinson's disease (PD), show poor-to-moderate correlations between the UPDRS scores of different tasks, highlighting that the patients' motor performance may vary significantly from one task to another, since different tasks relate to different aspects of the disease.

Assigning UPDRS Scores in the Leg Agility Task of Parkinsonians: Can It Be Done Through BSN-Based Kinematic Variables?

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