Exploring Motion Boundaries in an End-to-End Network for Vision-based Parkinson's Severity Assessment

Dadashzadeh, Amirhossein; Whone, Alan; Rolinski, Michal; Mirmehdi, Majid

doi:10.48550/arxiv.2012.09890

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…The highest classification accuracy of 81.2% was achieved using an SVM with an RBF kernel. Dadashzadeh et al [36] proposed an end-to-end DL-based framework for assessing the severity of PD symptoms based on the patient's gait and ability to perform hand-movement tasks. To evaluate the performance of their proposed system, they collected a data set of 1058 videos from 25 PD patients.…”

Section: Related Workmentioning

confidence: 99%

Classification of Hand-Movement Disabilities in Parkinson’s Disease Using a Motion-Capture Device and Machine Learning

Shin,

Matsumoto,

Maniruzzaman

et al. 2024

IEEE Access

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P ARKINSON'S disease (PD) is a neurological disorder caused by degeneration of dopaminergic neurons in the midbrain. PD patients mainly suffer from motor symptoms, which significantly impact their daily lives. The diagnostic criteria for PD include the presence of muscle rigidity, tremor, and postural reflex disturbances. The Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the standard tool for evaluating PD symptoms, part III of which is dedicated to motor symptoms. That part involves a comprehensive set of specific physical examinations, and physicians assign semi quantitative scores from 0 to 4. However, this approach faces notable challenges, including the requirement for movement-disorder experts proficient in using MDS-UPDRS and the presence of substantial inter rater variability even among experts. Overcoming these challenges requires a quantitative and objective

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Section: Related Workmentioning

confidence: 99%

Classification of Hand-Movement Disabilities in Parkinson’s Disease Using a Motion-Capture Device and Machine Learning

Shin,

Matsumoto,

Maniruzzaman

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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“…In a similar context, Liu et al [40], Lu et al [41], Dadashzadeh et al [42], Mehta et al [43], and Rupprechter et al [44], use other deep learning techniques for pose estimation in order to study upper and lower limb movements. They detect abnormalities in posture and gait and evaluate patients' bradykinesia according to UPDRS.…”

Section: Related Workmentioning

confidence: 99%

Learning Spatio-Temporal Radon Footprints for Assessment of Parkinson’s Dyskinesia

Theofilou,

Tsatiris,

Kollias

2024

Electronics

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Parkinson’s disease is a severe neurodegenerative disorder that leads to loss of control over various motor and mental functions. Its progression can be limited with medication, particularly through the use of levodopa. However, prolonged administration of levodopa often results in disorders independent of those caused by the disease. The detection of these disorders is based on the clinical examination of patients, through different type of activities and tasks, using the Unified Dyskinesia Rating Scale (UDysRS). In the present work, our aim is to develop a state-of-the-art assessment system for levodopa-induced dyskinesia (LID) using the joint coordinate data of a human skeleton body depicted on videotaped activities related to UDysRS. For this reason, we combine a robust mathematical method for encoding action sequences known as Spatio-temporal Radon Footprints (SRF) with a Convolutional Neural Network (CNN), in order to estimate dyskinesia’s ratings for six body parts. We introduce two different methodological approaches: Global SRF-CNN and Local SRF-CNN, based on the set of skeletal points used in the encoding scheme. A comparison between these approaches reveals that Local SRF-CNN demonstrates better performance than the Global one. Finally, Local SRF-CNN outperforms the state-of-the-art technique, on both tasks, for UDysRS dyskinesia assessment, using joint coordinate data of the human body, achieving an overall performance in mean RMSE value of 0.6198 for Drinking task and 0.4885 for Communication, compared to 0.6575 and 0.5175, respectively. This illustrates the ability of the proposed machine learning system to successfully assess LID.

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Exploring Motion Boundaries in an End-to-End Network for Vision-based Parkinson's Severity Assessment

Cited by 2 publications

References 26 publications

Classification of Hand-Movement Disabilities in Parkinson’s Disease Using a Motion-Capture Device and Machine Learning

Classification of Hand-Movement Disabilities in Parkinson’s Disease Using a Motion-Capture Device and Machine Learning

Learning Spatio-Temporal Radon Footprints for Assessment of Parkinson’s Dyskinesia

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