handwriting analysis

Automated analysis of Parkinson’s Disease on the basis of evaluation of handwriting

Bachelor in Computer science, Friedrich Alexander Universität (FAU), 2020.

Current methods and new trends in signal processing and pattern recognition for the automatic assessment of motor impairments: the case of Parkinson's disease

This chapter describes current methods and future trends in signal processing and pattern recognition techniques applied to the automatic analysis of motor impairments observed in patients with Parkinson's disease (PD). A revision of the …

Comparison of User Models Based on GMM-UBM and I-Vectors for Speech, Handwriting, and Gait Assessment of Parkinson’s Disease Patients

Parkinson’s disease is a neurodegenerative disorder characterized by the presence of different motor impairments. Information from speech, handwriting, and gait signals have been considered to evaluate the neurological state of the patients. On the …

Apkinson: the smartphone application for telemonitoring Parkinson’s patients through speech, gait and hands movement

Aim: This paper introduces Apkinson, a mobile application for motor evaluation and monitoring of Parkinson’s disease patients. Materials & methods: The App is based on previously reported methods, for instance, the evaluation of articulation and …

Analysis and evaluation of handwriting in patients with Parkinson’s disease using kinematic, geometrical, and non-linear features

Background and objectives: Parkinson’s disease is a neurological disorder that affects the motor system producing lack of coordination, resting tremor, and rigidity. Impairments in handwriting are among the main symptoms of the disease. Handwriting …

Multimodal assessment of Parkinson's disease: a deep learning approach

Parkinson's disease is a neurodegenerative disorder characterized by a variety of motor symptoms. Particularly, difficulties to start/stop movements have been observed in patients. From a technical/diagnostic point of view, these movement changes can …

Multi-view representation learning via GCCA for multimodal analysis of Parkinson's disease

Information from different bio-signals such as speech, handwriting, and gait have been used to monitor the state of Parkinson's disease (PD) patients, however, all the multimodal bio-signals may not always be available. We propose a method based on …