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 pronunciation in speech, regularity and freezing of gait in walking, and tapping accuracy in hand movement. Results: Preliminary experiments indicate that most of the measurements are suitable to discriminate patients and controls. Significance is evaluated through statistical tests. Conclusion: Although the reported results correspond to preliminary experiments, we think that Apkinson is a very useful App that can help patients, caregivers and clinicians, in performing a more accurate monitoring of the disease progression. Additionally, the mobile App can be a personal health assistant.

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Most patients with Parkinson’s Disease (PD) develop speech deficits, including reduced sonority, altered articulation, and abnormal prosody. This article presents a methodology to automatically classify patients with PD and Healthy Control (HC) subjects. In this study, the Hilbert-Huang Transform (HHT) and Mel-Frequency Cepstral Coefficients (MFCCs) were considered to model modulated phonations (changing the tone from low to high and vice versa) of the vowels /a/, /i/, and /u/. The HHT was used to extract the first two formants from audio signals with the aim of modeling the stability of the tongue while the speakers were producing modulated vowels. Kruskal-Wallis statistical tests were used to eliminate redundant and non-relevant features in order to improve classification accuracy. PD patients and HC subjects were automatically classified using a Radial Basis Support Vector Machine (RBF-SVM). The results show that the proposed approach allows an automatic discrimination between PD and HC subjects with accuracies of up to 75 % for women and 73 % for men.

The study of automatic personality recognition has gained attention in the last decade thanks to a variety of applications that derive from this field. The big five model (also known as OCEAN) constitutes a well-known method to label different personality traits. This work considers transliterations of video recordings collected from YouTube (originally provided by the Idiap research institute) and automatically generated scores for the five personality traits which also were provided in the database. The transliterations are modeled with two different word embedding approaches, Word2Vec and GloVe and three different levels of analysis are included: regression to predict the score of each personality trait, binary classification between strong vs. weak presence of each trait, and the tri-class classification according to three different levels of manifestations in each trait (low, medium, and high). According to our findings, the proposed approach provides similar results to others reported in the state-of-the-art. We think that further research is required to find better results. Our results, as well as others reported in the literature, suggest that there is a big gap in the study of personality traits based on linguistic patterns, which make it necessary to work on collecting and labeling data considering the knowledge of expert psychologists and psycholinguists.

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