Continuous Parkinsonian Tremor Estimation Using Motion Data Tremor is one of the main symptoms of Parkinsonxe2x80x99s Disease (PD) that reduces the quality of life of affected patients. Tremor is measured as part of the Unified Parkinson Disease Rating Scale (UPDRS) part III. However, the assessment is based on onsite physical examinations and do not necessarily represent the patientsxe2x80x99 tremor experience in their daytoday life. In this work, we developed two methods based on deep long shortterm memory (LSTM) networks and gradient tree boosting to estimate Parkinsonian tremor using gyroscope sensor signals collected as the patients performed a variety of free body movements. The developed methods were assessed on data from 24 PD subjects. Subjectbased, leaveoneout crossvalidation demonstrated that the method based on gradient tree boosting provided a high correlation (r0.93 (pu003c0.0001)) between the estimated and clinicallyassessed tremor subscores in comparison to the LSTMbased method with (r0.77 (pu003c0.0001)).

LSTM Siamese Network for Parkinsonxe2x80x99s Disease Detection from Speech Parkinsonxe2x80x99s disease (PD) is a neurodegenerative disorder that severely affects motor functions. Symptoms include dysarthria and this fact has been the basis for PD detection from speech in several works. Machine learningbased technologies have made significant strides in automatic speech recognition, but their use is fairly limited for clinical diagnosis due to the lack of large corpora containing parkinsonian speech.In this work, we propose a twostep strategy to use machine learning methods for PD detection. In the first step, we use Long ShortTerm Memory (LSTM)based siamese networks to learn feature representations that highlight the information related to speech articulation and prosody relevant for PD detection. Siamese networks are trained on data pairs employing a Spanish corpus containing 52 patients and 56 control subjects. In the second step, we train a classifier to make decisions about the presence or absence of PD employing the features provided by the LSTM networks.We achieve an EER of 1.9% in the detection by combining the scores of different textdependent models. Preliminary experiments show the efficacy of the proposed method and prove the usefulness of LSTM for PD detection from speech.