A Mobile Application for Smart Computer-Aided Self-Administered Testing of Cognition, Speech, and Motor Impairment

Abstract

We present a model for digital neural impairment screening and self-assessment, which can evaluate cognitive and motor deficits for patients with symptoms of central nervous system (CNS) disorders, such as mild cognitive impairment (MCI), Parkinson’s disease (PD), Huntington’s disease (HD), or dementia. The data was collected with an Android mobile application that can track cognitive, hand tremor, energy expenditure, and speech features of subjects. We extracted 238 features as the model inputs using 16 tasks, 12 of them were based on a self-administered cognitive testing (SAGE) methodology and others used finger tapping and voice features acquired from the sensors of a smart mobile device (smartphone or tablet). Fifteen subjects were involved in the investigation: 7 patients with neurological disorders (1 with Parkinson’s disease, 3 with Huntington’s disease, 1 with early dementia, 1 with cerebral palsy, 1 post-stroke) and 8 healthy subjects. The finger tapping, SAGE, energy expenditure, and speech analysis features were used for neural impairment evaluations. The best results were achieved using a fusion of 13 classifiers for combined finger tapping and SAGE features (96.12% accuracy), and using bidirectional long short-term memory (BiLSTM) (94.29% accuracy) for speech analysis features.

Using Automatic Speech Recognition to Assess Thai Speech Language Fluency in the Montreal Cognitive Assessment (MoCA)

“…The overall error rate of the automatic transcripts was 33.4%, and the automated ASR classifier reached results comparable with those of the classifier that utilized manual transcriptions. Lauraitis et al [19] proposed neural impairment screening and selfassessment using a mobile application for MCI detection based on the self-administered gerocognitive examination (SAGE) screening. They developed a mobile application to collect data from different tasks.…”

BiLSTM with Data Augmentation using Interpolation Methods to Improve Early Detection of Parkinson Disease

“…1. A typical wave form variance of a healthy person and an individual suffering from speech impairment (data taken from the dataset described in [20,21])…”

“…Previous study on early diagnosis of PD include [19], which presented an ensemble classifier based on Deep Belief Network (DBN) and Self-Organizing Map (SOM) for remote tracking of PD progress. Recent studies [20,21] proposed a hybrid model based on bidirectional LSTM (Bi-LSTM) neural network and wavelet scattering transform (WST) and SVM classifier to detect speech impairments. Authors experimented on 15 subjects and 7 diseased subjects making up for 339 voice samples.…”

Local Pattern Transformation Based Feature Extraction for Recognition of Parkinson’s Disease Based on Gait Signals

“…Using information obtained from wearable sensors, the medical doctor can remotely assess the PD patient's status and prescribe the best treatment [23]. Recently, mobile devices such as smartphones and tablets have been employed to analyze user input in the form of voice, spiral drawings and answers to the self-administered cognitive test (SAGE) [24] to detect the symptoms of PD and related central nervous system (CNS) disorders, such as Alzheimer's disease, Huntington's disease (HD), or mild cognitive impairment (MCI) [25,26].…”