Mental illness (MI) is a leading cause of workplace absenteeism that often goes unrecognized and untreated. This paper presents a machine learning algorithm for predicting MI at workplace. The dataset consisted of responses from 1259 subjects collected through an online survey using a self-assessed questionnaire on the workplace environment. The responses were used as features for training a support vector machine to predict MI. Statistical analysis using the Guttmann correlation and the analysis of variance was done to determine feature significance. Results using 10-fold cross-validation showed that the model predicted MI with good accuracy. Findings support the feasibility of this approach for MI monitoring at the workplace as it offers an advantage over other technologies e.g., MRI scans, and EEG analysis, previously developed for the objective assessment of MI. © Mehran University of Engineering and Technology 2023

BACKGROUND: Gait impairment is an essential symptom of Parkinson’s disease (PD). OBJECTIVE: This paper introduces a novel computer-vision framework for automatic classification of the severity of gait impairment using front-view motion analysis. METHODS: Four hundred and fifty-six videos were recorded from 19 PD patients using an RGB camera during clinical gait assessment. Gait performance in each video was rated by a neurologist using the unified Parkinson’s disease rating scale for gait examination (UPDRS-gait). The proposed algorithm detects and tracks the silhouette of the test subject in the video to generate a height signal. Gait features were extracted from the height signal. Feature analysis was performed using the Kruskal-Wallis rank test. A support vector machine was trained using the features to classify the severity levels according to UPDRS-gait in 10-fold cross-validation. RESULTS: Features significantly (p< 0.05) differentiated between median-ranks of UPDRS-gait levels. The SVM classified the levels with a promising area under the ROC of 80.88%. CONCLUSION: Findings support the feasibility of this model for Parkinson’s gait assessment in the home environment. © 2021 - IOS Press. All rights reserved.

Objective: Aimless movement or wandering may be a symptom of mild cognitive impairment (MCI) that arises as a consequence of confusion and forgetfulness. This paper presents a support vector machine (SVM) framework based on movement analysis for the prediction of the onset and progression of MCI.

Methods: Movement data of 22 subjects with MCI, and 22 other healthy subjects, living independently in smart homes were collected for ten years using motion sensors. Features were extracted from the sensor data using movement metrics, including cyclomatic complexity, detrended fluctuation analysis, fractal index, entropy, and room transitions. Two different SVM classification algorithms were trained using the features, first to predict the progression of MCI in the post-transition period, and second to predict the onset of MCI in the pre-transition phase.

Results: The two SVMs were able to detect the onset six months earlier than the clinical diagnosis. The model accuracy in classifying MCI increased monotonically from the onset month and reached maximum (81%) at the 11th post-transition month. The features of cyclomatic complexity contributed significantly to the prediction results.

Conclusion: Findings support the use of movement complexity measures and machine learning for monitoring cognitive behavior in an independent living environment.

© Copyright 2020 IEEE., All rights reserved.

Blood lactate accumulation is a crucial fatigue indicator during sports training. Previous studies have predicted cycling fatigue using surface-electromyography (sEMG) to non-invasively estimate lactate concentration in blood. This study used sEMG to predict muscle fatigue while running and proposes a novel method for the automatic classification of running fatigue based on sEMG. Data were acquired from 12 runners during an incremental treadmill running-test using sEMG sensors placed on the vastus-lateralis, vastus-medialis, biceps-femoris, semitendinosus, and gastrocnemius muscles of the right and left legs. Blood lactate samples of each runner were collected every two minutes during the test. A change-point segmentation algorithm labeled each sample with a class of fatigue level as (1) aerobic, (2) anaerobic, or (3) recovery. Three separate random forest models were trained to classify fatigue using 36 frequency, 51 time-domain, and 36 time-event sEMG features. The models were optimized using a forward sequential feature elimination algorithm. Results showed that the random forest trained using distributive power frequency of the sEMG signal of the vastus-lateralis muscle alone could classify fatigue with high accuracy. Importantly for this feature, group-mean ranks were significantly different (p < 0.01) between fatigue classes. Findings support using this model for monitoring fatigue levels during running. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Background: Speech disorder is a common manifestation of Parkinson's disease with two main symptoms, dysprosody and dysphonia. Previous research studying objective measures of speech symptoms involved patients and examiners who were native language speakers. Measures such as cepstral separation difference (CSD) features to quantify dysphonia and dysprosody accurately distinguish the severity of speech impairment. Importantly CSD, together with other speech features, including Mel-frequency coefficients, fundamental-frequency variation, and spectral dynamics, characterize speech intelligibility in PD. However, non-native language speakers transfer phonological rules of their mother language that tamper speech assessment.

Objectives: This paper explores CSD's capability: first, to quantify dysprosody and dysphonia of non-native language speakers, Parkinson patients and controls, and secondly, to characterize the severity of speech impairment when Parkinson's dysprosody accompanies non-native linguistic dysprosody.

Methods: CSD features were extracted from 168 speech samples recorded from 19 healthy controls, 15 rehabilitated and 23 not-rehabilitated Parkinson patients in three different clinical speech tests based on Unified Parkinson's disease rating scale motor-speech examination. Statistical analyses were performed to compare groups using analysis of variance, intraclass correlation, and Guttman correlation coefficient µ2. Random forests were trained to classify the severity of speech impairment using CSD and the other speech features. Feature importance in classification was determined using permutation importance score.

Results: Results showed that the CSD feature describing dysphonia was uninfluenced by non-native accents, strongly correlated with the clinical examination (µ2>0.5), and significantly discriminated between the healthy, rehabilitated, and not-rehabilitated patient groups based on the severity of speech symptoms. However, the feature describing dysprosody did not correlate with the clinical examination but significantly distinguished the groups. The classification model based on random forests and selected features characterized the severity of speech impairment of non-native language speakers with high accuracy. Importantly, the permutation importance score of the CSD feature representing dysphonia was the highest compared to other features. Results showed a strong negative correlation (µ2<-0.5) between L-dopa administration and the CSD features.

Conclusions: Although non-native accents reduce speech intelligibility, the CSD features can accurately characterize speech impairment, which is not always possible in the clinical examination. Findings support using CSD for monitoring Parkinson's disease.

© 2019 Elsevier Ltd. All rights reserved.

Background & Objective: Hypokinetic arthria mainly associated with Parkinson’s disease. According to Duffy (1995) range of movement, tongue strength, speech rate and voice onset time for stops are reduced. There is an increase in phoneme to phoneme transitions, in syllable and word duration, and in voicing of voiceless stop. Unfortunately patients have physical limitations to reach the clinicians and speech therapists. So the objective of the study was to develop mobile assessment tool to monitor the speech impairments in patients with Parkinson’s disease.

Material And Methods: The data was collected from the study of Goetz et al. (2009), recently summarized in Tsanasetal.(2010a). The data of 120 subjects were collected through the Quantitative Motor Assessment Tool (QMAT) system. Data consisted of both normal and pathological voice. In speech tests, three different types of sentences were spoken by each subject. Each speech test was paired with Unified Parkinson’s Disease Rating Scale (UPDRS) test. 220 twenty audio sample were assessed on the basis of performance of subjects in spoken sentences. The data was used to discriminate healthy and impaired voice in Hypokinetic Dysarthria. For this purpose data is classified in to two classes, Class 0 for healthy voice Class 1 for unhealthy voice. Naïve Bayes classifier (NB) has been used for speech classification. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of Inter Stress Intervals (ISI) were read by each subject. These sentences were computed for comparisons between normal and impaired voice. The speech features which have been assessed for classification are Energy Entropy, Zero crossing rate (ZCR), Spectral-Centroid, Mean Fundamental-Frequency (Meanf0), Jitter (RAP), Jitter (PPQ), adShimmer (APQ).

Results: For speech test-1 and test-2, 72% and 80% accuracies of classification between healthy and impaired speech samples have been achieved respectively using the NB. For speech test-3, 64% correct classification is achieved using the NB.

Conclusion: The results direct the possibility of speech impairment classification in PD patients based on the clinical rating scale. Future, research will focused on the classification of speech impairment using Unified Parkinson's Disease Rating Scale (UPDRS). This will be helpful for the self-assessment of Patient with Parkinson (PWP) using the mobile device assessment tool.