A hybrid linear discriminant analysis and genetic algorithm to create a linear model of aging when performing motor tasks through inertial sensors positioned on the hand and forearm.

BACKGROUND: During the aging process, cognitive functions and performance of the muscular and neural system show signs of decline, thus making the elderly more susceptible to disease and death. These alterations, which occur with advanced age, affect functional performance in both the lower and upper members, and consequently human motor functions. Objective measurements are important tools to help understand and characterize the dysfunctions and limitations that occur due to neuromuscular changes related to advancing age. Therefore, the objective of this study is to attest to the difference between groups of young and old individuals through manual movements and whether the combination of features can produce a linear correlation concerning the different age groups. METHODS: This study counted on 99 participants, these were divided into 8 groups, which were grouped by age. The data collection was performed using inertial sensors (positioned on the back of the hand and on the back of the forearm). Firstly, the participants were divided into groups of young and elderly to verify if the groups could be distinguished through the features alone. Following this, the features were combined using the linear discriminant analysis (LDA), which gave rise to a singular feature called the LDA-value that aided in verifying the correlation between the different age ranges and the LDA-value. RESULTS: The results demonstrated that 125 features are able to distinguish the difference between the groups of young and elderly individuals. The use of the LDA-value allows for the obtaining of a linear model of the changes that occur with aging in the performance of tasks in line with advancing age, the correlation obtained, using Pearson's coefficient, was 0.86. CONCLUSION: When we compare only the young and elderly groups, the results indicate that there is a difference in the way tasks are performed between young and elderly individuals. When the 8 groups were analyzed, the linear correlation obtained was strong, with the LDA-value being effective in obtaining a linear correlation of the eight groups, demonstrating that although the features alone do not demonstrate gradual changes as a function of age, their combination established these changes.

Freezing of gait in Parkinson's disease: Classification using computational intelligence.

Parkinson's disease (PD) is a neurodegenerative disease represented by the progressive loss of dopamine producing neurons, with motor and non-motor symptoms that may be hard to distinguish from other disorders. Affecting millions of people across the world, its symptoms include bradykinesia, tremors, depression, rigidity, postural instability, cognitive decline, and falls. Furthermore, changes in gait can be used as a primary diagnosis factor. A dataset is described that records data on healthy individuals and on PD patients, including those who experience freezing of gait, in both the ON and OFF-medication states. The dataset is comprised of data for four separate tasks: voluntary stop, timed up and go, simple motor task, and dual motor and cognitive task. Seven different classifiers are applied to two problems relating to this data. The first problem is to distinguish PD patients from healthy individuals, both overall and per task. The second problem is to determine the effectiveness of medication. A thorough analysis on the classifiers and their results is performed. Overall, multilayer perceptron and decision tree provide the most consistent results.

RehaBEElitation: the architecture and organization of a serious game to evaluate motor signs in Parkinson's disease.

Background: The use of serious games (SG) has received increasing attention in health care, and can be applied for both rehabilitation and evaluation of motor signs of several diseases, such as Parkinson's disease (PD). However, the use of these instruments in clinical practice is poorly observed, since there is a scarcity of games that, during their development process, simultaneously address issues of usability and architectural design, contributing to the non-satisfaction of the actual needs of professionals and patients. Thus, this study aimed to present the architecture and usability evaluation at the design stage of a serious game, so-called RehaBEElitation, and assess the accessibility of the game. Methods: The game was created by a multidisciplinary team with experience in game development and PD, taking into consideration design guidelines for the development of SG. The user must control the movements of a bee in a 3D environment. The game tasks were designed to mimic the following movements found in the gold-standard method tool-Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS)-for the assessment of individuals with PD: hand opening and closing, hand extension and flexion, hand adduction and abduction, finger tapping, and forearm supination and pronation. The user interacts with the game using a wearable interface device that embeds inertial and tactile sensors. The architecture of RehaBEElitation was detailed using the business process model (BPM) notation and the usability of the architecture was evaluated using the Nielsen-Shneiderman heuristics. Game accessibility was evaluated by comparing the overall scores of each phase between 15 healthy participants and 15 PD patients. The PD group interacted with the game in both the ON and OFF states. Results: The system was modularized in order to implement parallel, simultaneous and independent programming at different levels, requiring less computational effort and enabling fluidity between the game and the control of the interface elements in real time. The developed architecture allows the inclusion of new elements for patient status monitoring, extending the functionality of the tool without changing its fundamental characteristics. The heuristic evaluation contemplated all the 14 heuristics proposed by Shneiderman, which enabled the implementation of improvements in the game. The evaluation of accessibility revealed no statistically significant differences (p < 0.05) between groups, except for the healthy group and the PD group in the OFF state of medication during Phase 3 of the game. Conclusions: The proposed architecture was presented in order to facilitate the reproduction of the system and extend its application to other scenarios. In the same way, the heuristic evaluation performed can serve as a contribution to the advancement of the SG design for PD. The accessibility evaluation revealed that the game is accessible to individuals with PD.

Wrist Movement Variability Assessment in Individuals with Parkinson's Disease.

(1) Background: Parkinson's disease (PD) is a neurodegenerative disorder represented by the progressive loss of dopamine-producing neurons, it decreases the individual's motor functions and affects the execution of movements. There is a real need to include quantitative techniques and reliable methods to assess the evolution of PD. (2) Methods: This cross-sectional study assessed the variability of wrist RUD (radial and ulnar deviation) and FE (flexion and extension) movements measured by two pairs of capacitive sensors (PS25454 EPIC). The hypothesis was that PD patients have less variability in wrist movement execution than healthy individuals. The data was collected from 29 participants (age: 62.13 ± 9.7) with PD and 29 healthy individuals (60.70 ± 8). Subjects performed the experimental tasks at normal and fast speeds. Six features that captured the amplitude of the hand movements around two axes were estimated from the collected signals. (3) Results: The movement variability was greater for healthy individuals than for PD patients (p < 0.05). (4) Conclusion: The low variability seen in the PD group may indicate they execute wrist RUD and FE in a more restricted way. The variability analysis proposed here could be used as an indicator of patient progress in therapeutic programs and required changes in medication dosage.

Prevalence of distinct types of hardware failures related to deep brain stimulation.

Deep brain stimulation (DBS) is an effective treatment of several types of neurological conditions, including Parkinson's disease, essential tremor, dystonia, and epilepsy. Despite technological progress in the past 10 years, the number of studies reporting side effects of DBS has increased, mainly due to hardware failures. This review investigated studies published between 2017 and 2021 to identify the prevalence of distinct types of hardware failures related to DBS. In total, fifteen studies were selected for the estimate of the prevalence of five distinct types of hardware failures: high impedance, fracture or failure of the lead or other parts of the implant, skin erosion and infection, lead malposition or migration, and implantable pulse generator (IPG) malfunction. The quality evaluation of the studies suggests a need to report results including populations from distinct regions of the world so that results can be generalized. The objective analysis of the prevalence of hardware failures showed that skin erosion and infection presented the highest prevalence in relation to other hardware failures. Despite the sophistication of the surgical technique of DBS over time, there is a considerable complication rate, about 7 per 100 individuals ([Formula: see text], in which CI is the confidence interval). Future research can also include correlation analysis with the aim of understanding the correlation between distinct hardware failures and variables such as gender, type of disorder, and age.

Effects of additional load at different heights on gait initiation: A statistical parametric mapping of center of pressure and center of mass behavior.

The purpose of this study was to investigate the effects of different vertical positions of an asymmetrical load on the anticipatory postural adjustments phase of gait initiation. Sixty-eight college students (32 males, 36 females; age: 23.65 ± 3.21 years old; weight: 69.98 ± 8.15 kg; height: 1.74 ± 0.08 m) were enrolled in the study. Ground reaction forces and moments were collected using two force platforms. The participants completed three trials under each of the following random conditions: no-load (NL), waist uniformly distributed load (WUD), shoulder uniformly distributed load (SUD), waist stance foot load (WST), shoulder stance foot load (SST), waist swing foot load (WSW), and shoulder swing foot load (SSW). The paired Hotelling's T-square test was used to compare the experimental conditions. The center of pressure (COP) time series were significantly different for the SUD vs. NL, SST vs. NL, WST vs. NL, and WSW vs. NL comparisons. Significant differences in COP time series were observed for all comparisons between waist vs. shoulder conditions. Overall, these differences were greater when the load was positioned at the shoulders. For the center of mass (COM) time series, significant differences were found for the WUD vs. NL and WSW vs. NL conditions. However, no differences were observed with the load positioned at the shoulders. In conclusion, only asymmetrical loading at the waist produced significant differences, and the higher the extra load, the greater the effects on COP behavior. By contrast, only minor changes were observed in COM behavior, suggesting that the changes in COP (the controller) behavior are adjustments to maintain the COM (controlled object) unaltered.

Discrimination between healthy and patients with Parkinson's disease from hand resting activity using inertial measurement unit.

BACKGROUND: Parkinson's disease (PD) is a neurological disease that affects the motor system. The associated motor symptoms are muscle rigidity or stiffness, bradykinesia, tremors, and gait disturbances. The correct diagnosis, especially in the initial stages, is fundamental to the life quality of the individual with PD. However, the methods used for diagnosis of PD are still based on subjective criteria. As a result, the objective of this study is the proposal of a method for the discrimination of individuals with PD (in the initial stages of the disease) from healthy groups, based on the inertial sensor recordings. METHODS: A total of 27 participants were selected, 15 individuals previously diagnosed with PD and 12 healthy individuals. The data collection was performed using inertial sensors (positioned on the back of the hand and on the back of the forearm). Different numbers of features were used to compare the values of sensitivity, specificity, precision, and accuracy of the classifiers. For group classification, 4 classifiers were used and compared, those being [Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes (NB)]. RESULTS: When all individuals with PD were analyzed, the best performance for sensitivity and accuracy (0.875 and 0.800, respectively) was found in the SVM classifier, fed with 20% and 10% of the features, respectively, while the best performance for specificity and precision (0.933 and 0.917, respectively) was associated with the RF classifier fed with 20% of all the features. When only individuals with PD and score 1 on the Hoehn and Yahr scale (HY) were analyzed, the best performances for sensitivity, precision and accuracy (0.933, 0.778 and 0.848, respectively) were from the SVM classifier, fed with 40% of all features, and the best result for precision (0.800) was connected to the NB classifier, fed with 20% of all features. CONCLUSION: Through an analysis of all individuals in this study with PD, the best classifier for the detection of PD (sensitivity) was the SVM fed with 20% of the features and the best classifier for ruling out PD (specificity) was the RF classifier fed with 20% of the features. When analyzing individuals with PD and score HY = 1, the SVM classifier was superior across the sensitivity, precision, and accuracy, and the NB classifier was superior in the specificity. The obtained result indicates that objective methods can be applied to help in the evaluation of PD.

Choice reaction time can be influenced by intervention protocols after stroke: A systematic review.

INTRODUCTION: Post-stroke individuals usually present a delay in choice reaction time (CRT), and it would be important to verify the efficacy in the reduction of CRT after intervention protocols. OBJECTIVE: The main question of this review is 'What are the characteristics of the CRT test and the interventions that decrease the CRT?' STUDY DESIGN: Systematic review. METHODS: The search was performed in March 2019 using the electronic databases, PubMed, Science Direct, Scopus, Web of Science, Lilacs, Cinahal, Cochrane, Ovid, Scielo, PEDro, and Embase. There was no restriction regarding publication dates, and studies written in English that were conducted on poststroke patients and presented CRT results were included. RESULTS: Six studies were included in this systematic review, and the majority showed varied objectives, methodologies, and groups, regarding the number and characteristics of the sample, varying from complex to simple tasks for the CRT evaluation. CONCLUSION: This review suggests the investigation of the CRT in stroke patients with functional tasks using auditory and/or visual stimulus. About the CRT training in stroke patients, this review also suggests bilateral training, including functional tasks, and the use of structural practice blocks, but more studies are needed to better demonstrate the effects of interventions on the CRT. REGISTRATION NUMBER: PROSPERO (protocol no. CRD42017073995).

On the use of histograms of oriented gradients for tremor detection from sinusoidal and spiral handwritten drawings of people with Parkinson's disease.

Parkinson's disease (PD), whose cardinal signs are tremor, rigidity, bradykinesia, and postural instability, gradually reduces the quality of life of the patient, making early diagnosis and follow-up of the disorder essential. This study aims to contribute to the objective evaluation of tremor in PD by introducing and assessing histograms of oriented gradients (HOG) to the analysis of handwriting sinusoidal and spiral patterns. These patterns were digitized and collected from handwritten drawings of people with PD (n = 20) and control healthy individuals (n = 20). The HOG descriptor was employed to represent relevant information from the data classified by three distinct machine-learning methods (random forest, k-nearest neighbor, support vector machine) and a deep learning method (convolutional neural network) to identify tremor in participants with PD automatically. The HOG descriptor allowed for the highest discriminating rates (accuracy 83.1%, sensitivity 85.4%, specificity 80.8%, area under the curve 91%) on the test set of sinusoidal patterns by using the one-dimensional convolutional neural network. In addition, ANOVA and Tukey analysis showed that the sinusoidal drawing is more appropriate than the spiral pattern, which is the most common drawing used for tremor detection. This research introduces a novel and alternative way of quantifying and evaluating tremor by means of handwritten drawings.

Influence of sampling frequency and number of strides on recurrence quantifiers extracted from gait data.

In this study, the influence of the sampling frequency and number of strides on recurrence quantifiers extracted from gait data was investigated in order to provide baseline values and preserve the system's non-linear dynamical characteristics expressed by these recurrence quantifiers. Recurrence quantifiers were extracted from a recurrence plot (RP), which required the reconstruction of a high-dimensional state space capable of reproducing the dynamical characteristics of the analyzed system. In this study, the following quantifiers were extracted: rate of recurrence (RR), determinism (DET), average diagonal lines length (AVG), maximum diagonal lines length (MaxL), Shannon entropy (EntD), and measure of trend (TREND). Data collected during treadmill walking were statistically analyzed to compare the distribution characteristics (mean, median, and standard deviation) and the quantifiers' correlation with those obtained from a control time series with an acquisition time corresponding to 150 strides and a 100-Hz sampling frequency, which are common values used in gait studies. It was not possible to reduce the number of strides for the MaxL or TREND. However, for the RR, DET, AVG, and EntD, it was possible to reduce the number of strides by 60% when analyzed together. The minimum sampling frequency required to extract all quantifiers simultaneously was 100 Hz. This potential reduction in the number of strides is appropriate for evaluating fast gait events, with short temporal localization in the RP, by applying the sliding window method to the recurrence plot.

Identification of arthropathy and myopathy of the temporomandibular syndrome by biomechanical facial features.

BACKGROUND: Temporomandibular disorders (TMDs) are pathological conditions affecting the temporomandibular joint and/or masticatory muscles. The current diagnosis of TMDs is complex and multi-factorial, including questionnaires, medical testing and the use of diagnostic methods, such as computed tomography and magnetic resonance imaging. The evaluation, like the mandibular range of motion, needs the experience of the professional in the field and as such, there is a probability of human error when diagnosing TMD. The aim of this study is therefore to develop a method with infrared cameras, using the maximum range of motion of the jaw and four types of classifiers to help professionals to classify the pathologies of the temporomandibular joint (TMJ) and related muscles in a quantitative way, thus helping to diagnose and follow up on TMD. METHODS: Forty individuals were evaluated and diagnosed using the diagnostic criteria for temporomandibular disorders (DC/TMD) scale, and divided into three groups: 20 healthy individuals (control group CG), 10 individuals with myopathies (MG), 10 individuals with arthropathies (AG). A quantitative assessment was carried out by motion capture. The TMJ movement was captured with camera tracking markers mounted on the face and jaw of each individual. Data was exported and analyzed using a custom-made software. The data was used to identify and place each participant into one of three classes using the K-nearest neighbor (KNN), Random Forest, Naïve Bayes and Support Vector Machine algorithms. RESULTS: Significant precision and accuracy (over 90%) was reached by KNN when classifying the three groups. The other methods tested presented lower values of sensitivity and specificity. CONCLUSION: The quantitative TMD classification method proposed herein has significant precision and accuracy over the DC/TMD standards. However, this should not be used as a standalone tool but as an auxiliary method for diagnostic TMDs.

Local dynamic stability and gait variability during attentional tasks in young adults.

Cell phone use while walking may be a cognitive distraction and reduce visual and motor attention. Thus, the aim of this study was to verify the effects of attentional dual-tasks while using a cell phone in different conditions. Stability, regularity, and linear variability of trunk kinematics, and gait spatiotemporal parameters in young adults were measured. Twenty young subjects of both genders were asked to walk on a treadmill for 4min under the following conditions: (a) looking forward at a fixed target 2.5m away (walking); (b) talking on a cell phone with unilateral handling (talking); (c) texting messages on a cell phone with unilateral handling (texting); and (d) looking forward at the aforementioned target while listening to music without handling the phone (listening). Local dynamic stability measured in terms of the largest Lyapunov exponent decreased while handling a cell phone (talking and texting). Gait variability and regularity increased when talking on a cell phone, but no variable changed in the listening condition. Under all dual-task conditions, there were significant increases in stride width and its variability. We conclude that young adults who use a cell phone when walking adapt their gait pattern conservatively, which can be because of increased attentional demand during cell phone use.