Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor.

Background: Several validated clinical scales measure the severity of essential tremor (ET). Their assessments are subjective and can depend on familiarity and training with scoring systems.

Method: We propose a multi-modal sensing using a wearable inertial measurement unit for estimating scores on the Fahn-Tolosa-Marin tremor rating scale (FTM) and determine the classification accuracy within the tremor type.

A translational study for detection of cardiac autonomic neuropathy using fractal features: A bench to bedside approach.

Cardiac autonomic Neuropathy (CAN) is an acute complication of Diabetes mellitus (DM) that does not exhibit overt symptoms in the subclinical stage. Researchers have developed several techniques that have proved to give higher efficiency in classification using software tools. The challenge in implementing the same using hardware for diagnosis fails when classification boundaries are mismatched, as there are more chances of misinterpreting the classes.

A Review of Spectroscopic and Non-Spectroscopic Techniques for Diagnosing Breast Cancer.

Malignancy, one of the leading causes of death worldwide, accounts for 9.6 million deaths in 2018. Around 1 out of 6 deaths are the direct result of the malignancy.

Wearable sensors during drawing tasks to measure the severity of essential tremor.

Commonly used methods to assess the severity of essential tremor (ET) are based on clinical observation and lack objectivity. This study proposes the use of wearable accelerometer sensors for the quantitative assessment of ET. Acceleration data was recorded by inertial measurement unit (IMU) sensors during sketching of Archimedes spirals in 17 ET participants and 18 healthy controls.

A Non-Invasive IR Sensor Technique to Differentiate Parkinson's Disease from Other Neurological Disorders Using Autonomic Dysfunction as Diagnostic Criterion.

Early diagnosis of Parkinson's disease (PD) plays a critical role in effective disease management and delayed disease progression. This study reports a technique that could diagnose and differentiate PD from essential tremor (ET) in its earlier stage using a non-motor phenotype. Autonomic dysfunction, an early symptom in PD patients, is caused by α-synuclein pathogenesis in the central nervous system and can be diagnosed using skin vasomotor response to cold stimuli.

Muscle activation strategies of people with early-stage Parkinson's during walking.

Introduction: Some people with Parkinson's disease (PD) frequently have an unsteady gait with shuffling, reduced strength, and increased rigidity. This study has investigated the difference in the neuromuscular strategies of people with early-stage PD, healthy older adults (HOA) and healthy young adult (HYA) during short-distance walking.

Method: Surface electromyogram (sEMG) was recorded from tibialis anterior (TA) and medial gastrocnemius (MG) muscles along with the acceleration data from the lower leg from 72 subjects-24 people with early-stage PD, 24 HOA and 24 HYA during short-distance walking on a level surface using wearable sensors.

Spatial Intensity Map of HDEMG Based Classification of Muscle Fatigue.

In this, study, we have investigated to identify the muscle fatigue using spatial maps of High-Density Electromyography (HDEMG). The experiment involves subjects performing plantar flexion at 40% maximum voluntary contraction until fatigue. During the experiment, HDEMG signal was recorded from the tibialis anterior muscle.

Pupillometric recordings to detect glaucoma.

. Glaucoma is the second cause of vision loss with early diagnosis having significantly better prognosis. We propose the use of hippus, the steady-state pupil oscillations, obtained from an eye-tracker for computerised detection of glaucoma.

Estimation of Parkinson's disease severity from voice features of vowels and consonant.

This study has investigated the efficiency of voice features in estimating the motor Unified Parkinson's Disease Rating Scale (UPDRS) score in Parkinson's disease (PD) patients. A total of 26 PD patients (mean age = 72) and 22 control subjects (mean age = 66.91) were recruited for the study.

Differentiating between Parkinson's disease patients and controls using variability in muscle activation during walking.

Surface electromyography (sEMG) of the lower limb muscles has been proposed to evaluate motor dysfunctions in Parkinson's disease (PD) patients. Variability in the sEMG could be used as an indicator of poor muscle coordination, but previous studies have reported conflicting results. This study has examined the variability of muscle using the coefficients of variance of Tibialis anterior (TA) and Medial gastrocnemius (MG) lower limb muscles for 24 PD, 24 age matched controls (CO), and 24 young controls (YC), during different phases of the gait cycle.

Locomo-Net: A Low -Complex Deep Learning Framework for sEMG-Based Hand Movement Recognition for Prosthetic Control.

The enhancement in the performance of the myoelectric pattern recognition techniques based on deep learning algorithm possess computationally expensive and exhibit extensive memory behavior. Therefore, in this paper we report a deep learning framework named 'Low-Complex Movement recognition-Net' (LoCoMo-Net) built with convolution neural network (CNN) for recognition of wrist and finger flexion movements; grasping and functional movements; and force pattern from single channel surface electromyography (sEMG) recording. The network consists of a two-stage pipeline: 1) input data compression; 2) data-driven weight sharing.

Normalized Mutual Information of phonetic sound to distinguish the speech of Parkinson's disease.

This study has investigated the use of inter-personnel mutual information computed from the phonetic sound recordings to differentiate between Parkinson's disease (PD) and control subjects. The normalized mutual information (NMI) denotes the amount of information shared between the voice recordings of people within the same group: PD and Control. The hypothesis of this study was that within group NMI will be significantly different when compared with inter- group NMI.

Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson's Disease.

In this paper, we have investigated the differences in the voices of Parkinson's disease (PD) and age-matched control (CO) subjects when uttering three phonemes using two complexity measures: fractal dimension (FD) and normalised mutual information (NMI). Three sustained phonetic voice recordings, /a/, /u/ and /m/, from 22 CO (mean age = 66.91) and 24 PD (mean age = 71.

Implementation and experimental validation of surface electromyogram and force model of Tibialis Anterior muscle for examining muscular factors.

This study reports a surface electromyogram and force of contraction model. The objective was to investigate the effect of changes in the size, type and number of motor units in the Tibialis Anterior muscle to surface electromyogram and force of dorsiflexion. A computational model to simulate surface electromyogram and associated force of contraction by the Tibialis Anterior muscle was developed.

Gait Parameters and Walking Patterns Show the Significant Differences Between Parkinson's Disease and Healthy Participants?

This study investigated the difference in the gait of patients with Parkinson's disease (PD), age-matched controls and young controls during three walking patterns. Experiments were conducted with 24 PD, 24 age-matched controls and 24 young controls, and four gait intervals were measured using inertial measurement units (IMU). Group differences between the mean and variance of the gait parameters (stride interval, stance interval, swing interval and double support interval) for the three groups were calculated and statistical significance was tested.

Effect of levodopa on handwriting tasks of different complexity in Parkinson's disease: a kinematic study.

Levodopa treatment does improve Parkinson's disease (PD) dysgraphia, but previous research is not in agreement about which aspects are most responsive. This study investigated the effect of levodopa on the kinematics of writing. Twenty-four patients with PD of less than 10 years duration and 25 age-matched controls were recruited.

Introducing a Novel Layer in Convolutional Neural Network for Automatic Identification of Diabetic Retinopathy.

Convolutional neural networks have been widely used for identifying diabetic retinopathy on color fundus images. For such application, we proposed a novel framework for the convolutional neural network architecture by embedding a preprocessing layer followed by the first convolutional layer to increase the performance of the convolutional neural network classifier. Two image enhancement techniques i.

Measuring complexity in different muscles during sustained contraction using fractal properties of SEMG signal.

Modelling and analysis of surface Electromyogram (sEMG) signal has gained increasing attention in bio-signal processing for medical and healthcare applications. This research reports the study to examine the complexity in surface electromyogram signal measured from different muscles to identify the properties of muscles. Experiments were conducted to study the properties of the four muscle groups representing four sizes in length and complexities: Zygomaticus (facial), biceps, quadriceps and flexor digitorum superficialis (FDS).

Identifying Noisy Electrodes in High Density Surface Electromyography Recordings Through Analysis of Spatial Similarities.

In this study we developed a technique for identifying noisy electrodes in high density surface electromyography (HD-sEMG). The technique finds the spatial similarity of each electrode in the electrode array by counting the number of interactions the electrode has. Using this information the technique identifies noisy electrodes by finding electrodes that are significantly dissimilar to the other electrodes.

An ICA-EBM-Based sEMG Classifier for Recognizing Lower Limb Movements in Individuals With and Without Knee Pathology.

Surface electromyography (sEMG) data acquired during lower limb movements has the potential for investigating knee pathology. Nevertheless, a major challenge encountered with sEMG signals generated by lower limb movements is the intersubject variability, because the signals recorded from the leg or thigh muscles are contingent on the characteristics of a subject such as gait activity and muscle structure. In order to cope with this difficulty, we have designed a three-step classification scheme.

Measuring the interactions between different locations in a muscle to monitor localized muscle fatigue.

In this study we investigated a technique for estimating the progression of localized muscle fatigue. This technique measures the dependence between motor units using high density surface electromyogram (HD-sEMG) and is based on the Normalized Mutual Information (NMI) measure. The NMI between every pair combination of the electrode array is computed to measure the interactions between electrodes.

Computing the variations in the self-similar properties of the various gait intervals in Parkinson disease patients.

This study has investigated the stride, swing, stance and double support intervals of gait for Parkinson's disease (PD) patients with different levels of severity. Self-similar properties of the gait signal were analyzed to investigate the changes in the gait pattern of the healthy and PD patients. To understand the self-similar property, detrended fluctuation analysis was performed.

Efficacy of Guided Spiral Drawing in the Classification of Parkinson's Disease.

Background: Change of handwriting can be an early marker for severity of Parkinson's disease but suffers from poor sensitivity and specificity due to inter-subject variations.

Aim: This study has investigated the group-difference in the dynamic features during sketching of spiral between PD and control subjects with the aim of developing an accurate method for diagnosing PD patients.

Method: Dynamic handwriting features were computed for 206 specimens collected from 62 Subjects (31 Parkinson's and 31 Controls).

Estimating the progression of muscle fatigue based on dependence between motor units using high density surface electromyogram.

In this study we have tested the hypothesis regarding the increase in synchronization with the onset of muscle fatigue. For this aim, we have investigated the difference in the synchronicity between high density surface electromyogram (sEMG) channels of the rested muscles and when at the limit of endurance. Synchronization was measured by computing and normalizing the mutual information between the sEMG signals recorded from the high-density array electrode locations.

Non-invasive detection of the freezing of gait in Parkinson's disease using spectral and wavelet features.

In this study, we have analyzed the accelerometer data recorded during gait analysis of Parkinson disease patients for detecting freezing of gait (FOG) episodes. The proposed method filters the recordings for noise reduction of the leg movement changes and computes the wavelet coefficients to detect FOG events. Publicly available FOG database was used and the technique was evaluated using receiver operating characteristic (ROC) analysis.