Testing Dynamic Balance in People with Multiple Sclerosis: A Correlational Study between Standard Posturography and Robotic-Assistive Device.

BACKGROUND: Robotic devices are known to provide pivotal parameters to assess motor functions in Multiple Sclerosis (MS) as dynamic balance. However, there is still a lack of validation studies comparing innovative technologies with standard solutions. Thus, this study's aim was to compare the postural assessment of fifty people with MS (PwMS) during dynamic tasks performed with the gold standard EquiTest® and the robotic platform hunova®, using Center of Pressure (COP)-related parameters and global balance indexes. METHODS: Pearson's ρ correlations were run for each COP-related measure and the global balance index was computed from EquiTest® and hunova® in both open (EO) and closed-eyes (EC) conditions. RESULTS: Considering COP-related parameters, all correlations were significant in both EO (0.337 ≤ ρ ≤ 0.653) and EC (0.344 ≤ ρ ≤ 0.668). Furthermore, Pearson's analysis of global balance indexes revealed relatively strong for visual and vestibular, and strong for somatosensory system associations (ρ = 0.573; ρ = 0.494; ρ = 0.710, respectively). CONCLUSIONS: Findings confirm the use of hunova® as a valid device for dynamic balance assessment in MS, suggesting that such a robotic platform could allow for a more sensitive assessment of balance over time, and thus a better evaluation of the effectiveness of personalized treatment, thereby improving evidence-based clinical practice.

Automatic video analysis and classification of sleep-related hypermotor seizures and disorders of arousal.

OBJECTIVE: Sleep-related hypermotor epilepsy (SHE) is a focal epilepsy with seizures occurring mostly during sleep. SHE seizures present different motor characteristics ranging from dystonic posturing to hyperkinetic motor patterns, sometimes associated with affective symptoms and complex behaviors. Disorders of arousal (DOA) are sleep disorders with paroxysmal episodes that may present analogies with SHE seizures. Accurate interpretation of the different SHE patterns and their differentiation from DOA manifestations can be difficult and expensive, and can require highly skilled personnel not always available. Furthermore, it is operator dependent. METHODS: Common techniques for human motion analysis, such as wearable sensors (e.g., accelerometers) and motion capture systems, have been considered to overcome these problems. Unfortunately, these systems are cumbersome and they require trained personnel for marker and sensor positioning, limiting their use in the epilepsy domain. To overcome these problems, recently significant effort has been spent in studying automatic methods based on video analysis for the characterization of human motion. Systems based on computer vision and deep learning have been exploited in many fields, but epilepsy has received limited attention. RESULTS: In this paper, we present a pipeline composed of a set of three-dimensional convolutional neural networks that, starting from video recordings, reached an overall accuracy of 80% in the classification of different SHE semiology patterns and DOA. SIGNIFICANCE: The preliminary results obtained in this study highlight that our deep learning pipeline could be used by physicians as a tool to support them in the differential diagnosis of the different patterns of SHE and DOA, and encourage further investigation.

Markerless vs. Marker-Based Gait Analysis: A Proof of Concept Study.

The analysis of human gait is an important tool in medicine and rehabilitation to evaluate the effects and the progression of neurological diseases resulting in neuromotor disorders. In these fields, the gold standard techniques adopted to perform gait analysis rely on motion capture systems and markers. However, these systems present drawbacks: they are expensive, time consuming and they can affect the naturalness of the motion. For these reasons, in the last few years, considerable effort has been spent to study and implement markerless systems based on videography for gait analysis. Unfortunately, only few studies quantitatively compare the differences between markerless and marker-based systems in 3D settings. This work presented a new RGB video-based markerless system leveraging computer vision and deep learning to perform 3D gait analysis. These results were compared with those obtained by a marker-based motion capture system. To this end, we acquired simultaneously with the two systems a multimodal dataset of 16 people repeatedly walking in an indoor environment. With the two methods we obtained similar spatio-temporal parameters. The joint angles were comparable, except for a slight underestimation of the maximum flexion for ankle and knee angles. Taking together these results highlighted the possibility to adopt markerless technique for gait analysis.

Modified Functional Reach Test: Upper-Body Kinematics and Muscular Activity in Chronic Stroke Survivors.

Effective control of trunk muscles is fundamental to perform most daily activities. Stroke affects this ability also when sitting, and the Modified Functional Reach Test is a simple clinical method to evaluate sitting balance. We characterize the upper body kinematics and muscular activity during this test. Fifteen chronic stroke survivors performed twice, in separate sessions, three repetitions of the test in forward and lateral directions with their ipsilesional arm. We focused our analysis on muscles of the trunk and of the contralesional, not moving, arm. The bilateral activations of latissimi dorsi, trapezii transversalis and oblique externus abdominis were left/right asymmetric, for both test directions, except for the obliquus externus abdominis in the frontal reaching. Stroke survivors had difficulty deactivating the contralesional muscles at the end of each trial, especially the trapezii trasversalis in the lateral direction. The contralesional, non-moving arm had muscular activations modulated according to the movement phases of the moving arm. Repeating the task led to better performance in terms of reaching distance, supported by an increased activation of the trunk muscles. The reaching distance correlated negatively with the time-up-and-go test score.

Relationship between Timed Up and Go performance and quantitative biomechanical measures of balance.

Traumatic brain injury (TBI) impairs sensory-motor functions, with debilitating consequences on postural control and balance, which persist during the chronic stages of recovery. The Timed Up and Go (TUG) test is a reliable, safe, time-efficient, and one of the most widely used clinical measures to assess gait, balance, and fall risk in TBI patients and is extensively used in inpatient and outpatient settings. Although the TUG test has been used extensively due to its ease of performance and excellent reliability, limited research has been published that investigates the relationship between TUG performance and quantitative biomechanical measures of balance. The objective of this paper was to quantify the relationship between biomechanical variables of balance and the TUG scores in individuals with chronic TBI. Regression models were constructed using six biomechanical variables to predict TUG scores. The model that conservatively removed gait speed (i.e., TUG-1/GS) gave the best results, achieving a root-mean-square error of ∼±2 s and explaining over 69% of the variability.

A Computer Interface Controlled by Upper Limb Muscles: Effects of a Two Weeks Training on Younger and Older Adults.

As the population worldwide ages, there is a growing need for assistive technology and effective human-machine interfaces to address the wider range of motor disabilities that older adults may experience. Motor disabilities can make it difficult for individuals to perform basic daily tasks, such as getting dressed, preparing meals, or using a computer. The goal of this study was to investigate the effect of two weeks of training with a myoelectric computer interface (MCI) on motor functions in younger and older adults. Twenty people were recruited in the study: thirteen younger (range: 22-35 years old) and seven older (range: 61-78 years old) adults. Participants completed six training sessions of about 2 hours each, during which the activity of right and left biceps and trapezius were mapped into a control signal for the cursor of a computer. Results highlighted significant improvements in cursor control, and therefore in muscle coordination, in both groups. All participants with training became faster and more accurate, although people in different age range learned with a different dynamic. Results of the questionnaire on system usability and quality highlighted a general consensus about easiness of use and intuitiveness. These findings suggest that the proposed MCI training can be a powerful tool in the framework of assistive technologies for both younger and older adults. Further research is needed to determine the optimal duration and intensity of MCI training for different age groups and to investigate long-term effects of training on physical and cognitive function.

Standard versus innovative robotic balance assessment for people with multiple sclerosis: a correlational study.

INTRODUCTION: Balance disorders are common in people with Multiple Sclerosis (PwMS) and, together with other impairments and disabilities, often prevent PwMS from performing their daily living activities. Besides clinical scales and performance tests, robotic platforms can provide more sensitive, specific, and objective monitoring. Validated technologies have been adopted as gold standard, but innovative robotic solutions would represent an opportunity to detect balance impairment in PwMS. AIM: Study's aim was to compare postural assessment of 46 PwMS with a relapsing-remitting form during static tasks performed with the novel robotic platform hunova® and the gold standard EquiTest®, METHODS: Pearson's r was run on Center of Pressure (COP)-related parameters and global static balance measures computed from hunova® and EquiTest® in eyes-open (EO) and eyes-closed (EC) conditions. In addition, agreeableness level toward the use of both devices was tested through numeric rating scale. RESULTS: Considering COP-related parameters, correlations were significant for all measures (p < .001). Interestingly, in EO, a strong correlation was shown for sway area (r = .770), while Medio-Lateral (ML) and Anterior-Posterior (AP) oscillation range, path length, ML and AP speed, ML and AP root mean square distance had a relatively strong association (.454 ≤ r ≤ .576). In EC, except for ML oscillation range showing a relatively strong correlation (r = .532), other parameters were strongly associated (.603 ≤ r ≤ .782). Correlations between global balance indexes of hunova® and EquiTest® revealed a relatively strong association between the Somatosensory Score in EquiTest® and the Somatosensory Index in hunova® (r = - .488). While in EO Static Balance Index from hunova® was highly correlated with Equilibrium score of EquiTest® (r = .416), Static Balance Index had a relatively strong association with both the Equilibrium (r = .482) and Strategy Score (r = .583) of EquiTest® in EC. Results from agreeableness rating scale revealed that hunova® was highly appreciated compared to EquiTest® (p = .044). CONCLUSIONS: hunova® represents an innovative adjunct to standard robotic balance evaluation for PwMS. This confirms that combining traditional and robotic assessments can more accurately detect balance impairments in MS.

A Lifespan Approach to Balance in Static and Dynamic Conditions: The Effect of Age on Balance Abilities.

Postural control is a complex sensorimotor skill that is fundamental to our daily life. The abilities to maintain and recover balance degrade with age. However, the time decay of balance performance with age is not well understood. In this study, we aim at quantifying the age-dependent changes in standing balance under static and dynamic conditions. We tested 272 healthy subjects with ages ranging from 20 to 90. Subjects maintained the upright posture while standing on the robotic platform hunova®. In the evaluation of static balance, subjects stood on the fixed platform both with eyes open (EO) and eyes closed (EC). In the dynamic condition, subjects stood with eyes open on the moving foot platform that provided three different perturbations: (i) an inclination proportional to the center of pressure displacements, (ii) a pre-defined predictable motion, and (iii) an unpredictable and unexpected tilt. During all these tests, hunova® measured the inclination of the platform and the displacement of the center of pressure, while the trunk movements were recorded with an accelerometer placed on the sternum. To quantify balance performance, we computed spatio-temporal parameters typically used in clinical environments from the acceleration measures: mean velocity, variability of trunk motion, and trunk sway area. All subjects successfully completed all the proposed exercises. Their motor performance in the dynamic balance tasks quadratically changed with age. Also, we found that the reliance on visual feedback is not age-dependent in static conditions. All subjects well-tolerated the proposed protocol independently of their age without experiencing fatigue as we chose the timing of the evaluations based on clinical needs and routines. Thus, this study is a starting point for the definition of robot-based assessment protocols aiming at detecting the onset of age-related standing balance deficits and allowing the planning of tailored rehabilitation protocols to prevent falls in older adults.

Robot-based assessment of sitting and standing balance: preliminary results in Parkinson's disease.

Postural responses to unstable conditions or perturbations are important predictors of the risk of falling and can reveal balance deficits in people with neurological disorders, such as Parkinson's Disease (PD). However, there is a lack of evidences related to devices and protocols providing a comprehensive and quantitative evaluation of postural responses in different stability conditions. We tested ten people with PD and ten controls on a robotic platform capable to provide different mechanical interactions and to measure the center of pressure displacement, while trunk acceleration was recorded with a sensor placed on the sternum. We evaluated performance while maintaining upright posture in unperturbed, perturbed, and unstable conditions. The latter was tested while standing and sitting. We measured whether the proposed exercises and metrics could highlight differences in postural control. Participants with PD had worse performance metrics when standing under unperturbed or unstable conditions, and when sitting on the unstable platform. PD subjects in response to a forward perturbation showed bigger trunk oscillations coupled with a sharper increase of the CoP backward displacement. These responses could be due to higher stiffness of lower limb which leads to postural instability. The exercises and the proposed metrics highlighted differences in postural control, hence they can be used in clinical environment for the assessment and progression of postural impairments.

Effects of rTMS and intensive rehabilitation in Parkinson's Disease on learning and retention.

Movement is accompanied by modulation of oscillatory activity in different ranges over the sensorimotor areas. This increase is more evident in normal subjects and less in patients with Parkinson's Disease (PD), a disorder associated with deficits in the formation of new motor skills. Here, we investigated whether such EEG changes improved in a group of PD patients, after two different treatments and whether this relates to performance. Subjects underwent either a session of 5 Hz repetitive Transcranial Magnetic Stimulation (rTMS) over the right posterior parietal cortex or a 4-week Multidisciplinary Intensive Rehabilitation Treatment (MIRT). We used a reaching task with visuo-motor adaptation to a rotated display in incremental 10° steps up to 60°. Retention of the learned rotation was tested before and after either intervention over two consecutive days. High-density EEG was recorded throughout the testing. We found that patients adapted their movements to the rotated display similarly to controls, although retention was poorer. Both rTMS and MIRT lead to improvement in retention of the learned rotation. Mean beta modulation levels changed significantly after MIRT and not after rTMS. These results suggest that rTMS produced local improvement reflected in enhanced short-term skill retention; on the other hand, MIRT determined changes across the contralateral sensorimotor area, reflected in beta EEG changes.