Comparison of vestibular input statistics during natural activities and while piloting an aircraft.

The present study investigates the statistics and spectral content of natural vestibular stimuli experienced by healthy human subjects during three unconstrained activities. More specifically, we assessed how the characteristics of vestibular inputs are altered during the operation of a complex human-machine interface (a flight in a helicopter simulator) compared with more ecological tasks, namely a walk in an office space and a seated visual exploration task. As previously reported, we found that the power spectra of vestibular stimuli experienced during self-navigation could be modeled by two power laws but noted a potential effect of task intensity on the transition frequency between the two fits. In contrast, both tasks that required a seated position had power spectra that were better described by an inverted U shape in all planes of motion. Taken together, our results suggest that 1) walking elicits stereotyped vestibular inputs whose power spectra can be modeled by two power laws that intersect at a task intensity-dependent frequency; 2) body posture induces changes in the frequency content of vestibular information; 3) pilots tend to operate their aircraft in a way that does not generate highly nonecological vestibular stimuli; and 4) nevertheless, human-machine interfaces used as a means of manual navigation still impose some unnatural, contextual constraints on their operators.NEW & NOTEWORTHY Building upon previously published research, this study assesses and compares the vestibular stimuli experienced by healthy subjects in natural tasks and during the interaction with a complex machine: a helicopter simulator. Our results suggest the existence of an anatomical filter, meaning that body posture shapes vestibular spectral content. Our findings further indicate that operators control their machine within a constrained operating range such that they experience vestibular stimulations that are as ecological as possible.

Tilt perception is different in the pitch and roll planes in human.

Neurophysiological tests probing the vestibulo-ocular, colic and spinal pathways are the gold standard to evaluate the vestibular system in clinics. In contrast, vestibular perception is rarely tested despite its potential usefulness in professional training and for the longitudinal follow-up of professionals dealing with complex man-machine interfaces, such as aircraft pilots. This is explored here using a helicopter flight simulator to probe the vestibular perception of pilots. The vestibular perception of nine professional helicopter pilots was tested using a full flight helicopter simulator. The cabin was tilted six times in roll and six times in pitch (-15°, -10°, -5°, 5°, 10° and 15°) while the pilots had no visual cue. The velocities of the outbound displacement of the cabin were kept below the threshold of the semicircular canal perception. After the completion of each movement, the pilots were asked to put the cabin back in the horizontal plane (still without visual cues). The order of the 12 trials was randomized with two additional control trials where the cabin stayed in the horizontal plane but rotated in yaw (-10° and +10°). Pilots were significantly more precise in roll (average error in roll: 1.15 ± 0.67°) than in pitch (average error in pitch: 2.89 ± 1.06°) (Wilcoxon signed-rank test: p < 0.01). However, we did not find a significant difference either between left and right roll tilts (p = 0.51) or between forward and backward pitch tilts (p = 0.59). Furthermore, we found that the accuracies were significantly biased with respect to the initial tilt. The greater the initial tilt was, the less precise the pilots were, although maintaining the direction of the tilt, meaning that the error can be expressed as a vestibular error gain in the ability to perceive the modification in the orientation. This significant result was found in both roll (Friedman test: p < 0.01) and pitch (p < 0.001). However, the pitch trend error was more prominent (gain = 0.77 vs gain = 0.93) than roll. This study is a first step in the determination of the perceptive-motor profile of pilots, which could be of major use for their training and their longitudinal follow-up. A similar protocol may also be useful in clinics to monitor the aging process of the otolith system with a simplified testing device.

Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall.

Nowadays, it becomes of paramount societal importance to support many frail-prone groups in our society (elderly, patients with neurodegenerative diseases, etc.) to remain socially and physically active, maintain their quality of life, and avoid their loss of autonomy. Once older people enter the prefrail stage, they are already likely to experience falls whose consequences may accelerate the deterioration of their quality of life (injuries, fear of falling, reduction of physical activity). In that context, detecting frailty and high risk of fall at an early stage is the first line of defense against the detrimental consequences of fall. The second line of defense would be to develop original protocols to detect future fallers before any fall occur. This paper briefly summarizes the current advancements and perspectives that may arise from the combination of affordable and easy-to-use non-wearable systems (force platforms, 3D tracking motion systems), wearable systems (accelerometers, gyroscopes, inertial measurement units-IMUs) with appropriate machine learning analytics, as well as the efforts to address these challenges.

Heterogeneities of the perceptual-motor style during locomotion at height.

In a recent review, we summarized the characteristics of perceptual-motor style in humans. Style can vary from individual to individual, task to task and pathology to pathology, as sensorimotor transformations demonstrate considerable adaptability and plasticity. Although the behavioral evidence for individual styles is substantial, much remains to be done to understand the neural and mechanical substrates of inter-individual differences in sensorimotor performance. In this study, we aimed to investigate the modulation of perceptual-motor style during locomotion at height in 16 persons with no history of fear of heights or acrophobia. We used an inexpensive virtual reality (VR) video game. In this VR game, Richie's Plank, the person progresses on a narrow plank placed between two buildings at the height of the 30th floor. Our first finding was that the static markers (head, trunk and limb configurations relative to the gravitational vertical) and some dynamic markers (jerk, root mean square, sample entropy and two-thirds power law at head, trunk and limb level) we had previously identified to define perceptual motor style during locomotion could account for fear modulation during VR play. Our second surprising result was the heterogeneity of this modulation in the 16 young, healthy individuals exposed to moving at a height. Finally, 56% of participants showed a persistent change in at least one variable of their skeletal configuration and 61% in one variable of their dynamic control during ground locomotion after exposure to height.

Retinal atrophy and markers of systemic and cerebrovascular severity in homozygous sickle cell disease.

INTRODUCTION: While paramacular retinal atrophy (PRA) is known to be found in 48% of eyes of adults and 42% of eyes of children with homozygous SCD (SS-SCD), the aim of this study is to assess the association between PRA and red blood cell (RBC) deformability, hematological markers and brain imaging abnormalities in SS-SCD. METHODS: This study is a subset of DREAM2, a prospective observational study performed between August 2015 and August 2016. Children (5-17 years) with SS-SCD and no history of large vessel vasculopathy, were included. Ophthalmological characteristics including visual acuity, fundus examination, OCT of central and temporal retina (with several retinal thickness measurements) were explored in relation with RBC deformability (ektacytometry), hematological and biochemical (hemolysis parameters), and neurological (cerebral oxygenation estimated by Near Infrared Spectroscopy, brain magnetic resonance imaging) investigations. RESULTS: 17 children (5 boys; mean age: 13 years) with complete ophthalmological investigations were included in the analysis; 8 exhibited PRA. RBC deformability was found to be significantly lower in children with PRA for measurements made at 1.69 Pa (0.16 a.u ± 0.02 vs 0.21 a.u ± 0.03, p = 0.02) and above, as well as cerebral oxygenation (59.25% ± 9.9 vs 71.53% ± 4.9, p = 0.02). A significant positive correlation was found between temporal retinal thickness and hemoglobin level (ρ = 0.65, p = 0.007), hematocrit (ρ = 0.53, p = 0.04) and RBC deformability at 3 Pa (ρ = 0.75, p = 0.005) and above. CONCLUSIONS: These results suggest that PRA could be an early marker of systemic severity and cerebral oxygenation in SCD. Whether it could help predicting cerebral vasculopathy requires further investigations.

A review of center of pressure (COP) variables to quantify standing balance in elderly people: Algorithms and open-access code.

Postural control is often quantified by recording the trajectory of the center of pressure (COP)-also called stabilogram-during human quiet standing. This quantification has many important applications, such as the early detection of balance degradation to prevent falls, a crucial task whose relevance increases with the aging of the population. Due to the complexity of the quantification process, the analyses of sway patterns have been performed empirically using a number of variables, such as ellipse confidence area or mean velocity. This study reviews and compares a wide range of state-of-the-art variables that are used to assess the risk of fall in elderly from a stabilogram. When appropriate, we discuss the hypothesis and mathematical assumptions that underlie these variables, and we propose a reproducible method to compute each of them. Additionally, we provide a statistical description of their behavior on two datasets recorded in two elderly populations and with different protocols, to hint at typical values of these variables. First, the balance of 133 elderly individuals, including 32 fallers, was measured on a relatively inexpensive, portable force platform (Wii Balance Board, Nintendo) with a 25-s open-eyes protocol. Second, the recordings of 76 elderly individuals, from an open access database commonly used to test static balance analyses, were used to compute the values of the variables on 60-s eyes-open recordings with a research laboratory standard force platform.

The 5P program, personalized and participatory primary prevention pathway: Rational and design of a clinical trial in general practice.

The aging of the population is leading to an increase in the number of people with loss of autonomy, placing a strain on the health care system. Its prevention at early stages such as the frailty stage would allow an improvement in the quality of life of seniors while limiting health care expenses. The "Atout Age" prevention program set up by the health public authorities of Reunion Island for retired people and the new frailty assessment tools based on mathematical machine learning algorithms could improve the ambulatory care of senior citizens. At present, referral care remains hospital with comprehensive geriatric assessment and there is a lack of evidence of the effectiveness of a prevention pathway for loss of autonomy in primary care. For these reasons, the 5P program "Personalized and Participative Primary Prevention Pathway" has been started in order to obtain scientific evidence. In this article, we present the objectives, design and first results, used in the 5P program up to the implementation of a clinical trial in general practice. The program is articulated in 3 phases. A first phase to evaluate the acceptability of innovative screening tools for frailty. A second pilot phase evaluates the feasibility of a large-scale ambulatory clinical trial in general practice. The last phase described in this article, is a multisite, pseudo-randomized, controlled clinical trial measuring the impact of the "Atout Age" workshops on the physical performance and the quality of life of seniors compared with their usual ambulatory follow-up.

Perceptual-motor styles.

Even for a stereotyped task, sensorimotor behavior is generally variable due to noise, redundancy, adaptability, learning or plasticity. The sources and significance of different kinds of behavioral variability have attracted considerable attention in recent years. However, the idea that part of this variability depends on unique individual strategies has been explored to a lesser extent. In particular, the notion of style recurs infrequently in the literature on sensorimotor behavior. In general use, style refers to a distinctive manner or custom of behaving oneself or of doing something, especially one that is typical of a person, group of people, place, context, or period. The application of the term to the domain of perceptual and motor phenomenology opens new perspectives on the nature of behavioral variability, perspectives that are complementary to those typically considered in the studies of sensorimotor variability. In particular, the concept of style may help toward the development of personalised physiology and medicine by providing markers of individual behaviour and response to different stimuli or treatments. Here, we cover some potential applications of the concept of perceptual-motor style to different areas of neuroscience, both in the healthy and the diseased. We prefer to be as general as possible in the types of applications we consider, even at the expense of running the risk of encompassing loosely related studies, given the relative novelty of the introduction of the term perceptual-motor style in neurosciences.

Revealing posturographic profile of patients with Parkinsonian syndromes through a novel hypothesis testing framework based on machine learning.

Falling in Parkinsonian syndromes (PS) is associated with postural instability and consists a common cause of disability among PS patients. Current posturographic practices record the body's center-of-pressure displacement (statokinesigram) while the patient stands on a force platform. Statokinesigrams, after appropriate processing, can offer numerous posturographic features. This fact, although beneficial, challenges the efforts for valid statistics via standard univariate approaches. In this work, 123 PS patients were classified into fallers (PSF) or non-faller (PSNF) based on the clinical assessment, and underwent simple Romberg Test (eyes open/eyes closed). We developed a non-parametric multivariate two-sample test (ts-AUC) based on machine learning, in order to examine statokinesigrams' differences between PSF and PSNF. We analyzed posturographic features using both multiple testing with p-value adjustment and ts-AUC. While ts-AUC showed significant difference between groups (p-value = 0.01), multiple testing did not agree with this result (eyes open). PSF showed significantly increased antero-posterior movements as well as increased posturographic area compared to PSNF. Our study highlights the superiority of ts-AUC compared to standard statistical tools in distinguishing PSF and PSNF in multidimensional space. Machine learning-based statistical tests can be seen as a natural extension of classical statistics and should be considered, especially when dealing with multifactorial assessments.

Assessing Smoothness of Arm Movements With Jerk: A Comparison of Laterality, Contraction Mode and Plane of Elevation. A Pilot Study.

Measuring the quality of movement is a need and a challenge for clinicians. Jerk, defined as the quantity of acceleration variation, is a kinematic parameter used to assess the smoothness of movement. We aimed to assess and compare jerk metrics in asymptomatic participants for 3 important movement characteristics that are considered by clinicians during shoulder examination: dominant and non-dominant side, concentric and eccentric contraction mode, and arm elevation plane. In this pilot study, we measured jerk metrics by using Xsens® inertial measurement units strapped to the wrists for 11 different active arm movements (ascending and lowering phases): 3 bilateral maximal arm elevations in sagittal, scapular and frontal plane; 2 unilateral functional movements (hair combing and low back washing); and 2 unilateral maximal arm elevations in sagittal and scapular plane, performed with both arms alternately, right arm first. Each arm movement was repeated 3 times successively and the whole procedure was performed 3 times on different days. The recorded time series was segmented with semi-supervised algorithms. Comparisons involved the Wilcoxon signed rank test (p < 0.05) with Bonferroni correction. We included 30 right-handed asymptomatic individuals [17 men, mean (SD) age 31.9 (11.4) years]. Right jerk was significantly less than left jerk for bilateral arm elevations in all planes (all p < 0.05) and for functional movement (p < 0.05). Jerk was significantly reduced during the concentric (ascending) phase than eccentric (lowering) phase for bilateral and unilateral right and left arm elevations in all planes (all p < 0.05). Jerk during bilateral arm elevation was significantly reduced in the sagittal and scapular planes versus the frontal plane (both p < 0.01) and in the sagittal versus scapular plane (p < 0.05). Jerk during unilateral left arm elevation was significantly reduced in the sagittal versus scapular plane (p < 0.05). Jerk metrics did not differ between sagittal and scapular unilateral right arm elevation. Using inertial measurement units, jerk metrics can well describe differences between the dominant and non-dominant arm, concentric and eccentric modes and planes in arm elevation. Jerk metrics were reduced during arm movements performed with the dominant right arm during the concentric phase and in the sagittal plane. Using IMUs, jerk metrics are a promising method to assess the quality of basic shoulder movement.

Quantitative assessment of consciousness during anesthesia without EEG data.

Assessing the depth of anesthesia (DoA) is a daily challenge for anesthesiologists. The best assessment of the depth of anesthesia is commonly thought to be the one made by the doctor in charge of the patient. This evaluation is based on the integration of several parameters including epidemiological, pharmacological and physiological data. By developing a protocol to record synchronously all these parameters we aim at having this evaluation made by an algorithm. Our hypothesis was that the standard parameters recorded during anesthesia (without EEG) could provide a good insight into the consciousness level of the patient. We developed a complete solution for high-resolution longitudinal follow-up of patients during anesthesia. A Hidden Markov Model (HMM) was trained on the database in order to predict and assess states based on four physiological variables that were adjusted to the consciousness level: Heart Rate (HR), Mean Blood Pressure (MeanBP) Respiratory Rate (RR), and AA Inspiratory Concentration (AAFi) all without using EEG recordings. Patients undergoing general anesthesia for hernial inguinal repair were included after informed consent. The algorithm was tested on 30 patients. The percentage of error to identify the actual state among Awake, LOC, Anesthesia, ROC and Emergence was 18%. This protocol constitutes the very first step on the way towards a multimodal approach of anesthesia. The fact that our first classifier already demonstrated a good predictability is very encouraging for the future. Indeed, this first model was merely a proof of concept to encourage research ways in the field of machine learning and anesthesia.

An opinion paper on the maintenance of robustness: Towards a multimodal and intergenerational approach using digital twins.

The increasing number of frail elderly people in our aging society is becoming problematic: about 11% of community-dwelling older persons are frail and another 42% are pre-frail. Consequently, a major challenge in the coming years will be to test people over the age of 60 years to detect pre-frailty at the earliest stage and to return them to robustness using the targeted interventions that are becoming increasingly available. This challenge requires individual longitudinal monitoring (ILM) or follow-up of community-dwelling older persons using quantitative approaches. This paper briefly describes an effort to tackle this challenge. Extending the detection of the pre-frail stages to other population groups is also suggested. Appropriate algorithms have been used to begin the tracing of faint physiological signals in order to detect transitions from robustness to pre-frailty states and from pre-frailty to frailty states. It is hoped that these studies will allow older adults to receive preventive treatment at the correct institutions and by the appropriate professionals as early as possible, which will prevent loss of autonomy. Altogether, ILM is conceived as an emerging property of databases ("digital twins") and not the reverse. Furthermore, ILM should facilitate a coordinated set of actions by the caregivers, which is a complex challenge in itself. This approach should be gradually extended to all ages, because frailty has no age, as is testified by overwork, burnout, and post-traumatic syndrome.

Center of pressure displacement characteristics differentiate fall risk in older people: A systematic review with meta-analysis.

Falling is the second most prevalent cause of accidental death in the world. Currently available clinical tests to assess balance in older people are insufficiently sensitive to screen for fall risk in this population. Laboratory tests that record the center of pressure (COP) trajectory could overcome this problem but despite their widespread use, the choice of COP trajectory features for use as a biomarker of fall risk lacks consensus. This systematic review and meta-analysis aimed at identifying the best COP characteristics to predict risk of falling in older adults. More than 4000 articles were screened; 44 (7176 older adults) were included in this study. Several COP parameters emerged as good indices to discriminate fallers from non-fallers. From sensitivity analysis, Sway area per unit time, anteroposterior mean velocity, and radial mean velocity were the best traditional features. In this study, identification of older people with a high fall risk was demonstrated using quiet-standing recordings. Such screening would also be useful for routine follow-up of balance changes in older fallers in clinical practice.

Personalized Template-Based Step Detection From Inertial Measurement Units Signals in Multiple Sclerosis.

Background: Objective gait assessment is key for the follow-up of patients with progressive multiple sclerosis (pMS). Inertial measurement units (IMUs) provide reliable and yet easy quantitative gait assessment in routine clinical settings. However, to the best of our knowledge, no automated step-detection algorithm performs well in detecting severely altered pMS gait. Method: This article elaborates on a step-detection method based on personalized templates tested against a gold standard. Twenty-two individuals with pMS and 10 young healthy subjects (HSs) were instructed to walk on an electronic walkway wearing synchronized IMUs. Templates were derived from the IMU signals by using Initial and Final Contact times given by the walkway. These were used to detect steps from other gait trials of the same individual (intra-individual template-based detection, IITD) or another participant from the same group (pMS or HS) (intra-group template-based detection, IGTD). All participants were seen twice with a 6-month interval, with two measurements performed at each visit. Performance and accuracy metrics were computed, along with a similarity index (SId), which was computed as the mean distance between detected steps and their respective closest template. Results: For HS participants, both the IITD and the IGTD algorithms had precision and recall of 1.00 for detecting steps. For pMS participants, precision and recall ranged from 0.94 to 1.00 for IITD and 0.85 to 0.95 for IGTD depending on the level of disability. The SId was correlated with performance and the accuracy of the result. An SId threshold of 0.957 (IITD) and 0.963 (IGTD) could rule out decreased performance (F-measure ≤ 0.95), with negative predictive values of 0.99 and 0.96 with the IITD and IGTD algorithms. Also, the SId computed with the IITD and IGTD algorithms could distinguish individuals showing changes at 6-month follow-up. Conclusion: This personalized step-detection method has high performance for detecting steps in pMS individuals with severely altered gait. The algorithm can be self-evaluating with the SI, which gives a measure of the confidence the clinician can have in the detection. What is more, the SId can be used as a biomarker of change in disease severity occurring between the two measurement times.

Motor style at rest and during locomotion in human.

Humans exhibit various motor styles that reflect their intra- and interindividual variability when implementing sensorimotor transformations. This opens important questions, such as, At what point should they be readjusted to maintain optimal motor control? Do changes in motor style reveal the onset of a pathological process and can these changes help rehabilitation and recovery? To further investigate the concept of motor style, tests were carried out to quantify posture at rest and motor control in 18 healthy subjects under four conditions: walking at three velocities (comfortable walking, walking at 4 km/h, and race walking) and running at maximum velocity. The results suggest that motor control can be conveniently decomposed into a static component (a stable configuration of the head and column with respect to the gravitational vertical) and dynamic components (head, trunk, and limb movements) in humans, as in quadrupeds, and both at rest and during locomotion. These skeletal configurations provide static markers to quantify the motor style of individuals because they exhibit large variability among subjects. Also, using four measurements (jerk, root mean square, sample entropy, and the two-thirds power law), it was shown that the dynamics were variable at both intra- and interindividual levels during locomotion. Variability increased following a head-to -toe gradient. These findings led us to select dynamic markers that could define, together with static markers, the motor style of a subject. Finally, our results support the view that postural and motor control are subserved by different neuronal networks in frontal, sagittal, and transversal planes.NEW & NOTEWORTHY During human locomotion, motor control can be conveniently decomposed into a static and dynamic components. Variable dynamics were observed at both the intra- and interindividual levels during locomotion. Variability increased following a head-to-toe gradient. Finally, our results support the view that postural and motor control are subserved by different neuronal networks in the frontal, sagittal, and transversal planes.

Non-Linear Template-Based Approach for the Study of Locomotion.

The automatic detection of gait events (i.e., Initial Contact (IC) and Final Contact (FC)) is crucial for the characterisation of gait from Inertial Measurements Units. In this article, we present a method for detecting steps (i.e., IC and FC) from signals of gait sequences of individuals recorded with a gyrometer. The proposed approach combines the use of a dictionary of templates and a Dynamic Time Warping (DTW) measure of fit to retrieve these templates into input signals. Several strategies for choosing and learning the adequate templates from annotated data are also described. The method is tested on thirteen healthy subjects and compared to gold standard. Depending of the template choice, the proposed algorithm achieves average errors from 0.01 to 0.03 s for the detection of IC, FC and step duration. Results demonstrate that the use of DTW allows achieving these performances with only one single template. DTW is a convenient tool to perform pattern recognition on gait gyrometer signals. This study paves the way for new step detection methods: it shows that using one single template associated with non-linear deformations may be sufficient to model the gait of healthy subjects.

Postadychute-AG, Detection, and Prevention of the Risk of Falling Among Elderly People in Nursing Homes: Protocol of a Multicentre and Prospective Intervention Study.

Introduction: While falls among the elderly is a public health issue, because of the social, medical, and economic burden they represent, the tools to predict falls are limited. Posturography has been developed to distinguish fallers from non-fallers, however, there is too little data to show how predictions change as older adults' physical abilities improve. The Postadychute-AG clinical trial aims to evaluate the evolution of posturographic parameters in relation to the improvement of balance through adapted physical activity (APA) programs. Methods: In this prospective, multicentre clinical trial, institutionalized seniors over 65 years of age will be followed for a period of 6 months through computer-assisted posturography and automatic gait analysis. During the entire duration of the follow-up, they will benefit from a monthly measurement of their postural and locomotion capacities through a recording of their static balance and gait thanks to a software developed for this purpose. The data gathered will be correlated with the daily record of falls in the institution. Static and dynamic balance measurements aim to extract biomechanical markers and compare them with functional assessments of motor skills (Berg Balance Scale and Mini Motor Test), expecting their superiority in predicting the number of falls. Participants will be followed for 3 months without APA and 3 months with APA in homogeneous group exercises. An analysis of variance will evaluate the variability of monthly measures of balance in order to record the minimum clinically detectable change (MDC) as participants improve their physical condition through APA. Discussion: Previous studies have stated the MDC through repeated measurements of balance but, to our knowledge, none appear to have implemented monthly measurements of balance and gait. Combined with a reliable measure of the number of falls per person, motor capacities and other precipitating factors, this study aims to provide biomechanical markers predictive of fall risk with their sensitivity to improvement in clinical status over the medium term. This trial could provide the basis for posturographic and gait variable values for these elderly people and provide a solution to distinguish those most at risk to be implemented in current practice in nursing homes. Trial Registration: ID-RCB 2017-A02545-48. Protocol Version: Version 4.2 dated January 8, 2020.

Wearable inertial sensors provide reliable biomarkers of disease severity in multiple sclerosis: A systematic review and meta-analysis.

BACKGROUND: Gait impairment is a hallmark of multiple sclerosis (MS). InertiaLocoGraphy, the quantification of gait with inertial measurement units (IMUs), has been found useful to detect early changes in gait in MS. Still, the potential use of IMUs as a reliable biomarker of disease severity in MS remains unknown. OBJECTIVE: This systematic review and meta-analysis of observational studies aimed to describe IMU protocols used to assess gait in MS patients and calculate the effect sizes of IMU features associated with disease severity scale measures. METHODS: We searched MEDLINE, Cochrane Central, EMBASE and grey literature to identify articles published before May 2, 2018 that measured gait in MS patients by using IMUs and correlated IMU parameters with disease severity scale measures. We excluded from the meta-analysis articles that did not provide enough data to evaluate the association between IMU parameters and disease severity scale measures. The study was registered with the International Prospective Register of Systematic Reviews on May 2, 2018 (Registration: CRD42018092651) and the protocol was published in Systematic Reviews on January 8, 2019. RESULTS: We included 36 articles in the systematic review and pooled 12 for the meta-analysis. The risk of bias was moderate, with only 2 articles (none included in the meta-analysis) showing a bias score<50%. Among protocols tested, 2 were predominant (the Timed Up and Go test and 6-min walk test). Speed, step length and step time with IMUs were significantly correlated with the Expanded Disability Status Scale (EDSS) score, and speed and step length were significantly correlated with the Multiple Sclerosis Walking Scale-12 score. CONCLUSION: IMU measurement has the potential to increase the sensitivity of clinical and performance tests to identify evolution in gait alteration in MS. Kinematic parameters easily accessible with IMUs, such as speed, step length and step duration, can help follow up disease severity in MS individuals with low to medium EDSS score (1.0-4.5).

Selection of the Best Electroencephalogram Channel to Predict the Depth of Anesthesia.

Precise cerebral dynamics of action of the anesthetics are a challenge for neuroscientists. This explains why there is no gold standard for monitoring the Depth of Anesthesia (DoA) and why experimental studies may use several electroencephalogram (EEG) channels, ranging from 2 to 128 EEG-channels. Our study aimed at finding the scalp area providing valuable information about brain activity under general anesthesia (GA) to select the more optimal EEG channel to characterized the DoA. We included 30 patients undergoing elective, minor surgery under GA and used a 32-channel EEG to record their electrical brain activity. In addition, we recorded their physiological parameters and the BIS monitor. Each individual EEG channel data were processed to test their ability to differentiate awake from asleep states. Due to strict quality criteria adopted for the EEG data and the difficulties of the real-life setting of the study, only 8 patients recordings were taken into consideration in the final analysis. Using 2 classification algorithms, we identified the optimal channels to discriminate between asleep and awake states: the frontal and temporal F8 and T7 were retrieved as being the two bests channels to monitor DoA. Then, using only data from the F8 channel, we tried to minimize the number of features required to discriminate between the awake and asleep state. The best algorithm turned out to be the Gaussian Naïve Bayes (GNB) requiring only 5 features (Area Under the ROC Curve - AUC- of 0.93 ± 0.04). This finding may pave the way to improve the assessment of DoA by combining one EEG channel recordings with a multimodal physiological monitoring of the brain state under GA. Further work is needed to see if these results may be valid to asses the depth of sedation in ICU.