Innovative multidimensional gait evaluation using IMU in multiple sclerosis: introducing the semiogram.

Background: Quantifying gait using inertial measurement units has gained increasing interest in recent years. Highly degraded gaits, especially in neurological impaired patients, challenge gait detection algorithms and require specific segmentation and analysis tools. Thus, the outcomes of these devices must be rigorously tested for both robustness and relevancy in order to recommend their routine use. In this study, we propose a multidimensional score to quantify and visualize gait, which can be used in neurological routine follow-up. We assessed the reliability and clinical coherence of this method in a group of severely disabled patients with progressive multiple sclerosis (pMS), who display highly degraded gait patterns, as well as in an age-matched healthy subjects (HS) group. Methods: Twenty-two participants with pMS and nineteen HS were included in this 18-month longitudinal follow-up study. During the follow-up period, all participants completed a 10-meter walk test with a U-turn and back, twice at M0, M6, M12, and M18. Average speed and seven clinical criteria (sturdiness, springiness, steadiness, stability, smoothness, synchronization, and symmetry) were evaluated using 17 gait parameters selected from the literature. The variation of these parameters from HS values was combined to generate a multidimensional visual tool, referred to as a semiogram. Results: For both cohorts, all criteria showed moderate to very high test-retest reliability for intra-session measurements. Inter-session quantification was also moderate to highly reliable for all criteria except smoothness, which was not reliable for HS participants. All partial scores, except for the stability score, differed between the two populations. All partial scores were correlated with an objective but not subjective quantification of gait severity in the pMS population. A deficit in the pyramidal tract was associated with altered scores in all criteria, whereas deficits in cerebellar, sensitive, bulbar, and cognitive deficits were associated with decreased scores in only a subset of gait criteria. Conclusions: The proposed multidimensional gait quantification represents an innovative approach to monitoring gait disorders. It provides a reliable and informative biomarker for assessing the severity of gait impairments in individuals with pMS. Additionally, it holds the potential for discriminating between various underlying causes of gait alterations in pMS.

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.

Evaluation methods to assess the efficacy of equinovarus foot surgery on the gait of post-stroke hemiplegic patients: A literature review.

Introduction: The aim of this study was to realize a systematic review of the different ways, both clinical and instrumental, used to evaluate the effects of the surgical correction of an equinovarus foot (EVF) deformity in post-stroke patients. Methods: A systematic search of full-length articles published from 1965 to June 2021 was performed in PubMed, Embase, CINAHL, Cochrane, and CIRRIE. The identified studies were analyzed to determine and to evaluate the outcomes, the clinical criteria, and the ways used to analyze the impact of surgery on gait pattern, instrumental, or not. Results: A total of 33 studies were included. The lack of methodological quality of the studies and their heterogeneity did not allow for a valid meta-analysis. In all, 17 of the 33 studies involved exclusively stroke patients. Ten of the 33 studies (30%) evaluated only neurotomies, one study (3%) evaluated only tendon lengthening procedures, 19 studies (58%) evaluated tendon transfer procedures, and only two studies (6%) evaluated the combination of tendon and neurological procedures. Instrumental gait analysis was performed in only 11 studies (33%), and only six studies (18%) combined it with clinical and functional analyses. Clinical results show that surgical procedures are safe and effective. A wide variety of different scales have been used, most of which have already been validated in other indications. Discussion: Neuro-orthopedic surgery for post-stroke EVF is becoming better defined. However, the method of outcome assessment is not yet well established. The complexity in the evaluation of the gait of patients with EVF, and therefore the analysis of the effectiveness of the surgical management performed, requires the integration of a patient-centered functional dimension, and a reliable and reproducible quantified gait analysis, which is routinely usable clinically if possible.

Pandemic lockdown, isolation, and exit policies based on machine learning predictions.

The widespread lockdowns imposed in many countries at the beginning of the COVID-19 pandemic elevated the importance of research on pandemic management when medical solutions such as vaccines are unavailable. We present a framework that combines a standard epidemiological SEIR (susceptible-exposed-infected-removed) model with an equally standard machine learning classification model for clinical severity risk, defined as an individual's risk of needing intensive care unit (ICU) treatment if infected. Using COVID-19-related data and estimates for France as of spring 2020, we then simulate isolation and exit policies. Our simulations show that policies considering clinical risk predictions could relax isolation restrictions for millions of the lowest risk population months earlier while consistently abiding by ICU capacity restrictions. Exit policies without risk predictions, meanwhile, would considerably exceed ICU capacity or require the isolation of a substantial portion of population for over a year in order to not overwhelm the medical system. Sensitivity analyses further decompose the impact of various elements of our models on the observed effects. Our work indicates that predictive modeling based on machine learning and artificial intelligence could bring significant value to managing pandemics. Such a strategy, however, requires governments to develop policies and invest in infrastructure to operationalize personalized isolation and exit policies based on risk predictions at scale. This includes health data policies to train predictive models and apply them to all residents, as well as policies for targeted resource allocation to maintain strict isolation for high-risk individuals.

Adaptive Change-Point Detection for Studying Human Locomotion.

This paper presents an innovative method to analyze inertial signals recorded in a semi-controlled environment. It uses an adaptive and supervised change point detection procedure to decompose the signals into homogeneous segments, allowing a refined analysis of the successive phases within a gait protocol. Thanks to a training procedure, the algorithm can be applied to a wide range of protocols and handles different levels of granularity. The method is tested on a cohort of 15 healthy subjects performing a complex protocol composed of different activities and shows promising results for the automated and adaptive study of human gait and activity.Clinical relevance- A new approach to study human activity and locomotion in Free-Living Environments FLEs through an adaptive change-point detection which isolates homogeneous phases.

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.

Sleep apnea syndrome and subthalamic stimulation in Parkinson's disease.

OBJECTIVES: Τhe association between Parkinson's disease (PD) and sleep apnea syndrome (SAS) is not fully elucidated and very few studies reported on SAS outcome after deep brain stimulation (DBS). Here, we compare the clinical profile of PD patients with and without SAS and assess, for the first time, the value of pre-DBS SAS as predictor of post-DBS outcome in PD. METHODS: Fifty patients were grouped into PD with SAS (PD-SAS+,n = 22) and without (PD-SAS-,n = 28), based on the Apnea-Hypopnea-Index (AHI≥5) in polysomnography. We used novel multivariate statistical models to compare pre-DBS profiles and assess post-DBS motor, non-motor and quality of life (QoL) changes in both groups. RESULTS: In the entire cohort, 44% of patients had at least mild SAS (AHI≥5), while 22% had at least moderate (AHI≥15). Mean AHI was 11/h (NREM-AHI = 10.2/h and REM-AHI = 13.5/h). The two groups had equal demographics and PD characteristics, and did not differ in respect to unified Parkinson's disease rating scale (UPDRS)-IIOFF, Body-Mass-Index, polysomnographic features, RBD, depression, sleepiness and QoL scores. The PD-SAS+ group had significantly higher scores in UPDRS-IIIOFF (41.1 ± 17.7 vs. 30.9 ± 11.7,p < 0.05) compared to PD-SAS- group. The groups did not differ in respect to post-DBS change in UPDRS-II, UPDRS-III, Epworth sleepiness scale, Hamilton depression rating scale and PDQ39 scores. Positive airway pressure therapy had no impact on post-DBS outcome. CONCLUSIONS: In patients with PD and candidates for DBS, the presence of SAS is associated with increased motor signs, but not with a specific non-motor, QoL or sleep-wake profile. The presence of SAS prior to STN-DBS is not associated with worse outcome after surgery.

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.

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.

The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review.

This article presents an overview of fifty-eight articles dedicated to the evaluation of physical activity in free-living conditions using wearable motion sensors. This review provides a comprehensive summary of the technical aspects linked to sensors (types, number, body positions, and technical characteristics) as well as a deep discussion on the protocols implemented in free-living conditions (environment, duration, instructions, activities, and annotation). Finally, it presents a description and a comparison of the main algorithms and processing tools used for assessing physical activity from raw signals.

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.

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.

Effects of bilateral stimulation of the subthalamic nucleus in Parkinson's disease with and without REM sleep behaviour disorder.

BACKGROUND: Although rapid eye movement sleep behaviour disorder (RBD) in Parkinson's disease (PD) is associated with increased non-motor symptoms, its impact on the deep brain stimulation (DBS) outcome remains unclear. This is the first study to compare the post-DBS outcome between PD patients with RBD (PD-RBD+) and without (PD-RBD-). METHODS: We analysed data from PD patients who were treated with bilateral DBS in the nucleus subthalamicus. Assessments included night-polysomnography (only pre-DBS), and motor and non-motor assessments pre-DBS and post-DBS. RESULTS: Among 50 PD patients (29 males, mean age 62.5 years, 11.8 mean PD years), 24 (48%) had RBD. Pre-DBS, the two groups were equal in respect to sociodemographic features, disease duration and PD medications. A multivariate analysis showed that the clinical profile linked to motor, non-motor and quality of life features differed significantly between PD patients with and without RBD. The most discriminative elements were Unified Parkinson's Disease Rating Scale (UPDRS)-III, apathy and depression scores. Post-DBS, UPDRS-III, Epworth sleepiness scale and PD questionnaire improved significantly in both groups. UPDRS-II scores significantly improved in the PD-RBD+ group (-45%) but remained unchanged in the PD-RBD- group (-14%). The depression score improved significantly in the PD-RBD+ (-34%) and remained unchanged in the PD-RBD- group. The apathy score remained unchanged in the PD-RBD+ group but increased significantly in the PD-RBD- group (+33%). CONCLUSION: While pre-DBS, PD patients with and without RBD showed different clinical profiles, post-DBS, the clinical profiles were comparable between the two groups. In respect to depressive symptoms, apathy and activities of daily living, PD-RBD+ patients show favourable post-DBS outcome. These findings highlight the importance of RBD assessment prior to DBS surgery.

Template-Based Step Detection with Inertial Measurement Units.

This article presents a method for step detection from accelerometer and gyrometer signals recorded with Inertial Measurement Units (IMUs). The principle of our step detection algorithm is to recognize the start and end times of the steps in the signal thanks to a predefined library of templates. The algorithm is tested on a database of 1020 recordings, composed of healthy subjects and patients with various neurological or orthopedic troubles. Simulations on more than 40,000 steps show that the template-based method achieves remarkable results with a 98% recall and a 98% precision. The method adapts well to pathological subjects and can be used in a medical context for robust step estimation and gait characterization.

On the importance of local dynamics in statokinesigram: A multivariate approach for postural control evaluation in elderly.

The fact that almost one third of population >65 years-old has at least one fall per year, makes the risk-of-fall assessment through easy-to-use measurements an important issue in current clinical practice. A common way to evaluate posture is through the recording of the center-of-pressure (CoP) displacement (statokinesigram) with force platforms. Most of the previous studies, assuming homogeneous statokinesigrams in quiet standing, used global parameters in order to characterize the statokinesigrams. However the latter analysis provides little information about local characteristics of statokinesigrams. In this study, we propose a multidimensional scoring approach which locally characterizes statokinesigrams on small time-periods, or blocks, while highlighting those which are more indicative to the general individual's class (faller/non-faller). Moreover, this information can be used to provide a global score in order to evaluate the postural control and classify fallers/non-fallers. We evaluate our approach using the statokinesigram of 126 community-dwelling elderly (78.5 ± 7.7 years). Participants were recorded with eyes open and eyes closed (25 seconds each acquisition) and information about previous falls was collected. The performance of our findings are assessed using the receiver operating characteristics (ROC) analysis and the area under the curve (AUC). The results show that global scores provided by splitting statokinesigrams in smaller blocks and analyzing them locally, classify fallers/non-fallers more effectively (AUC = 0.77 ± 0.09 instead of AUC = 0.63 ± 0.12 for global analysis when splitting is not used). These promising results indicate that such methodology might provide supplementary information about the risk of fall of an individual and be of major usefulness in assessment of balance-related diseases such as Parkinson's disease.