Characterizing the Gait of People With Different Types of Amputation and Prosthetic Components Through Multimodal Measurements: A Methodological Perspective

Prosthetic gait implies the use of compensatory motor strategies, including alterations in gait biomechanics and adaptations in the neural control mechanisms adopted by the central nervous system. Despite the constant technological advancements in prostheses design that led to a reduction in compensatory movements and an increased acceptance by the users, a deep comprehension of the numerous factors that influence prosthetic gait is still needed. The quantitative prosthetic gait analysis is an essential step in the development of new and ergonomic devices and to optimize the rehabilitation therapies. Nevertheless, the assessment of prosthetic gait is still carried out by a heterogeneous variety of methodologies, and this limits the comparison of results from different studies, complicating the definition of shared and well-accepted guidelines among clinicians, therapists, physicians, and engineers. This perspective article starts from the results of a project funded by the Italian Worker's Compensation Authority (INAIL) that led to the generation of an extended dataset of measurements involving kinematic, kinetic, and electrophysiological recordings in subjects with different types of amputation and prosthetic components. By encompassing different studies published along the project activities, we discuss the specific information that can be extracted by different kinds of measurements, and we here provide a methodological perspective related to multimodal prosthetic gait assessment, highlighting how, for designing improved prostheses and more effective therapies for patients, it is of critical importance to analyze movement neural control and its mechanical actuation as a whole, without limiting the focus to one specific aspect

Multiscale Entropy Algorithms to Analyze Complexity and Variability of Trunk Accelerations Time Series in Subjects with Parkinson’s Disease

The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson's disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 healthy subjects (HS) were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors (t) 1-6. Differences between swPD and HS were calculated at each t, and the area under the receiver operating characteristics, optimal cutoff points, post-test probabilities, and diagnostic odds ratios were calculated. MSE, RCMSE, and CIs showed to differentiate swPD from HS. MSE in the anteroposterior direction at t4 and t5, and MSE in the ML direction at t4 showed to characterize the gait disorders of swPD with the best trade-off between positive and negative posttest probabilities and correlated with the motor disability, pelvic kinematics, and stance phase. Using a time series of 2000 data points, a scale factor of 4 or 5 in the MSE procedure can yield the best trade-off in terms of post-test probabilities when compared to other scale factors for detecting gait variability and complexity in swPD

Actigraphic Sensors Describe Stroke Severity in the Acute Phase: Implementing Multi-Parametric Monitoring in Stroke Unit

: Actigraphy is a tool used to describe limb motor activity. Some actigraphic parameters, namely Motor Activity (MA) and Asymmetry Index (AR), correlate with stroke severity. However, a long-lasting actigraphic monitoring was never performed previously. We hypothesized that MA and AR can describe different clinical conditions during the evolution of the acute phase of stroke. We conducted a multicenter study and enrolled 69 stroke patients. NIHSS was assessed every hour and upper limbs' motor activity was continuously recorded. We calculated MA and AR in the first hour after admission, after a significant clinical change (NIHSS ± 4) or at discharge. In a control group of 17 subjects, we calculated MA and AR normative values. We defined the best model to predict clinical status with multiple linear regression and identified actigraphic cut-off values to discriminate minor from major stroke (NIHSS ≥ 5) and NIHSS 5-9 from NIHSS ≥ 10. The AR cut-off value to discriminate between minor and major stroke (namely NIHSS ≥ 5) is 27% (sensitivity = 83%, specificity = 76% (AUC 0.86 p < 0.001), PPV = 89%, NPV = 42%). However, the combination of AR and MA of the non-paretic arm is the best model to predict NIHSS score (R2: 0.482, F: 54.13), discriminating minor from major stroke (sensitivity = 89%, specificity = 82%, PPV = 92%, NPV = 75%). The AR cut-off value of 53% identifies very severe stroke patients (NIHSS ≥ 10) (sensitivity = 82%, specificity = 74% (AUC 0.86 p < 0.001), PPV = 73%, NPV = 82%). Actigraphic parameters can reliably describe the overall severity of stroke patients with motor symptoms, supporting the addition of a wearable actigraphic system to the multi-parametric monitoring in stroke units

Engineering Reconnaissance Following the October 2016 Central Italy Earthquakes - Version 2

Between August and November 2016, three major earthquake events occurred in Central Italy. The first event, with M6.1, took place on 24 August 2016, the second (M5.9) on 26 October, and the third (M6.5) on 30 October 2016. Each event was followed by numerous aftershocks. As shown in Figure 1.1, this earthquake sequence occurred in a gap between two earlier damaging events, the 1997 M6.1 Umbria-Marche earthquake to the north-west and the 2009 M6.1 L’Aquila earthquake to the south-east. This gap had been previously recognized as a zone of elevated risk (GdL INGV sul terremoto di Amatrice, 2016). These events occurred along the spine of the Apennine Mountain range on normal faults and had rake angles ranging from -80 to -100 deg, which corresponds to normal faulting. Each of these events produced substantial damage to local towns and villages. The 24 August event caused massive damages to the following villages: Arquata del Tronto, Accumoli, Amatrice, and Pescara del Tronto. In total, there were 299 fatalities (www.ilgiornale.it), generally from collapses of unreinforced masonry dwellings. The October events caused significant new damage in the villages of Visso, Ussita, and Norcia, although they did not produce fatalities, since the area had largely been evacuated. The NSF-funded Geotechnical Extreme Events Reconnaissance (GEER) association, with co-funding from the B. John Garrick Institute for the Risk Sciences at UCLA and the NSF I/UCRC Center for Unmanned Aircraft Systems (C-UAS) at BYU, mobilized a US-based team to the area in two main phases: (1) following the 24 August event, from early September to early October 2016, and (2) following the October events, between the end of November and the beginning of December 2016. The US team worked in close collaboration with Italian researchers organized under the auspices of the Italian Geotechnical Society, the Italian Center for Seismic Microzonation and its Applications, the Consortium ReLUIS, Centre of Competence of Department of Civil Protection and the DIsaster RECovery Team of Politecnico di Torino. The objective of the Italy-US GEER team was to collect and document perishable data that is essential to advance knowledge of earthquake effects, which ultimately leads to improved procedures for characterization and mitigation of seismic risk. The Italy-US GEER team was multi-disciplinary, with expertise in geology, seismology, geomatics, geotechnical engineering, and structural engineering. The composition of the team was largely the same for the two mobilizations, particularly on the Italian side. Our approach was to combine traditional reconnaissance activities of on-ground recording and mapping of field conditions, with advanced imaging and damage detection routines enabled by state-of-the-art geomatics technology. GEER coordinated its reconnaissance activities with those of the Earthquake Engineering Research Institute (EERI), although the EERI mobilization to the October events was delayed and remains pending as of this writing (April 2017). For the August event reconnaissance, EERI focused on emergency response and recovery, in combination with documenting the effectiveness of public policies related to seismic retrofit. As such, GEER had responsibility for documenting structural damage patterns in addition to geotechnical effects. This report is focused on the reconnaissance activities performed following the October 2016 events. More information about the GEER reconnaissance activities and main findings following the 24 August 2016 event, can be found in GEER (2016). The objective of this document is to provide a summary of our findings, with an emphasis of documentation of data. In general, we do not seek to interpret data, but rather to present it as thoroughly as practical. Moreover, we minimize the presentation of background information already given in GEER (2016), so that the focus is on the effects of the October events. As such, this report and GEER (2016) are inseparable companion documents. Similar to reconnaissance activities following the 24 August 2016 event, the GEER team investigated earthquake effects on slopes, villages, and major infrastructure. Figure 1.2 shows the most strongly affected region and locations described subsequently pertaining to: 1. Surface fault rupture; 2. Recorded ground motions; 3. Landslides and rockfalls; 4. Mud volcanoes; 5. Investigated bridge structures; 6. Villages and hamlets for which mapping of building performance was performed

Reconnaissance of 2016 Central Italy Earthquake Sequence

The Central Italy earthquake sequence nominally began on 24 August 2016 with a M6.1 event on a normal fault that produced devastating effects in the town of Amatrice and several nearby villages and hamlets. A major international response was undertaken to record the effects of this disaster, including surface faulting, ground motions, landslides, and damage patterns to structures. This work targeted the development of high-value case histories useful to future research. Subsequent events in October 2016 exacerbated the damage in previously affected areas and caused damage to new areas in the north, particularly the relatively large town of Norcia. Additional reconnaissance after a M6.5 event on 30 October 2016 documented and mapped several large landslide features and increased damage states for structures in villages and hamlets throughout the region. This paper provides an overview of the reconnaissance activities undertaken to document and map these and other effects, and highlights valuable lessons learned regarding faulting and ground motions, engineering effects, and emergency response to this disaster

Machine Learning Approach to Support the Detection of Parkinson’s Disease in IMU-Based Gait Analysis

The aim of this study was to determine which supervised machine learning (ML) algorithm can most accurately classify people with Parkinson’s disease (pwPD) from speed-matched healthy subjects (HS) based on a selected minimum set of IMU-derived gait features. Twenty-two gait features were extrapolated from the trunk acceleration patterns of 81 pwPD and 80 HS, including spatiotemporal, pelvic kinematics, and acceleration-derived gait stability indexes. After a three-level feature selection procedure, seven gait features were considered for implementing five ML algorithms: support vector machine (SVM), artificial neural network, decision trees (DT), random forest (RF), and K-nearest neighbors. Accuracy, precision, recall, and F1 score were calculated. SVM, DT, and RF showed the best classification performances, with prediction accuracy higher than 80% on the test set. The conceptual model of approaching ML that we proposed could reduce the risk of overrepresenting multicollinear gait features in the model, reducing the risk of overfitting in the test performances while fostering the explainability of the results

Neurophysiology of cerebellar ataxias and gait disorders

There are numerous forms of cerebellar disorders from sporadic to genetic diseases. The aim of this chapter is to provide an overview of the advances and emerging techniques during these last 2 decades in the neurophysiological tests useful in cerebellar patients for clinical and research purposes. Clinically, patients exhibit various combinations of a vestibulocerebellar syndrome, a cerebellar cognitive affective syndrome and a cerebellar motor syndrome which will be discussed throughout this chapter. Cerebellar patients show abnormal Bereitschaftpotentials (BPs) and mismatch negativity. Cerebellar EEG is now being applied in cerebellar disorders to unravel impaired electrophysiological patterns associated within disorders of the cerebellar cortex. Eyeblink conditioning is significantly impaired in cerebellar disorders: the ability to acquire conditioned eyeblink responses is reduced in hereditary ataxias, in cerebellar stroke and after tumor surgery of the cerebellum. Furthermore, impaired eyeblink conditioning is an early marker of cerebellar degenerative disease. General rules of motor control suggest that optimal strategies are needed to execute voluntary movements in the complex environment of daily life. A high degree of adaptability is required for learning procedures underlying motor control as sensorimotor adaptation is essential to perform accurate goal-directed movements. Cerebellar patients show impairments during online visuomotor adaptation tasks. Cerebellum-motor cortex inhibition (CBI) is a neurophysiological biomarker showing an inverse association between cerebellothalamocortical tract integrity and ataxia severity. Ataxic gait is characterized by increased step width, reduced ankle joint range of motion, increased gait variability, lack of intra-limb inter-joint and inter-segmental coordination, impaired foot ground placement and loss of trunk control. Taken together, these techniques provide a neurophysiological framework for a better appraisal of cerebellar disorders

Actigraphic Measurement of the Upper Limbs for the Prediction of Ischemic Stroke Prognosis: An Observational Study

Background: It is often challenging to formulate a reliable prognosis for patients with acute ischemic stroke. The most accepted prognostic factors may not be sufficient to predict the recovery process. In this view, describing the evolution of motor deficits over time via sensors might be useful for strengthening the prognostic model. Our aim was to assess whether an actigraphic-based parameter (Asymmetry Rate Index for the 24 h period (AR2_24 h)) obtained in the acute stroke phase could be a predictor of a 90 d prognosis. Methods: In this observational study, we recorded and analyzed the 24 h upper limb movement asymmetry of 20 consecutive patients with acute ischemic stroke during their stay in a stroke unit. We recorded the motor activity of both arms using two programmable actigraphic systems positioned on patients’ wrists. We clinically evaluated the stroke patients by NIHSS in the acute phase and then assessed them across 90 days using the modified Rankin Scale (mRS). Results: We found that the AR2_24 h parameter positively correlates with the 90 d mRS (r = 0.69, p < 0.001). Moreover, we found that an AR2_24 h > 32% predicts a poorer outcome (90 d mRS > 2), with sensitivity = 100% and specificity = 89%. Conclusions: Sensor-based parameters might provide useful information for predicting ischemic stroke prognosis in the acute phase

An artificial neural network approach to detect presence and severity of Parkinson's disease via gait parameters.

IntroductionGait deficits are debilitating in people with Parkinson's disease (PwPD), which inevitably deteriorate over time. Gait analysis is a valuable method to assess disease-specific gait patterns and their relationship with the clinical features and progression of the disease.ObjectivesOur study aimed to i) develop an automated diagnostic algorithm based on machine-learning techniques (artificial neural networks [ANNs]) to classify the gait deficits of PwPD according to disease progression in the Hoehn and Yahr (H-Y) staging system, and ii) identify a minimum set of gait classifiers.MethodsWe evaluated 76 PwPD (H-Y stage 1-4) and 67 healthy controls (HCs) by computerized gait analysis. We computed the time-distance parameters and the ranges of angular motion (RoMs) of the hip, knee, ankle, trunk, and pelvis. Principal component analysis was used to define a subset of features including all gait variables. An ANN approach was used to identify gait deficits according to the H-Y stage.ResultsWe identified a combination of a small number of features that distinguished PwPDs from HCs (one combination of two features: knee and trunk rotation RoMs) and identified the gait patterns between different H-Y stages (two combinations of four features: walking speed and hip, knee, and ankle RoMs; walking speed and hip, knee, and trunk rotation RoMs).ConclusionThe ANN approach enabled automated diagnosis of gait deficits in several symptomatic stages of Parkinson's disease. These results will inspire future studies to test the utility of gait classifiers for the evaluation of treatments that could modify disease progression