Kinematic Analysis of Lower Limb Joint Asymmetry during Gait in People with Multiple Sclerosis

The majority of people with Multiple Sclerosis (pwMS), report lower limb motor dysfunc- tions, which may relevantly affect postural control, gait and a wide range of activities of daily living. While it is quite common to observe a different impact of the disease on the two limbs (i.e., one of them is more affected), less clear are the effects of such asymmetry on gait performance. The present retrospective cross-sectional study aimed to characterize the magnitude of interlimb asymmetry in pwMS, particularly as regards the joint kinematics, using parameters derived from angle-angle di- agrams. To this end, we analyzed gait patterns of 101 pwMS (55 women, 46 men, mean age 46.3, average Expanded Disability Status Scale (EDSS) score 3.5, range 1–6.5) and 81 unaffected individ- uals age- and sex-matched who underwent 3D computerized gait analysis carried out using an eight-camera motion capture system. Spatio-temporal parameters and kinematics in the sagittal plane at hip, knee and ankle joints were considered for the analysis. The angular trends of left and right sides were processed to build synchronized angle–angle diagrams (cyclograms) for each joint, and symmetry was assessed by computing several geometrical features such as area, orientation and Trend Symmetry. Based on cyclogram orientation and Trend Symmetry, the results show that pwMS exhibit significantly greater asymmetry in all three joints with respect to unaffected individ- uals. In particular, orientation values were as follows: 5.1 of pwMS vs. 1.6 of unaffected individuals at hip joint, 7.0 vs. 1.5 at knee and 6.4 vs. 3.0 at ankle (p < 0.001 in all cases), while for Trend Sym- metry we obtained at hip 1.7 of pwMS vs. 0.3 of unaffected individuals, 4.2 vs. 0.5 at knee and 8.5 vs. 1.5 at ankle (p < 0.001 in all cases). Moreover, the same parameters were sensitive enough to discriminate individuals of different disability levels. With few exceptions, all the calculated sym- metry parameters were found significantly correlated with the main spatio-temporal parameters of gait and the EDSS score. In particular, large correlations were detected between Trend Symmetry and gait speed (with rho values in the range of –0.58 to –0.63 depending on the considered joint, p < 0.001) and between Trend Symmetry and EDSS score (rho = 0.62 to 0.69, p < 0.001). Such results suggest not only that MS is associated with significantly marked interlimb asymmetry during gait but also that such asymmetry worsens as the disease progresses and that it has a relevant impact on gait performances

Footwear-integrated force sensing resistor sensors: A machine learning approach for categorizing lower limb disorders

Lower limb disorders are a substantial contributor to both disability and lower standards of life. The prevalent disorders affecting the lower limbs include osteoarthritis of the knee, hip, and ankle. The present study focuses on the use of footwear that incorporates force-sensing resistor sensors to classify lower limb disorders affecting the knee, hip, and ankle joints. The research collected data from a sample of 117 participants who wore footwear integrated with force-sensing resistor sensors while walking on a predetermined walkway of 9 meters. Extensive preprocessing and feature extraction techniques were applied to form a structured dataset. Several machine learning classifiers were trained and evaluated. According to the findings, the Random Forest model exhibited the highest level of performance on the balanced dataset with an accuracy rate of 96%, while the Decision Tree model achieved an accuracy rate of 91%. The accuracy scores of the Logistic Regression, Gaussian Naive Bayes, and Long Short-Term Memory models were comparatively lower. K-fold cross-validation was also performed to evaluate the models’ performance. The results indicate that the integration of force-sensing resistor sensors into footwear, along with the use of machine learning techniques, can accurately categorize lower limb disorders. This offers valuable information for developing customized interventions and treatment plans

Applications of EMG in Clinical and Sports Medicine

This second of two volumes on EMG (Electromyography) covers a wide range of clinical applications, as a complement to the methods discussed in volume 1. Topics range from gait and vibration analysis, through posture and falls prevention, to biofeedback in the treatment of neurologic swallowing impairment. The volume includes sections on back care, sports and performance medicine, gynecology/urology and orofacial function. Authors describe the procedures for their experimental studies with detailed and clear illustrations and references to the literature. The limitations of SEMG measures and methods for careful analysis are discussed. This broad compilation of articles discussing the use of EMG in both clinical and research applications demonstrates the utility of the method as a tool in a wide variety of disciplines and clinical fields

Explaining Deep Learning Models for Age-related Gait Classification based on time series acceleration

Gait analysis holds significant importance in monitoring daily health, particularly among older adults. Advancements in sensor technology enable the capture of movement in real-life environments and generate big data. Machine learning, notably deep learning (DL), shows promise to use these big data in gait analysis. However, the inherent black-box nature of these models poses challenges for their clinical application. This study aims to enhance transparency in DL-based gait classification for aged-related gait patterns using Explainable Artificial Intelligence, such as SHAP. A total of 244 subjects, comprising 129 adults and 115 older adults (age>65), were included. They performed a 3-minute walking task while accelerometers were affixed to the lumbar segment L3. DL models, convolutional neural network (CNN) and gated recurrent unit (GRU), were trained using 1-stride and 8-stride accelerations, respectively, to classify adult and older adult groups. SHAP was employed to explain the models' predictions. CNN achieved a satisfactory performance with an accuracy of 81.4% and an AUC of 0.89, and GRU demonstrated promising results with an accuracy of 84.5% and an AUC of 0.94. SHAP analysis revealed that both CNN and GRU assigned higher SHAP values to the data from vertical and walking directions, particularly emphasizing data around heel contact, spanning from the terminal swing to loading response phases. Furthermore, SHAP values indicated that GRU did not treat every stride equally. CNN accurately distinguished between adults and older adults based on the characteristics of a single stride's data. GRU achieved accurate classification by considering the relationships and subtle differences between strides. In both models, data around heel contact emerged as most critical, suggesting differences in acceleration and deceleration patterns during walking between different age groups

Nonlinear and factorization methods for the non-invasive investigation of the central nervous system

This thesis focuses on the functional study of the Central Nervous System (CNS) with non-invasive techniques. Two different aspects are investigated: nonlinear aspects of the cerebrovascular system, and the muscle synergies model for motor control strategies. The main objective is to propose novel protocols, post-processing procedures or indices to enhance the analysis of cerebrovascular system and human motion analysis with noninvasive devices or wearable sensors in clinics and rehabilitation. We investigated cerebrovascular system with Near-infrared Spectroscopy (NIRS), a technique measuring blood oxygenation at the level of microcirculation, whose modification reflects cerebrovascular response to neuronal activation. NIRS signal was analyzed with nonlinear methods, because some physiological systems, such as neurovascular coupling, are characterized by nonlinearity. We adopted Empirical Mode Decomposition (EMD) to decompose signal into a finite number of simple functions, called Intrinsic Mode Functions (IMF). For each IMF, we computed entropy-based features to characterize signal complexity and variability. Nonlinear features of the cerebrovascular response were employed to characterize two treatments. Firstly, we administered a psychotherapy called eye movement desensitization and reprocessing (EMDR) to two groups of patients. The first group performed therapy with eye movements, the second without. NIRS analysis with EMD and entropy-based features revealed a different cerebrovascular pattern between the two groups, that may indicate the efficacy of the psychotherapy when administered with eye movements. Secondly, we administered ozone autohemotherapy to two groups of subjects: a control group of healthy subjects and a group of patients suffering by multiple sclerosis (MS). We monitored the microcirculation with NIRS from oxygen-ozone injection up 1.5 hours after therapy, and 24 hours after therapy. We observed that, after 1.5 hours after the ozonetherapy, oxygenation levels improved in both groups, that may indicate that ozonetherapy reduced oxidative stress level in MS patients. Furthermore, we observed that, after ozonetherapy, autoregulation improved in both groups, and that the beneficial effects of ozonetherapy persisted up to 24 hours after the treatment in MS patients. Due to the complexity of musculoskeletal system, CNS adopts strategies to efficiently control the execution of motor tasks. A model of motor control are muscle synergies, defined as functional groups of muscles recruited by a unique central command. Human locomotion was the object of investigation, due to its importance for daily life and the cyclicity of the movement. Firstly, by exploiting features provided from statistical gait analysis, we investigated consistency of muscle synergies. We demonstrated that synergies are highly repeatable within-subjects, reinforcing the hypothesis of modular control in motor performance. Secondly, in locomotion, we distinguish principal from secondary activations of electromyography. Principal activations are necessary for the generation of the movement. Secondary activations generate supplement movements, for instance slight balance correction. We investigated the difference in the motor control strategies underlying muscle synergies of principal (PS) and secondary (SS) activations. We found that PS are constituted by a few modules with many muscles each, whereas SS are described by more modules than PS with one or two muscles each. Furthermore, amplitude of activation signals of PS is higher than SS. Finally, muscle synergies were adopted to investigate the efficacy of rehabilitation of stiffed-leg walking in lower back pain (LBP). We recruited a group of patients suffering from non-specific LBP stiffening the leg at initial contact. Muscle synergies during gait were extracted before and after rehabilitation. Our results showed that muscles recruitment and consistency of synergies improved after the treatment, showing that the rehabilitation may affect motor control strategies

Advances in Neural Signal Processing

Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brain–machine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications

Advances in Neural Signal Processing

Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brain–machine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications