Gait analysis and clinical correlations in early Parkinson's disease

The effectiveness of action observation and motor imagery in freezing of gait, speed, physical function and balance in Parkinson's disease: a systematic review and meta-analysis

BACKGROUND

Parkinson's Disease (PD) is a neurodegenerative disease that produces balance and gait disorders. Action observation (AO) and motor imagery (MI) therapies appear to facilitate motor planning influencing balance and gait relearning.

OBJECTIVE

To investigate the effectiveness of AO and MI in isolation or combined (AO-MI), compared to sham interventions for the improvement of freezing of gait (FOG), speed, physical function and balance among individuals with PD.

METHODS

PubMed, Web of science, PEDro, Scopus and Cochrane Library were searched from inception to January 2024. Studies included were randomized controlled trials (RCTs). The study quality and risk of bias were assessed with PEDro scale and the Cochrane tool, respectively. The certainty of evidence was evaluated with GRADEpro GDT.

RESULTS

Eight RCTs were included, with a methodological quality ranged from fair to high. There were statistically significant results in FOG at follow-up when comparing AO to sham intervention (SMD= -0.50, 95% CI -0.88, -0.11; I2: 0%) 3 studies, 107 participants). Interventions based on MI compared to sham intervention were statistically significant in speed at post-treatment (MD = -0.06, 95% CI -0.04, -0.08; I2: 0%) and balance at post-treatment (SMD = -0.97; 95% CI -1.79, -0.15).

CONCLUSIONS

Very low certainty of evidence was found proposing that: AO produce improvements in FOG at follow-up; and MI produce improvements in speed and balance at post-treatment.

Feasibility of differentiating gait in Parkinson's disease and spinocerebellar degeneration using a pose estimation algorithm in two-dimensional video

BACKGROUND

Although pose estimation algorithms have been used to analyze videos of patients with Parkinson's disease (PD) to assess symptoms, their feasibility for differentiating PD from other neurological disorders that cause gait disturbances has not been evaluated yet. We aimed to determine whether it was possible to differentiate between PD and spinocerebellar degeneration (SCD) by analyzing video recordings of patient gait using a pose estimation algorithm.

METHODS

We videotaped 82 patients with PD and 61 patients with SCD performing the timed up-and-go test. A pose estimation algorithm was used to extract the coordinates of 25 key points of the participants from these videos. A transformer-based deep neural network (DNN) model was trained to predict PD or SCD using the extracted coordinate data. We employed a leave-one-participant-out cross-validation method to evaluate the predictive performance of the trained model using accuracy, sensitivity, and specificity. As there were significant differences in age, weight, and body mass index between the PD and SCD groups, propensity score matching was used to perform the same experiment in a population that did not differ in these clinical characteristics.

RESULTS

The accuracy, sensitivity, and specificity of the trained model were 0.86, 0.94, and 0.75 for all participants and 0.83, 0.88, and 0.78 for the participants extracted by propensity score matching.

CONCLUSION

The differentiation of PD and SCD using key point coordinates extracted from gait videos and the DNN model was feasible and could be used as a collaborative tool in clinical practice and telemedicine.

The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review

Parkinson's disease (PD) is the second most common movement disorder in the world. It is characterized by motor and non-motor symptoms that have a profound impact on the independence and quality of life of people affected by the disease, which increases caregivers' burdens. The use of the quantitative gait data of people with PD and deep learning (DL) approaches based on gait are emerging as increasingly promising methods to support and aid clinical decision making, with the aim of providing a quantitative and objective diagnosis, as well as an additional tool for disease monitoring. This will allow for the early detection of the disease, assessment of progression, and implementation of therapeutic interventions. In this paper, the authors provide a systematic review of emerging DL techniques recently proposed for the analysis of PD by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Scopus, PubMed, and Web of Science databases were searched across an interval of six years (between 2018, when the first article was published, and 2023). A total of 25 articles were included in this review, which reports studies on the movement analysis of PD patients using both wearable and non-wearable sensors. Additionally, these studies employed DL networks for classification, diagnosis, and monitoring purposes. The authors demonstrate that there is a wide employment in the field of PD of convolutional neural networks for analyzing signals from wearable sensors and pose estimation networks for motion analysis from videos. In addition, the authors discuss current difficulties and highlight future solutions for PD monitoring and disease progression.

Examining the validity of smart glasses in measuring spatiotemporal parameters of gait among people with Parkinson's disease

BACKGROUND

Gait impairment is an early marker of Parkinson's disease (PD) and is frequently monitored to evaluate disease progression. Wearable sensors are increasingly being used to quantify gait in the real-world setting among people with PD (pwPD). Particularly, embedding wearables on devices or clothing that are worn daily may represent a useful strategy to improve compliance and regular monitoring of gait.

RESEARCH QUESTION

The current investigation examined the validity of innovative smart glasses to measure gait among pwPD.

METHODS

Participants wore the smart glasses and 6 APDM gait sensors simultaneously, while performing two walking tasks: the 3-meters Timed Up and Go test (TUG) and the 7-meters Stand and Walk (SAW) test. The following spatiotemporal gait parameters were calculated from the data collected using the two different devices: step time, step length, swing percentage, TUG duration, turn duration, and turn velocity.

RESULTS

A total of 31 pwPD (mean age=68.6±8.5 years; 35.48 % female(N=11), mean Unified Parkinson's Disease Rating Scale (UPDRS) total score=32.1±14.7) participated in the study. Smart glasses achieved high validity in measuring step time (ICC=0.92, p=0.01) and TUG duration (ICC=0.96, p=0.03) compared to APDM sensors. On the other hand, the smart glasses did not achieve adequate validity when measuring step length, swing percentage, turn duration or turn velocity.

SIGNIFICANCE

The current study suggests that smart glasses has the potential to measure TUG and step time in individuals living with PD. However, further research is needed to improve algorithms for sensors worn on the head.

Assessing gait dysfunction severity in Parkinson's Disease using 2-Stream Spatial-Temporal Neural Network

Parkinson's Disease (PD), a neurodegenerative disorder, significantly impacts the quality of life for millions of people worldwide. PD primarily impacts dopaminergic neurons in the brain's substantia nigra, resulting in dopamine deficiency and gait impairments such as bradykinesia and rigidity. Currently, several well-established tools, such as the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and Hoehn and Yahr (H&Y) Scale, are used for evaluating gait dysfunction in PD. While insightful, these methods are subjective, time-consuming, and often ineffective in early-stage diagnosis. Other methods using specialized sensors and equipment to measure movement disorders are cumbersome and expensive, limiting their accessibility. This study introduces a hierarchical approach to evaluating gait dysfunction in PD through videos. The novel 2-Stream Spatial-Temporal Neural Network (2S-STNN) leverages the spatial-temporal features from the skeleton and silhouette streams for PD classification. This approach achieves an accuracy rate of 89% and outperforms other state-of-the-art models. The study also employs saliency values to highlight critical body regions that significantly influence model decisions and are severely affected by the disease. For a more detailed analysis, the study investigates 21 specific gait attributes for a nuanced quantification of gait disorders. Parameters such as walking pace, step length, and neck forward angle are found to be strongly correlated with PD gait severity categories. This approach offers a comprehensive and convenient solution for PD management in clinical settings, enabling patients to receive a more precise evaluation and monitoring of their gait impairments.

Kinematic network of joint motion provides insight on gait coordination: An observational study on Parkinson's disease

The analysis of gait kinematics requires to encode and collapse multidimensional information from multiple anatomical elements. In this study, we address this issue by analyzing the joints' coordination during gait, borrowing from the framework of network theory. We recruited twenty-three patients with Parkinson's disease and twenty-three matched controls that were recorded during linear gait using a stereophotogrammetric motion analysis system. The three-dimensional angular velocity of the joints was used to build a kinematic network for each participant, and both global (average whole-body synchronization) and nodal (individual joint synchronization, i.e., nodal strength) were extracted. By comparing the two groups, the results showed lower coordination in patients, both at global and nodal levels (neck, shoulders, elbows, and hips). Furthermore, the nodal strength of the left elbow and right hip in the patients, as well as the average joints' nodal strength were significantly correlated with the clinical motor condition and were predictive of it. Our study highlights the importance of integrating whole-body information in kinematic analyses and the advantages of using network theory. Finally, the identification of altered network properties of specific joints, and their relationship with the motor impairment in the patients, suggests a potential clinical relevance for our approach.

Muscle Synergy Analysis as a Tool for Assessing the Effectiveness of Gait Rehabilitation Therapies: A Methodological Review and Perspective

According to the modular hypothesis for the control of movement, muscles are recruited in synergies, which capture muscle coordination in space, time, or both. In the last two decades, muscle synergy analysis has become a well-established framework in the motor control field and for the characterization of motor impairments in neurological patients. Altered modular control during a locomotion task has been often proposed as a potential quantitative metric for characterizing pathological conditions. Therefore, the purpose of this systematic review is to analyze the recent literature that used a muscle synergy analysis of neurological patients' locomotion as an indicator of motor rehabilitation therapy effectiveness, encompassing the key methodological elements to date. Searches for the relevant literature were made in Web of Science, PubMed, and Scopus. Most of the 15 full-text articles which were retrieved and included in this review identified an effect of the rehabilitation intervention on muscle synergies. However, the used experimental and methodological approaches varied across studies. Despite the scarcity of studies that investigated the effect of rehabilitation on muscle synergies, this review supports the utility of muscle synergies as a marker of the effectiveness of rehabilitative therapy and highlights the challenges and open issues that future works need to address to introduce the muscle synergies in the clinical practice and decisional process.

Combined diagnosis for Parkinson's disease via gait and eye movement disorders

BACKGROUND AND OBJECTIVES

With the discovery of the potential role of gait and eye movement disorders in Parkinson's disease (PD) recognition, we intend to investigate the combined diagnostic value of gait and eye movement disorders for PD.

METHODS

We enrolled some Chinese PD patients and healthy controls and separated them into the training and validation sets based on enrollment time. Performance in five oculomotor paradigms and in one gait paradigm was examined using an infrared eye tracking device and a wearable gait analysis device. We developed and validated a combined model for PD diagnosis via multivariate stepwise logistic regression analysis. Furthermore, subgroup comparisons and multi-model comparison were performed to assess its applicability and advantages.

RESULTS

A total of 145 PD patients and 80 healthy controls in China were recruited. The pro-saccade velocity, the trunk-sway max, and the turn mean angular velocity were finally screened out for the model development. Incorporating age factor, the ternary model demonstrated more satisfactory performance on ROC (AUC of 0.953 in the training set and AUC of 0.972 in the validation set), calibration curve, and decision curve. A nomogram was drawn to visualize the model. The combined model outperforms individual models with a broad application and the unique diagnostic value for early detection of PD patients, especially TD-PD patients.

CONCLUSION

We demonstrated the presence of gait and eye movement disorders, as well as the feasibility, applicability, and superiority of employing them together to diagnose PD.

Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics

Neurodegenerative conditions significantly impact patient quality of life. Many conditions do not have a cure, but with appropriate and timely treatment the advance of the disease could be diminished. However, many patients only seek a diagnosis once the condition progresses to a point at which the quality of life is significantly impacted. Effective non-invasive and readily accessible methods for early diagnosis can considerably enhance the quality of life of patients affected by neurodegenerative conditions. This work explores the potential of convolutional neural networks (CNNs) for patient gain freezing associated with Parkinson's disease. Sensor data collected from wearable gyroscopes located at the sole of the patient's shoe record walking patterns. These patterns are further analyzed using convolutional networks to accurately detect abnormal walking patterns. The suggested method is assessed on a public real-world dataset collected from parents affected by Parkinson's as well as individuals from a control group. To improve the accuracy of the classification, an altered variant of the recent crayfish optimization algorithm is introduced and compared to contemporary optimization metaheuristics. Our findings reveal that the modified algorithm (MSCHO) significantly outperforms other methods in accuracy, demonstrated by low error rates and high Cohen's Kappa, precision, sensitivity, and F1-measures across three datasets. These results suggest the potential of CNNs, combined with advanced optimization techniques, for early, non-invasive diagnosis of neurodegenerative conditions, offering a path to improve patient quality of life.

Characterizing ambulatory function in children with PPP2R5D-related neurodevelopmental disorder

BACKGROUND

Individuals with PPP2R5D-related neurodevelopmental disorder have an atypical gait pattern characterized by ataxia and incoordination. Structured, quantitative assessments are needed to further understand the impact of these impairments on function.

RESEARCH QUESTION

How do gait parameters and ambulatory function of individuals with PPP2R5D-related neurodevelopmental disorder compare to age and sex matched healthy norms?

METHODS

Twenty-six individuals with PPP2R5D pathogenic genetic variants participated in this observational, single visit study. Participants completed at least one of the following gait assessments: quantitative gait analysis at three different speeds (preferred pace walking (PPW), fast paced walking (FPW) and running, six-minute walk test (6MWT), 10-meter walk run (10MWR), and timed up and go (TUG). Descriptive statistics were used to summarize gait variables. Percent of predicted values were calculated using published norms. Paired t-tests and regression analyses were used to compare gait variables.

RESULTS

The median age of the participants was 8 years (range 4-27) and eighteen (69.2 %) were female. Individuals with PPP2R5D-related neurodevelopmental disorder walked slower and with a wider base of support than predicted for their age and sex. Stride velocity ranged from 48.9 % to 70.1 % and stride distance from 58.5 % to 81.9 % of predicted during PPW. Percent of predicted distance walked on the 6MWT ranged from 30.6 % to 71.1 % representing varied walking impairment. Increases in stride distance, not cadence, were associated with changes in stride velocity in FPW (R2 = 0.675, p =< 0.001) and running conditions (R2 = 0.918, p =< 0.001).

SIGNIFICANCE

We quantitatively assessed the abnormal gait in individuals with PPP2R5D-related neurodevelopmental disorder. These impairments may affect ability to adapt to environmental changes and participation in daily life. Rehabilitative interventions targeting gait speed and balance may improve function and safety for individuals with PPP2R5D-related neurodevelopmental disorder.

Adaptive gait responses to varying weight-bearing conditions: Inferences from gait dynamics and H-reflex magnitude

This study investigates the effects of varying loading conditions on excitability in neural pathways and gait dynamics. We focussed on evaluating the magnitude of the Hoffman reflex (H-reflex), a neurophysiological measure representing the capability to activate motor neurons and the timing and placement of the foot during walking. We hypothesized that weight manipulation would alter H-reflex magnitude, footfall and lower body kinematics. Twenty healthy participants were recruited and subjected to various weight-loading conditions. The H-reflex, evoked by stimulating the tibial nerve, was assessed from the dominant leg during walking. Gait was evaluated under five conditions: body weight, 20% and 40% additional body weight, and 20% and 40% reduced body weight (via a harness). Participants walked barefoot on a treadmill under each condition, and the timing of electrical stimulation was set during the stance phase shortly after the heel strike. Results show that different weight-loading conditions significantly impact the timing and placement of the foot and gait stability. Weight reduction led to a 25% decrease in double limb support time and an 11% narrowing of step width, while weight addition resulted in an increase of 9% in step width compared to body weight condition. Furthermore, swing time variability was higher for both the extreme weight conditions, while the H-reflex reduced to about 45% between the extreme conditions. Finally, the H-reflex showed significant main effects on variability of both stance and swing phases, indicating that muscle-motor excitability might serve as feedback for enhanced regulation of gait dynamics under challenging conditions.

Wearable sensor devices can automatically identify the ON-OFF status of patients with Parkinson's disease through an interpretable machine learning model

Introduction

Accurately and objectively quantifying the clinical features of Parkinson's disease (PD) is crucial for assisting in diagnosis and guiding the formulation of treatment plans. Therefore, based on the data on multi-site motor features, this study aimed to develop an interpretable machine learning (ML) model for classifying the "OFF" and "ON" status of patients with PD, as well as to explore the motor features that are most associated with changes in clinical symptoms.

Methods

We employed a support vector machine with a recursive feature elimination (SVM-RFE) algorithm to select promising motion features. Subsequently, 12 ML models were constructed based on these features, and we identified the model with the best classification performance. Then, we used the SHapley Additive exPlanations (SHAP) and the Local Interpretable Model agnostic Explanations (LIME) methods to explain the model and rank the importance of those motor features.

Results

A total of 96 patients were finally included in this study. The naive Bayes (NB) model had the highest classification performance (AUC = 0.956; sensitivity = 0.8947, 95% CI 0.6686-0.9870; accuracy = 0.8421, 95% CI 0.6875-0.9398). Based on the NB model, we analyzed the importance of eight motor features toward the classification results using the SHAP algorithm. The Gait: range of motion (RoM) Shank left (L) (degrees) [Mean] might be the most important motor feature for all classification horizons.

Conclusion

The symptoms of PD could be objectively quantified. By utilizing suitable motor features to construct ML models, it became possible to intelligently identify whether patients with PD were in the "ON" or "OFF" status. The variations in these motor features were significantly correlated with improvement rates in patients' quality of life. In the future, they might act as objective digital biomarkers to elucidate the changes in symptoms observed in patients with PD and might be used to assist in the diagnosis and treatment of patients with PD.

Investigating underlying brain structures and influence of mild and subjective cognitive impairment on dual-task performance in people with Parkinson's disease

Cognitive impairment can affect dual-task abilities in Parkinson's disease (PD), but it remains unclear whether this is also driven by gray matter alterations across different cognitive classifications. Therefore, we investigated associations between dual-task performance during gait and functional mobility and gray matter alterations and explored whether these associations differed according to the degree of cognitive impairment. Participants with PD were classified according to their cognitive function with 22 as mild cognitive impairment (PD-MCI), 14 as subjective cognitive impairment (PD-SCI), and 20 as normal cognition (PD-NC). Multiple regression models associated dual-task absolute and interference values of gait speed, step-time variability, and reaction time, as well as dual-task absolute and difference values for Timed Up and Go (TUG) with PD cognitive classification. We repeated these regressions including the nucleus basalis of Meynert, dorsolateral prefrontal cortex, and hippocampus. We additionally explored whole-brain regressions with dual-task measures to identify dual-task-related regions. There was a trend that cerebellar alterations were associated with worse TUG dual-task in PD-SCI, but also with higher dual-task gait speed and higher dual-task step-time variability in PD-NC. After multiple comparison corrections, no effects of interest were significant. In summary, no clear set of variables associated with dual-task performance was found that distinguished between PD cognitive classifications in our cohort. Promising but non-significant trends, in particular regarding the TUG dual-task, do however warrant further investigation in future large-scale studies.

Gait alterations in patients with adult spinal deformity

Background

Adult spinal deformity patients (ASD) experience altered spinal alignment affecting spatiotemporal parameters and joint kinematics. Differences in spinal deformity between patients with symptomatic idiopathic scoliosis (ID-ASD) and patients with "de novo" scoliosis (DN-ASD) may affect gait characteristics differently. This study aims to compare gait characteristics between ID-ASD, DN-ASD, and asymptomatic healthy matched controls.

Methods

In this observational case-control study, ID-ASD (n = 24) and DN-ASD (n = 26) patients visiting the out-patient spine clinic and scheduled for long-segment spinal fusion were included. Patients were matched, based on age, gender, leg length and BMI, with asymptomatic healthy controls. Gait was measured at comfortable walking speed on an instrumented treadmill with 3D motion capture system. Trunk, pelvic and lower extremities range of motion (ROM) and spatiotemporal parameters (SPT) are presented as median (first and thirds quartile). Independent t-test or Mann-Whitney U test was used to compare ID-ASD, DN-ASD and controls. Statistical Parametric Mapping (independent t-test) was used to compare 3D joint kinematics.

Results

DN-ASD patients walk with increased anterior trunk tilt during the whole gait cycle compared with ID-ASD patients and controls. ID-ASD walk with decreased trunk lateroflexion compared with DN-ASD and controls. DN-ASD showed decreased pelvic obliquity and -rotation, increased knee flexion, and decreased ankle plantar flexion. ID-ASD and DN-ASD displayed decreased trunk, pelvic and lower extremity ROM compared with controls, but increased pelvic tilt ROM. ID-ASD patients walked with comparable SPT to controls, whereas DN-ASD patients walked significantly slower with corresponding changes in SPT and wider steps.

Conclusions

DN-ASD patients exhibit distinct alterations in SPT and kinematic gait characteristics compared with ID-ASD and controls. These alterations seem to be predominantly influenced by sagittal spinal malalignment and kinematic findings in ASD patients should not be generalized as such, but always be interpreted with consideration for the nature of the ASD.

Gray matter loss relates to dual task gait in Lewy body disorders and aging

BACKGROUND

Within the spectrum of Lewy body disorders (LBD), both Parkinson's disease (PD) and dementia with Lewy bodies (DLB) are characterized by gait and balance disturbances, which become more prominent under dual-task (DT) conditions. The brain substrates underlying DT gait variations, however, remain poorly understood in LBD.

OBJECTIVE

To investigate the relationship between gray matter volume loss and DT gait variations in LBD.

METHODS

Seventy-nine participants including cognitively unimpaired PD, PD with mild cognitive impairment, PD with dementia (PDD), or DLB and 20 cognitively unimpaired controls were examined across a multi-site study. PDD and DLB were grouped together for analyses. Differences in gait speed between single and DT conditions were quantified by dual task cost (DTC). Cortical, subcortical, ventricle, and cerebellum brain volumes were obtained using FreeSurfer. Linear regression models were used to examine the relationship between gray matter volumes and DTC.

RESULTS

Smaller amygdala and total cortical volumes, and larger ventricle volumes were associated with a higher DTC across LBD and cognitively unimpaired controls. No statistically significant interaction between group and brain volumes were found. Adding cognitive and motor covariates or white matter hyperintensity volumes separately to the models did not affect brain volume and DTC associations.

CONCLUSION

Gray matter volume loss is associated with worse DT gait performance compared to single task gait, across cognitively unimpaired controls through and the LBD spectrum. Impairment in DT gait performance may be driven by age-related cortical neurodegeneration.

Automatic selection model to identify neurodegenerative diseases

Objective

This study evaluates machine learning algorithms' effectiveness in classifying Parkinson's disease and Huntington's disease based on biomarker data obtained non-invasively from patients and healthy controls.

Methods

Datasets containing biomarker data (x, y, and z values of accelerometers) from sensors were collected from Parkinson's disease, Huntington's disease patients, and healthy controls. An automatic selection model method was implemented for disease classification, using a unique Mexican database of human gait biomarkers, which we consider the only one of its kind. Random forest, random subspace method, and K-star algorithms were employed, with parameters optimized through an automated model selection.

Results

The study achieved a 0.893 precision rate for Parkinson's disease and Huntington's disease using the random subspace method. The findings underscore the potential of machine learning techniques in medical diagnosis, particularly in neurological disorders.

Conclusion

The automatic selection model method demonstrated efficacy in classifying Parkinson's disease and Huntington's disease based on non-invasive biomarker data. This research contributes to advancing non-invasive diagnostic approaches in neurological disorders, highlighting the significance of machine learning in healthcare.

Automatic labeling of Parkinson's Disease gait videos with weak supervision

Motor dysfunction in Parkinson's Disease (PD) patients is typically assessed by clinicians employing the Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Such comprehensive clinical assessments are time-consuming, expensive, semi-subjective, and may potentially result in conflicting labels across different raters. To address this problem, we propose an automatic, objective, and weakly-supervised method for labeling PD patients' gait videos. The proposed method accepts videos of patients and classifies their gait scores as normal (Gait score in MDS-UPDRS = 0) or PD (MDS-UPDRS ≥ 1). Unlike previous work, the proposed method does not require a priori MDS-UPDRS ratings for training, utilizing only domain-specific knowledge obtained from neurologists. We propose several labeling functions that classify patients' gait and use a generative model to learn the accuracy of each labeling function in a self-supervised manner. Since results depended upon the estimated values of the patients' 3D poses, and existing pre-trained 3D pose estimators did not yield accurate results, we propose a weakly-supervised 3D human pose estimation method for fine-tuning pre-trained models in a clinical setting. Using leave-one-out evaluations, the proposed method obtains an accuracy of 89% on a dataset of 29 PD subjects - a significant improvement compared to previous work by 7%-10% depending upon the dataset. The method obtained state-of-the-art results on the Human3.6M dataset. Our results suggest that the use of labeling functions may provide a robust means to interpret and classify patient-oriented videos involving motor tasks.

The impact of Three-Dimensional Gait Analysis in adults with pathological gait on management recommendations

BACKGROUND

Three-Dimensional Gait Analysis (3DGA) is a gold standard tool that can help identify pathological components of walking patterns. It has been well established that this tool influences the treatment decision making of clinicians treating paediatric patients with Cerebral Palsy, but it has not been established whether this tool changes decision making of clinicians treating adults with complex pathological gait.

RESEARCH QUESTION

To investigate the impact of pre-treatment 3DGA on treatment plans and management of adults with complex pathological gait.

METHOD

This retrospective audit examined the medical records of 87 patients undergoing pre-treatment 3DGA between 2014 and 2019. The review collected treatment plans from the initial referral, the post-gait analysis multidisciplinary report, and post-intervention progress notes with consistencies and differences noted throughout the care pathway.

RESULTS

Treatment plans of patients were altered in 80 % (N = 32) of patients following 3DGA assessment and recommendations. These treatment plan alterations included a change in surgery or avoidance of surgery, changes in orthosis prescriptions, casting or rehabilitation; and administration or changes in administration of Botulinum Neurotoxin (BoNT-A). In 47 % (N = 15) of cases the change in plans represented a de-escalation in intervention requirements (e.g. BoNT-A in lieu of surgical intervention), and in 31 % (N = 10) the change in plans represented an escalation in intervention requirements (e.g. requirement for surgery). These changes in treatment plans were either fully or partly enacted by the referring consultant in 86 % of cases.

SIGNIFICANCE

Pre-treatment 3DGA impacts the management of adult patients with complex pathological gait and facilitates patients potentially avoiding unnecessary interventions. Further investigation is needed to determine the cost effectiveness of 3DGA in this population and the impact of pre-treatment 3DGA on management outcomes.

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.

Objective measurement versus clinician-based assessment for Parkinson's disease

INTRODUCTION

Although clinician-based assessment through standardized clinical rating scales is currently the gold standard for quantifying motor impairment in Parkinson's disease (PD), it is not without limitations, including intra- and inter-rater variability and a degree of approximation. There is increasing evidence supporting the use of objective motion analyses to complement clinician-based assessment. Objective measurement tools hold significant potential for improving the accuracy of clinical and research-based evaluations of patients.

AREAS COVERED

The authors provide several examples from the literature demonstrating how different motion measurement tools, including optoelectronics, contactless and wearable systems allow for both the objective quantification and monitoring of key motor symptoms (such as bradykinesia, rigidity, tremor, and gait disturbances), and the identification of motor fluctuations in PD patients. Furthermore, they discuss how, from a clinician's perspective, objective measurements can help in various stages of PD management.

EXPERT OPINION

In our opinion, sufficient evidence supports the assertion that objective monitoring systems enable accurate evaluation of motor symptoms and complications in PD. A range of devices can be utilized not only to support diagnosis but also to monitor motor symptom during the disease progression and can become relevant in the therapeutic decision-making process.

Gait Analysis in Neurorehabilitation: From Research to Clinical Practice

When brain damage occurs, gait and balance are often impaired. Evaluation of the gait cycle, therefore, has a pivotal role during the rehabilitation path of subjects who suffer from neurological disorders. Gait analysis can be performed through laboratory systems, non-wearable sensors (NWS), and/or wearable sensors (WS). Using these tools, physiotherapists and neurologists have more objective measures of motion function and can plan tailored and specific gait and balance training early to achieve better outcomes and improve patients' quality of life. However, most of these innovative tools are used for research purposes (especially the laboratory systems and NWS), although they deserve more attention in the rehabilitation field, considering their potential in improving clinical practice. In this narrative review, we aimed to summarize the most used gait analysis systems in neurological patients, shedding some light on their clinical value and implications for neurorehabilitation practice.

Predicting the fatigue in Parkinson's disease using inertial sensor gait data and clinical characteristics

Objectives

The study aimed to analyze the clinical features and gait characteristics of patients with Parkinson's disease (PD) who also suffer from fatigue and to develop a model that can help identify fatigue states in the early stages of PD.

Methodology

A total of 81 PD patients have been enrolled for the Parkinson's Fatigue Scale (PFS-16) assessment and divided into two groups: patients with or without fatigue. Neuropsychological assessments of the two groups, including motor and non-motor symptoms, were collected. The patient's gait characteristics were collected using a wearable inertial sensor device.

Results

PD patients who experienced fatigue had a more significant impairment of motor symptoms than those who did not, and the experience of fatigue became more pronounced as the disease progressed. Patients with fatigue had more significant mood disorders and sleep disturbances, which can lead to a poorer quality of life. PD patients with fatigue had shorter step lengths, lower velocity, and stride length and increased stride length variability. As for kinematic parameters, PD patients with fatigue had lower shank-forward swing max, trunk-max sagittal angular velocity, and lumbar-max coronal angular velocity than PD patients without fatigue. The binary logistic analysis found that Movement Disorder Society-Unified Parkinson's Disease Rating Scale-I (MDS-UPDRS-I) scores, Hamilton Depression Scale (HAMD) scores, and stride length variability independently predicted fatigue in PD patients. The area under the curve (AUC) of these selected factors in the receiver operating characteristic (ROC) analysis was 0.900. Moreover, HAMD might completely mediate the association between Hamilton Anxiety Scale (HAMA) scores and fatigue (indirect effect: β = 0.032, 95% confidence interval: 0.001-0.062), with a percentage of mediation of 55.46%.

Conclusion

Combining clinical characteristics and gait cycle parameters, including MDS-UPDRS-I scores, HAMD scores, and stride length variability, can identify PD patients with a high fatigue risk.

Antiparkinsonian medication masks motor signal progression in de novo patients

Patients not yet receiving medication provide insight to drug-naïve early physiology of Parkinson's Disease (PD). Wearable sensors can measure changes in motor features before and after introduction of antiparkinsonian medication. We aimed to identify features of upper limb bradykinesia, postural stability, and gait that measurably progress in de novo PD patients prior to the start of medication, and determine whether these features remain sensitive to progression in the period after commencement of antiparkinsonian medication. Upper limb motion was measured using an inertial sensor worn on a finger, while postural stability and gait were recorded using an array of six wearable sensors. Patients were tested over nine visits at three monthly intervals. The timepoint of start of medication was noted. Three upper limb bradykinetic features (finger tapping speed, pronation supination speed, and pronation supination amplitude) and three gait features (gait speed, arm range of motion, duration of stance phase) were found to progress in unmedicated early-stage PD patients. In all features, progression was masked after the start of medication. Commencing antiparkinsonian medication is known to lead to masking of progression signals in clinical measures in de novo PD patients. In this study, we show that this effect is also observed with digital measures of bradykinetic and gait motor features.

Effect of freezing of gait and dopaminergic medication in the biomechanics of lower limbs in the gait of patients with Parkinson's disease compared to neurologically healthy

INTRODUCTION

This study aims to evaluate the effects of medication, and the freezing of gait (FoG) on the kinematic and kinetic parameters of gait in people with Parkinson's disease (pwPD) compared to neurologically healthy.

METHODS

Twenty-two people with a clinical diagnosis of idiopathic PD in ON and OFF medication (11 FoG), and 18 healthy participants (control) were selected from two open data sets. All participants walked on the floor on a 10-meter-long walkway. The joint kinematic and ground reaction forces (GRF) variables of gait and the clinical characteristics were compared: (1) PD with FoG (pwFoG) and PD without FoG (pwoFoG) in the ON condition and control; (2) PD with FoG and PD without FoG in the OFF condition and control; (3) Group (PD with FoG and PD without FoG) and Medication.

RESULTS

(1) FoG mainly affects distal joints, such as the ankle and knee; (2) PD ON showed changes in the range of motion of both distal and proximal joints, which may explain the increase in step length and gait speed expected with the use of L-Dopa; and (3) the medication showed improvements in the kinematic and kinetic parameters of the gait of people with pwFoG and pwoFoG equally; (4) pwPD showed a smaller second peak of the vertical component of the GRF than the control.

CONCLUSION

The presence of FoG mainly affects distal joints, such as the ankle and knee. PD presents a lower application of GRF during the impulse period than healthy people, causing lower gait performances.

Effect of Medication and Deep Brain Stimulation on Gait in Parkinson's Disease and its Quantitative Analysis Using Mobishoe - A Comparative Study

Background

Movement abnormalities pertaining to balance, posture, and gait are observed in Parkinson's disease patients. Gait characteristics vary widely and their analysis has been performed traditionally in gait labs. Freezing and festination usually occur at an advanced stage of the disease and are associated with reduced quality of life. Therapeutic strategies and surgical interventions are often modulated by the physician depending upon the clinical manifestations. Introduction of accelerometers and wireless data transmission systems made quantitative gait analysis possible and cost-effective.

Objective

To assess spatiotemporal gait parameters (step height, length (spatial), and swing support time of each foot and double support time (temporal)) in subjects who underwent deep brain stimulation surgery using a purpose-built instrument-Mobishoe.

Methods

A simple footwear-based gait sensing device-Mobishoe was built in-house. Thirty-six participants were included in the study after obtaining consent. Participants were made to wear Mobishoe and walk an empty corridor of 30m before Deep Brain Stimulation (DBS) in the drug on and off stated and post DBS in DBS stimulation on and medication off state (B1M0), DBS stimulation off-medication off state (B0M0), DBS stimulation off-medication on (B0M1), and DBS stimulation on and on medication (B1M1). Data was electronically captured and analyzed offline in MATrix LABoratory (MATLAB). Various gait parameters were extracted and analyzed.

Results

Improvement in gait parameters was observed when the subject was on medication, on stimulation, or on both when compared to baseline. Improvement was similar with both medication and stimulation and was synergistic when both were used. Significant improvement was noted in spatial characteristics when the subjects were on both the treatments, which is the ideal treatment modality.

Conclusion

Mobishoe is an affordable device which can measure spatiotemporal characteristics of gait. The best improvement was seen when the subjects were on both the treatment groups and the improvement can be justified as a synergistic effect of stimulation and medication.

Reduced Range of Gait Speed: A Parkinson's Disease-Specific Symptom?

Reduced range of gait speed (RGS) may lead to decreased environmental adaptability in persons with Parkinson's disease (PwPD). Therefore, lab-measured gait speed, step time, and step length during slow, preferred, and fast walking were assessed in 24 PwPD, 19 stroke patients, and 19 older adults and compared with 31 young adults. Only PwPD, but not the other groups, showed significantly reduced RGS compared to young adults, driven by step time in the low and step length in the high gait speed range. These results suggest that reduced RGS may occur as a PD-specific symptom, and different gait components seem to contribute.

Deletion of Calhm2 alleviates MPTP-induced Parkinson's disease pathology by inhibiting EFHD2-STAT3 signaling in microglia

Background: Neuroinflammation is involved in the development of Parkinson's disease (PD). Calhm2 plays an important role in the development of microglial inflammation, but whether Calhm2 is involved in PD and its regulatory mechanisms are unclear. Methods: To study the role of Calhm2 in the development of PD, we utilized conventional Calhm2 knockout mice, microglial Calhm2 knockout mice and neuronal Calhm2 knockout mice, and established the MPTP-induced PD mice model. Moreover, a series of methods including behavioral test, immunohistochemistry, immunofluorescence, real-time polymerase chain reaction, western blot, mass spectrometry analysis and co-immunoprecipitation were utilized to study the regulatory mechanisms. Results: We found that both conventional Calhm2 knockout and microglial Calhm2 knockout significantly reduced dopaminergic neuronal loss, and decreased microglial numbers, thereby improving locomotor performance in PD model mice. Mechanistically, we found that Calhm2 interacted with EFhd2 and regulated downstream STAT3 signaling in microglia. Knockdown of Calhm2 or EFhd2 both inhibited downstream STAT3 signaling and inflammatory cytokine levels in microglia. Conclusion: We demonstrate the important role of Calhm2 in microglial activation and the pathology of PD, thus providing a potential therapeutic target for microglia-mediated neuroinflammation-related diseases.

Gait performance and non-motor symptoms burden during dual-task condition in Parkinson's disease

INTRODUCTION

Impaired gait is observed in patients with Parkinson's disease (PD) in both single-task (ST) and dual-task (DT) conditions. Non-motor symptoms (NMSs), another vital symptom future experienced along the PD disease trajectory, contribute to gait performance in PD. However, whether DT gait performance is indicative of NMS burden (NMSB) remains unknown. This study investigated correlation between NMS and DT gait performance and whether NMSB is reflected in the DT effects (DTEs) of gait parameters in PD.

METHODS

Thirty-three idiopathic PD participants were enrolled in this study; the median H-Y staging was 2.5. NMSB was assessed by Non-motor Symptoms Scale (NMSS). Spatiotemporal gait parameters under ST and DT conditions were evaluated by wearable sensors. Gait parameters under ST and DT conditions and DTEs of gait parameters were compared across NMSB groups. The associations between NMS and DTEs of gait parameters were analyzed by correlation analysis and linear regression models.

RESULTS

Compared to PD patients with mild-moderate NMSB, the severe-very severe NMSB group showed slower gait speed and shorter stride length under both ST and DT conditions (p < 0.05). DT had significantly negative effect on gait parameters in PD patients, including gait speed, stride length, and gait cycle duration (p < 0.05). PD patients with mild-moderate NMSB showed larger DTEs of cadence and bilateral gait cycle duration (p < 0.05). DTEs of bilateral gait cycle duration and swing phase on the more affected (MA) side were significantly correlated with NMSS scores (∣rSp∣ ≥ 0.3, p < 0.05). Gait cycle duration on the less affected (LA) side explained 43% of the variance in NMSS scores, when accounting for demographic and clinical confounders (β =  - 1.095 95% CI - 4.061 ~  - 0.058, p = 0.044; adjusted R² = 0.434).

CONCLUSION

DT gait performance could reflect NMSB in PD patients at early stage, and gait cycle duration is a valuable gait parameter to further investigate and to provide more evidence for PD management.

A public data set of walking full-body kinematics and kinetics in individuals with Parkinson's disease

Background

To our knowledge, there is no Parkinson's disease (PD) gait biomechanics data sets available to the public.

Objective

This study aimed to create a public data set of 26 idiopathic individuals with PD who walked overground on ON and OFF medication.

Materials and methods

Their upper extremity, trunk, lower extremity, and pelvis kinematics were measured using a three-dimensional motion-capture system (Raptor-4; Motion Analysis). The external forces were collected using force plates. The results include raw and processed kinematic and kinetic data in c3d and ASCII files in different file formats. In addition, a metadata file containing demographic, anthropometric, and clinical data is provided. The following clinical scales were employed: Unified Parkinson's disease rating scale motor aspects of experiences of daily living and motor score, Hoehn & Yahr, New Freezing of Gait Questionnaire, Montreal Cognitive Assessment, Mini Balance Evaluation Systems Tests, Fall Efficacy Scale-International-FES-I, Stroop test, and Trail Making Test A and B.

Results

All data are available at Figshare (https://figshare.com/articles/dataset/A_dataset_of_overground_walking_full-body_kinematics_and_kinetics_in_individuals_with_Parkinson_s_disease/14896881).

Conclusion

This is the first public data set containing a three-dimensional full-body gait analysis of individuals with PD under the ON and OFF medication. It is expected to contribute so that different research groups worldwide have access to reference data and a better understanding of the effects of medication on gait.

Evaluation of an Adoptive Cellular Therapy-Based Vaccine in a Transgenic Mouse Model of α-synucleinopathy

Aggregated α-synuclein, a major constituent of Lewy bodies plays a crucial role in the pathogenesis of α-synucleinopathies (SPs) such as Parkinson's disease (PD). PD is affected by the innate and adaptive arms of the immune system, and recently both active and passive immunotherapies targeted against α-synuclein are being trialed as potential novel treatment strategies. Specifically, dendritic cell-based vaccines have shown to be an effective treatment for SPs in animal models. Here, we report on the development of adoptive cellular therapy (ACT) for SP and demonstrate that adoptive transfer of pre-activated T-cells generated from immunized mice can improve survival and behavior, reduce brain microstructural impairment via magnetic resonance imaging (MRI), and decrease α-synuclein pathology burden in a peripherally induced preclinical SP model (M83) when administered prior to disease onset. This study provides preclinical evidence for ACT as a potential immunotherapy for LBD, PD and other related SPs, and future work will provide necessary understanding of the mechanisms of its action.

Present and future of gait assessment in clinical practice: Towards the application of novel trends and technologies

Background

Despite being available for more than three decades, quantitative gait analysis remains largely associated with research institutions and not well leveraged in clinical settings. This is mostly due to the high cost/cumbersome equipment and complex protocols and data management/analysis associated with traditional gait labs, as well as the diverse training/experience and preference of clinical teams. Observational gait and qualitative scales continue to be predominantly used in clinics despite evidence of less efficacy of quantifying gait.

Research objective

This study provides a scoping review of the status of clinical gait assessment, including shedding light on common gait pathologies, clinical parameters, indices, and scales. We also highlight novel state-of-the-art gait characterization and analysis approaches and the integration of commercially available wearable tools and technology and AI-driven computational platforms.

Methods

A comprehensive literature search was conducted within PubMed, Web of Science, Medline, and ScienceDirect for all articles published until December 2021 using a set of keywords, including normal and pathological gait, gait parameters, gait assessment, gait analysis, wearable systems, inertial measurement units, accelerometer, gyroscope, magnetometer, insole sensors, electromyography sensors. Original articles that met the selection criteria were included.

Results and significance

Clinical gait analysis remains highly observational and is hence subjective and largely influenced by the observer's background and experience. Quantitative Instrumented gait analysis (IGA) has the capability of providing clinicians with accurate and reliable gait data for diagnosis and monitoring but is limited in clinical applicability mainly due to logistics. Rapidly emerging smart wearable technology, multi-modality, and sensor fusion approaches, as well as AI-driven computational platforms are increasingly commanding greater attention in gait assessment. These tools promise a paradigm shift in the quantification of gait in the clinic and beyond. On the other hand, standardization of clinical protocols and ensuring their feasibility to map the complex features of human gait and represent them meaningfully remain critical challenges.

Human gait-labeling uncertainty and a hybrid model for gait segmentation

Motion capture systems are widely accepted as ground-truth for gait analysis and are used for the validation of other gait analysis systems. To date, their reliability and limitations in manual labeling of gait events have not been studied.

Objectives

Evaluate manual labeling uncertainty and introduce a hybrid stride detection and gait-event estimation model for autonomous, long-term, and remote monitoring.

Methods

Estimate inter-labeler inconsistencies by computing the limits-of-agreement. Develop a hybrid model based on dynamic time warping and convolutional neural network to identify valid strides and eliminate non-stride data in inertial (walking) data collected by a wearable device. Finally, detect gait events within a valid stride region.

Results

The limits of inter-labeler agreement for key gait events heel off, toe off, heel strike, and flat foot are 72, 16, 24, and 80 ms, respectively; The hybrid model's classification accuracy for stride and non-stride are 95.16 and 84.48%, respectively; The mean absolute error for detected heel off, toe off, heel strike, and flat foot are 24, 5, 9, and 13 ms, respectively, when compared to the average human labels.

Conclusions

The results show the inherent labeling uncertainty and the limits of human gait labeling of motion capture data; The proposed hybrid-model's performance is comparable to that of human labelers, and it is a valid model to reliably detect strides and estimate the gait events in human gait data.

Significance

This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.

Longitudinal assessment of motor function following the unilateral intrastriatal 6-hydroxydopamine lesion model in mice

Introduction

Despite the widespread use of the unilateral striatal 6-hydroxydopamine (6-OHDA) lesion model in mice in recent years, the stability of behavioral deficits in the 6-OHDA striatal mouse model over time is not yet clear, raising concerns about using this model to evaluate a compound's long-term therapeutic effects.

Materials and methods

In the current study, mice were tested at regular intervals in the cylinder test and gait analysis beginning 3 days after 6-OHDA injection of 4 and 8 μg and lasting until 56 days post-lesion. Apomorphine-induced rotational test and rotarod test were also performed on Day 23 and 43 post-lesion, respectively. Immunohistochemistry for dopaminergic neurons stained by tyrosine hydroxylase (TH) was also performed.

Results

Our results showed that both the 4 and 8 μg 6-OHDA lesion groups exhibited forelimb use asymmetry with a preference for the ipsilateral (injection) side on Day 3 and until Day 21 post-lesion, but did not show forelimb asymmetry on Day 28 to 56 post-lesion. The 8 μg 6-OHDA lesion group still exhibited forelimb asymmetry on Day 28 and 42 post-lesion, but not on Day 56. The gait analysis showed that the contralateral front and hind step cycles increased from Day 3 to 42 post-lesion and recovered on Day 56 post-lesion. In addition, our results displayed a dose-dependent reduction in TH⁺ cells and TH⁺ fibers, as well as dose-dependent apomorphine-induced rotations. In the rotarod test, the 8 μg 6-OHDA lesion group, but not the 4 μg group, decreased the latency to fall on the rotarod on Day 43 post-lesion.

Conclusion

In summary, unilateral striatal 6-OHDA injections of 4 and 8 μg induced spontaneous motor impairment in mice, which partially recovered starting on Day 28 post-lesion. Forced motor deficits were observed in the 8 g 6-OHDA lesion group, which remained stable on Day 43 post-lesion. In addition, the rotarod test and apomorphine-induced rotational test can distinguish between lesions of different extents and are useful tools for the assessment of functional recovery in studies screening novel potential therapies.

An 8-week aerobic cycling intervention elicits improved gait velocity and biomechanics in persons with Parkinson's disease

BACKGROUND

It is unknown if improvements in gait velocity following an aerobic cycling intervention are accompanied by improved gait biomechanics in individuals with Parkinson's disease (PD) or if gait abnormalities are exaggerated in response to increased velocity. Research question Can an 8-week aerobic cycling intervention elicit improvements in locomotor function in individuals with mild to moderate PD?

METHODS

A secondary analysis of data from a randomized clinical trial was conducted in individuals with mild to moderate idiopathic PD (N = 28). Participants were randomized to an aerobic cycling intervention (PDex, N = 14) consisting of 24 sessions at a targeted aerobic intensity of 60-80% of heart rate reserve or to a no intervention control group (PDcontrol, N = 14). Change in comfortable walking speed in addition to gait kinematics, kinetics, and spatiotemporal variables using motion capture were obtained at baseline and end of treatment (EOT).

RESULTS

The PDex group made significantly greater improvements in the primary outcome, change in comfortable gait velocity, from 0.86 ± 0.24 m/s at baseline to 1.00 ± 0.23 m/s at EOT compared to the PDcontrol group who declined from 0.91 ± 0.23 m/s at baseline to 0.80 ± 0.29 at EOT (P = 0.002). Improvements in gait velocity for the PDex group were accompanied by improvements in gait kinematics, kinetics, and spatiotemporal parameters, while the PDcontrol group demonstrated slight worsening in all gait parameters over the 8-week period. Significance The 8-week moderate- to high-intensity cycling intervention elicited significantly greater improvements in gait velocity compared to the PDcontrol group. Increased gait velocity was accompanied by normalization of gait biomechanics, rather than an exaggeration of existing gait deviations. Aerobic cycling may be a viable treatment approach to improve gait velocity and gait biomechanics in individuals with mild to moderate PD and may mitigate declines in mobility.

The kinectome: A comprehensive kinematic map of human motion in health and disease

Human voluntary movement stems from the coordinated activations in space and time of many musculoskeletal segments. However, the current methodological approaches to study human movement are still limited to the evaluation of the synergies among a few body elements. Network science can be a useful approach to describe movement as a whole and to extract features that are relevant to understanding both its complex physiology and the pathophysiology of movement disorders. Here, we propose to represent human movement as a network (that we named the kinectome), where nodes represent body points, and edges are defined as the correlations of the accelerations between each pair of them. We applied this framework to healthy individuals and patients with Parkinson's disease, observing that the patients' kinectomes display less symmetrical patterns as compared to healthy controls. Furthermore, we used the kinectomes to successfully identify both healthy and diseased subjects using short gait recordings. Finally, we highlighted topological features that predict the individual clinical impairment in patients. Our results define a novel approach to study human movement. While deceptively simple, this approach is well-grounded, and represents a powerful tool that may be applied to a wide spectrum of frameworks.

Original article: Validity and reliability of gait metrics derived from researcher-placed and self-placed wearable inertial sensors

To compare the inter-session placement reliability for researcher-placed and self-placed sensors, and to evaluate the validity and reliability of waveforms and discrete variables from researcher-placed and self-placed sensors following a previously described alignment correction algorithm. Fourteen healthy, pain-free participants underwent gait analysis over two data collection sessions. Participants self-placed an inertial sensor on their left tibia and a researcher placed one on their right tibia, before completing 10 overground walking trials. Following an axis correction from a principal component analysis-based algorithm, validity and reliability were assessed within and between days for each sensor placement type through Euclidean distances, waveforms, and discrete outcomes. The placement location of researcher-placed sensors exhibited good inter-session reliability (ICC = 0.85) in comparison to self-placed sensors (ICC = 0.55). Similarly, waveforms from researcher-placed sensors exhibited excellent validity across all variables (CMC ≥ 0.90), while self-placed sensors saw high validity for most axes with reductions in validity for mediolateral acceleration and frontal plane angular velocity. Discrete outcomes saw good to excellent reliability across both sensor placement types. A simple alignment correction algorithm for inertial sensor gait data demonstrated good to excellent validity and reliability in self-placed sensors with no additional data or measures. This method can be used to align sensors easily and effectively despite sensor placement errors during straight, level walking to improve 3D gait data outcomes in data collected with self-placed sensors.

Detection and assessment of Parkinson's disease based on gait analysis: A survey

Neurological disorders represent one of the leading causes of disability and mortality in the world. Parkinson's Disease (PD), for example, affecting millions of people worldwide is often manifested as impaired posture and gait. These impairments have been used as a clinical sign for the early detection of PD, as well as an objective index for pervasive monitoring of the PD patients in daily life. This review presents the evidence that demonstrates the relationship between human gait and PD, and illustrates the role of different gait analysis systems based on vision or wearable sensors. It also provides a comprehensive overview of the available automatic recognition systems for the detection and management of PD. The intervening measures for improving gait performance are summarized, in which the smart devices for gait intervention are emphasized. Finally, this review highlights some of the new opportunities in detecting, monitoring, and treating of PD based on gait, which could facilitate the development of objective gait-based biomarkers for personalized support and treatment of PD.

Gait alterations in Parkinson’s disease at the stage of hemiparkinsonism—A longitudinal study

Background

Progressive gait impairment in Parkinson’s disease (PD) leads to significant disability. Quantitative gait parameters analysis provides valuable information about fine gait alterations.

Objectives

To analyse change of gait parameters in patients with early PD at the stage of hemiparkinsonism and after 1 year of follow up, taking into account clinical asymmetry.

Methods

Consecutive early PD outpatients with strictly unilateral motor features underwent clinical and neuropsychological assessment at the study entry and after 1 year of follow up. Gait was assessed with GAITRite walkway using dual-task methodology. Spatiotemporal gait parameters (step time and length, swing time and double support time) and their coefficients of variation (CV), gait velocity and heel-to-heel base support were evaluated.

Results

We included 42 PD patients with disease duration of 1.3 years (±1.13). Progression of motor and non-motor symptoms, without significant cognitive worsening, was observed after 1 year of follow up. Significant shortening of the swing time, prolongation of the double support and increase of their CVs were observed during all task conditions similarly for most parameters on symptomatic and asymptomatic bodysides, except for CV for the swing time under the combined task.

Conclusion

Alterations of the swing time and double support time are already present even at the asymptomatic body side, and progress similarly, or even at faster pace, at this side, despite dopaminergic treatment These parameters deserve further investigation in larger, prospective studies to address their potential to serve as markers of progression in interventional disease modifying trials with early PD patients.

Dual task effect on upper and lower extremity skills in different stages of Parkinson's disease

BACKGROUND AND PURPOSE

Loss of automaticity and deteriorated executive function give rise to dual task deficits in Parkinson's disease (PD). This study aimed to compare single task and dual task upper and lower extremity skills in people with PD (PwPD) at different stages of PD and to examine the dual task effect (DTE) on upper and lower extremity skills in PwPD at different stages of PD. The second aim was to investigate the relationship between the DTE and the quality of life in PwPD.

METHODS

30 patients divided into 2 groups as mild PD group and moderate PD group according to the Modified Hoehn & Yahr Scale. 15 age matched healthy adults were recruited as the control group. The Unified Parkinson's Disease Rating Scale (UPDRS), the Purdue Pegboard Test (PPT), the Timed Up and Go Test (TUG), the 10 Meter Walk Test (10MWT), and the Parkinson's Disease Questionnaire (PDQ-8) were used for assessments.

RESULTS

Single task and dual task scores of all assessments of all groups were significantly different. The DTE on PPT was greater in mild and moderate PD groups than control group and significantly lower in mild PD group than moderate PD group. However, DTE on the TUG and 10MWT was not different in mild PD group than control group and DTE significantly lower in both groups than moderate PD group. Significant correlations between the DTE on PPT, TUG and 10MWT and the PDQ-8 in PwPD were observed.

CONCLUSION

Dual task has a worsening effect on upper and lower extremity skills in PwPD. This effect can be observed earlier in upper extremity skills than lower extremity skills. Also, the DTE and the QoL in PwPD are related.

Absolute Reliability of Gait Parameters Acquired With Markerless Motion Capture in Living Domains

Purpose: To examine the between-day absolute reliability of gait parameters acquired with Theia3D markerless motion capture for use in biomechanical and clinical settings. Methods: Twenty-one (7 M,14 F) participants aged between 18 and 73 years were recruited in community locations to perform two walking tasks: self-selected and fastest-comfortable walking speed. Participants walked along a designated walkway on two separate days.Joint angle kinematics for the hip, knee, and ankle, for all planes of motion, and spatiotemporal parameters were extracted to determine absolute reliability between-days. For kinematics, absolute reliability was examined using: full curve analysis [root mean square difference (RMSD)] and discrete point analysis at defined gait events using standard error of measurement (SEM). The absolute reliability of spatiotemporal parameters was also examined using SEM and SEM%. Results: Markerless motion capture produced low measurement error for kinematic full curve analysis with RMSDs ranging between 0.96° and 3.71° across all joints and planes for both walking tasks. Similarly, discrete point analysis within the gait cycle produced SEM values ranging between 0.91° and 3.25° for both sagittal and frontal plane angles of the hip, knee, and ankle. The highest measurement errors were observed in the transverse plane, with SEM >5° for ankle and knee range of motion. For the majority of spatiotemporal parameters, markerless motion capture produced low SEM values and SEM% below 10%. Conclusion: Markerless motion capture using Theia3D offers reliable gait analysis suitable for biomechanical and clinical use.

Feasibility of Markerless Motion Capture for Three-Dimensional Gait Assessment in Community Settings

Three-dimensional (3D) kinematic analysis of gait holds potential as a digital biomarker to identify neuropathologies, monitor disease progression, and provide a high-resolution outcome measure to monitor neurorehabilitation efficacy by characterizing the mechanisms underlying gait impairments. There is a need for 3D motion capture technologies accessible to community, clinical, and rehabilitation settings. Image-based markerless motion capture (MLMC) using neural network-based deep learning algorithms shows promise as an accessible technology in these settings. In this study, we assessed the feasibility of implementing 3D MLMC technology outside the traditional laboratory environment to evaluate its potential as a tool for outcomes assessment in neurorehabilitation. A sample population of 166 individuals aged 9-87 years (mean 43.7, S.D. 20.4) of varied health history were evaluated at six different locations in the community over a 3-month period. Participants walked overground at self-selected (SS) and fastest comfortable (FC) speeds. Feasibility measures considered the expansion, implementation, and practicality of this MLMC system. A subset of the sample population (46 individuals) walked over a pressure-sensitive walkway (PSW) concurrently with MLMC to assess agreement of the spatiotemporal gait parameters measured between the two systems. Twelve spatiotemporal parameters were compared using mean differences, Bland-Altman analysis, and intraclass correlation coefficients for agreement (ICC2,1) and consistency (ICC3,1). All measures showed good to excellent agreement between MLMC and the PSW system with cadence, speed, step length, step time, stride length, and stride time showing strong similarity. Furthermore, this information can inform the development of rehabilitation strategies targeting gait dysfunction. These first experiments provide evidence for feasibility of using MLMC in community and clinical practice environments to acquire robust 3D kinematic data from a diverse population. This foundational work enables future investigation with MLMC especially its use as a digital biomarker of disease progression and rehabilitation outcome.

The reliability of gait parameters captured via instrumented walkways: a systematic review and meta-analysis

INTRODUCTION

Electronic pressure-sensitive walkways are commonly available solutions to quantitatively assess gait parameters for clinical and research purposes. Many studies have evaluated their measurement properties in different conditions with variable findings. In order to be informed about the current evidence of their reliability for optimal clinical and scientific decision making, this systematic review provided a quantitative synthesis of the test-retest reliability and minimal detectable change of the captured gait parameters across different test conditions (single and cognitive dual-task conditions) and population groups.

EVIDENCE ACQUISITION

A literature search was conducted in PubMed, Embase, and Scopus until November 2021 to identify articles that examined the test-retest reliability properties of the gait parameters captured by pressure-sensitive walkways (gait speed, cadence, stride length and time, double support time, base of support) in adult healthy individuals or patients. The methodological quality was rated using the Consensus-Based Standards for the Selection of Health Measurement Instruments Checklist. Data were meta-analyzed on intraclass correlation coefficient to examine the test-retest relative reliability. Quantitative synthesis was performed for absolute reliability, examined by the weighted average of minimal detectable change values.

EVIDENCE SYNTHESIS

A total of 44 studies were included in this systematic review. The methodological quality was adequate in half of the included studies. The main finding was that pressure-sensitive walkways are reliable tools for objective assessment of spatial and temporal gait parameters both in single-and cognitive dual-task conditions. Despite few exceptions, the review identified intraclass correlation coefficient higher than 0.75 and minimal detectable change lower than 30%, demonstrating satisfactory relative and absolute reliability in all examined populations (healthy adults, elderly, patients with cognitive impairment, spinocerebellar ataxia type 14, Huntington's disease, multiple sclerosis, Parkinson's disease, rheumatoid arthritis, spinal cord injury, stroke or vestibular dysfunction).

CONCLUSIONS

Current evidence suggested that, despite different populations and testing protocols used in the included studies, the test-retest reliability of the examined gait parameters was acceptable under single and cognitive dual-task conditions. Further high-quality studies with powered sample sizes are needed to examine the reliability findings of the currently understudied and unexplored pathologies and test conditions.

An Activity Recognition Framework for Continuous Monitoring of Non-Steady-State Locomotion of Individuals with Parkinson’s Disease

Fundamental knowledge in activity recognition of individuals with motor disorders such as Parkinson’s disease (PD) has been primarily limited to detection of steady-state/static tasks (e.g., sitting, standing, walking). To date, identification of non-steady-state locomotion on uneven terrains (stairs, ramps) has not received much attention. Furthermore, previous research has mainly relied on data from a large number of body locations which could adversely affect user convenience and system performance. Here, individuals with mild stages of PD and healthy subjects performed non-steady-state circuit trials comprising stairs, ramp, and changes of direction. An offline analysis using a linear discriminant analysis (LDA) classifier and a Long-Short Term Memory (LSTM) neural network was performed for task recognition. The performance of accelerographic and gyroscopic information from varied lower/upper-body segments were tested across a set of user-independent and user-dependent training paradigms. Comparing the F1 score of a given signal across classifiers showed improved performance using LSTM compared to LDA. Using LSTM, even a subset of information (e.g., feet data) in subject-independent training appeared to provide F1 score > 0.8. However, employing LDA was shown to be at the expense of being limited to using a subject-dependent training and/or biomechanical data from multiple body locations. The findings could inform a number of applications in the field of healthcare monitoring and developing advanced lower-limb assistive devices by providing insights into classification schemes capable of handling non-steady-state and unstructured locomotion in individuals with mild Parkinson’s disease.

Foot Trajectory Features in Gait of Parkinson’s Disease Patients

Parkinson’s disease (PD) is a progressive neurological disorder characterized by movement disorders, such as gait instability. This study investigated whether certain spatial features of foot trajectory are characteristic of patients with PD. The foot trajectory of patients with mild and advanced PD in on-state and healthy older and young individuals was estimated from acceleration and angular velocity measured by inertial measurement units placed on the subject’s shanks, just above the ankles. We selected six spatial variables in the foot trajectory: forward and vertical displacements from heel strike to toe-off, maximum clearance, and change in supporting leg (F1 to F3 and V1 to V3, respectively). Healthy young individuals had the greatest F2 and F3 values, followed by healthy older individuals, and then mild PD patients. Conversely, the vertical displacements of mild PD patients were larger than the healthy older individuals. Still, those of healthy older individuals were smaller than the healthy young individuals except for V3. All six displacements of the advanced PD patients were smaller than the mild PD patients. To investigate features in foot trajectories in detail, a principal components analysis and soft-margin kernel support vector machine was used in machine learning. The accuracy in distinguishing between mild PD patients and healthy older individuals and between mild and advanced PD patients was 96.3 and 84.2%, respectively. The vertical and forward displacements in the foot trajectory was the main contributor. These results reveal that large vertical displacements and small forward ones characterize mild and advanced PD patients, respectively.

Turning reveals the characteristics of gait freezing better than walking forward and backward in Parkinson's disease

BACKGROUNDS

People with Parkinson's disease (PD) experiences walking disturbances and freezing of gait (FoG) is one of the most distressing symptoms.

RESEARCH QUESTION

This study aimed to comprehensively analyze the walking characteristics of patients with PD, including forward and backward walking and turning, in order to define the characteristics of FoG.

METHODS

A total of 68 patients with PD and 14 control subjects were enrolled in this study. Forward and backward walking and 360-degree turning were recorded at preferred speed in "off" state using three-dimensional motion analysis system.

RESULTS

PD patients showed a narrower step length, slower walking speed, and higher asymmetry index (AI) of step length during forward and backward walking. During turning, the PD patients had more turning steps, longer turning time, and shorter step length than the control subjects. No difference was observed in the characteristics of forward walking according to the FoG status, but the freezer group showed a narrower step length and decreased range of motion in the ankle joints during backward walking. Freezer group showed longer step time and higher AI of step length at turning. The severity of FoG was correlated with step length and walking speed during forward and backward walking, total step count, total step time, and walking speed during turning.

SIGNIFICANCE

The characteristics and impacts of FoG in PD were most prominent during turning, followed by backward and forward walking. Further comprehensive analyses of walking including turning might contribute to the understanding of the pathophysiology of walking disturbances in PD.

Sensor-based gait analysis in the premotor stage of LRRK2 G2019S-associated Parkinson's disease

INTRODUCTION

There is a need for biomarkers to monitor the earliest phases of Parkinson's disease (PD), especially in premotor stages. Here, we studied whether there are early gait alterations in carriers of the G2019S mutation of LRRK2 that can be detected by means of an inertial sensor system.

METHODS

Twenty-one idiopathic PD patients, 20 LRRK2-G2019S PD, 27 asymptomatic carriers of LRRK2-G2019S mutation (AsG2019S) and 36 controls walked equipped with 16 lightweight inertial sensors in three different experiments: i/normal gait, ii/fast gait and iii/dual-task gait. In the AsG2019S group, DaT-SPECT (¹²³I-ioflupane) with semi-quantitative analysis was carried out. Motor and cognitive performance were evaluated using MDS-UPDRS-III and MoCA scales. We employed neural network techniques to classify individuals based on their walking patterns.

RESULTS

PD patients and controls showed differences in speed, stride length and arm swing amplitude, variability and asymmetry in all three tasks (p < 0.01). In the AsG2019S group, the only differences were detected during fast walking, with greater step time on the non-dominant side (p < 0.05), lower step/stride time variability (p < 0.01) and lower step time asymmetry (p < 0.01). DaT uptake showed a significant correlation with step time during fast walking on the non-dominant side (r = -0.52; p < 0.01). The neural network was able to differentiate between AsG2019S and healthy controls with an accuracy rate of 82.5%.

CONCLUSION

Our sensor-based analysis did not detect substantial and robust changes in the gait of LRRK2-G2019S asymptomatic mutation carriers. Nonetheless, step or stride time during fast walking, supported by the observed correlation with striatal DaT binding deserves consideration as a potential biomarker in future studies.

Framework for Simultaneous Indoor Localization, Mapping, and Human Activity Recognition in Ambient Assisted Living Scenarios

Indoor localization and human activity recognition are two important sources of information to provide context-based assistance. This information is relevant in ambient assisted living (AAL) scenarios, where older adults usually need supervision and assistance in their daily activities. However, indoor localization and human activity recognition have been mostly considered isolated problems. This work presents and evaluates a framework that takes advantage of the relationship between location and activity to simultaneously perform indoor localization, mapping, and human activity recognition. The proposed framework provides a non-intrusive configuration, which fuses data from an inertial measurement unit (IMU) placed in the person's shoe, with proximity and human activity-related data from Bluetooth low energy beacons (BLE) deployed in the indoor environment. A variant of the simultaneous location and mapping (SLAM) framework was used to fuse the location and human activity recognition (HAR) data. HAR was performed using data streaming algorithms. The framework was evaluated in a pilot study, using data from 22 people, 11 young people, and 11 older adults (people aged 65 years or older). As a result, seven activities of daily living were recognized with an F1 score of 88%, and the in-door location error was 0.98 ± 0.36 m for the young and 1.02 ± 0.24 m for the older adults. Furthermore, there were no significant differences between the groups, indicating that our proposed method works adequately in broad age ranges.

Integrated Equipment for Parkinson’s Disease Early Detection Using Graph Convolution Network

There is an increasing need to diagnose Parkinson’s disease (PD) in an early stage. Existing solutions mainly focused on traditional ways such as MRI, thus suffering from the ease-of-use issue. This work presents a new approach using video and skeleton-based techniques to solve this problem. In this paper, an end-to-end Parkinson’s disease early diagnosis method based on graph convolution networks is proposed, which takes patients’ skeletons sequence as input and returns the diagnosis result. The asymmetric dual-branch network architecture is designed to process global and local information separately and capture the subtle manifestation of PD. To train the network, we present the first Parkinson’s disease gait dataset, PD-Walk. This dataset consists of 95 PD patients and 96 healthy people’s walking videos. All the data are annotated by experienced doctors. Furthermore, we implement our method on portable equipment, which has been in operation in the First Affiliated Hospital, Zhejiang University School of Medicine. Experiments show that our method can achieve 84.1% accuracy and achieve real-time performance on the equipment in the real environment. Compared with traditional solutions, the proposed method can detect suspicious PD symptoms quickly and conveniently. Integrated equipment can be easily placed in hospitals or nursing homes to provide services for elderly people.

Proof of Concept in Artificial-Intelligence-Based Wearable Gait Monitoring for Parkinson's Disease Management Optimization

Parkinson's disease (PD) is the second most common progressive neurodegenerative disorder, affecting 6.2 million patients and causing disability and decreased quality of life. The research is oriented nowadays toward artificial intelligence (AI)-based wearables for early diagnosis and long-term PD monitoring. Our primary objective is the monitoring and assessment of gait in PD patients. We propose a wearable physiograph for qualitative and quantitative gait assessment, which performs bilateral tracking of the foot biomechanics and unilateral tracking of arm balance. Gait patterns are assessed by means of correlation. The surface plot of a correlation coefficient matrix, generated from the recorded signals, is classified using convolutional neural networks into physiological or PD-specific gait. The novelty is given by the proposed AI-based decisional support procedure for gait assessment. A proof of concept of the proposed physiograph is validated in a clinical environment on five patients and five healthy controls, proving to be a feasible solution for ubiquitous gait monitoring and assessment in PD. PD management demonstrates the complexity of the human body. A platform empowering multidisciplinary, AI-evidence-based decision support assessments for optimal dosing between drug and non-drug therapy could lay the foundation for affordable precision medicine.