Middle-range scores from the patient determined disease steps scale reflect varying levels of walking dysfunction in multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is a leading cause of neurological disability among young and middle-aged adults worldwide, and disability is measured using a variety of approaches, including patient reported outcome measures (PROMs) such as the Patient Determined Disease Steps (PDDS) scale. There is limited evidence for the validity of inferences from the middle-range of scores on the PDDS (i.e., 3 "gait disability" - 6 "bilateral support"), but that range of scores seemingly represents moderate disability characterized by varying levels of walking dysfunction. PURPOSE: The current study examined whether the middle-range of scores from the PDDS reflect varying levels of walking dysfunction among people with MS. METHOD: Participants (N = 374) completed the Patient Determined Disease Steps (PDDS) scale, Multiple Sclerosis Walking Scale-12 (MSWS-12), timed 25-foot walk (T25FW), six-minute walk (6 MW), Modified Fatigue Impact Scale (MFIS), and Multiple Sclerosis Impact Scale-29 (MSIS-29), and underwent a neurological exam for generating an Expanded Disability Status Scale (EDSS) score as part of screening and baseline data collection for a clinical trial of exercise training in MS. We undertook a series of linear trend analyses that examined differences in the outcomes of EDSS, T25FW, 6 MW, MSWS-12, MFIS subscales, and MSIS-29 subscales across the 4 levels of PDDS scores (i.e., 3-6). RESULTS: There were statistically significant and strong linear trends for EDSS (F1,370 = 306.1, p < .0001, η2 = 0.48), T25FW (F1,370 = 161.0, p < .0001, η2 = 0.32), 6 MW (F1,370 = 178.9, p < .0001, η2 = 0.34), and MSWS-12 (F1,370 = 97.0, p < .0001, η2 = 0.24). There was a strong correlation between PDDS and EDSS scores (rs = 0.695, 95% CI = 0.643, 0.748). Both PDDS and EDSS scores had strong correlations with walking outcomes, yet weaker correlations with measures of fatigue and QOL. CONCLUSION: The PDDS could serve as a simple, inexpensive, and rapidly administered PROM for remote screening and early detection of walking dysfunction for initial eligibility into clinical trials and practice for managing mobility-specific disability in MS. REGISTRATION: The study was registered on ClinicalTrials.gov on March 19, 2018 (NCT03468868).

Deep learning-based detection of affected body parts in Parkinson's disease and freezing of gait using time-series imaging.

We proposed a deep learning method using a convolutional neural network on time-series (TS) images to detect and differentiate affected body parts in people with Parkinson's disease (PD) and freezing of gait (FOG) during 360° turning tasks. The 360° turning task was performed by 90 participants (60 people with PD [30 freezers and 30 nonfreezers] and 30 age-matched older adults (controls) at their preferred speed. The position and acceleration underwent preprocessing. The analysis was expanded from temporal to visual data using TS imaging methods. According to the PD vs. controls classification, the right lower third of the lateral shank (RTIB) on the least affected side (LAS) and the right calcaneus (RHEE) on the LAS were the most relevant body segments in the position and acceleration TS images. The RHEE marker exhibited the highest accuracy in the acceleration TS images. The identified markers for the classification of freezers vs. nonfreezers vs. controls were the left lateral humeral epicondyle (LELB) on the more affected side and the left posterior superior iliac spine (LPSI). The LPSI marker in the acceleration TS images displayed the highest accuracy. This approach could be a useful supplementary tool for determining PD severity and FOG.

Clinical Reasoning: A 49-Year-Old Man With Progressive Numbness, Gait Instability, and Tremors.

Approaching patients with paraproteinemic neuropathies can be challenging for the practicing neurologist, and a well-defined strategy considering specific etiologies is necessary to arrive at the correct diagnosis. In this case, a 49-year-old man presented with a 2-year history of progressive upper then lower extremity numbness, weakness, gait instability, and tremors. His examination was marked by proximal and distal symmetric upper and lower extremity weakness, large more than small-fiber sensory loss, prominent sensory ataxia, action and postural tremors, and globally absent deep tendon reflexes. His workup was notable for a chronic demyelinating sensorimotor polyradiculoneuropathy and a monoclonal immunoglobulin (Ig) M kappa gammopathy. This case highlights the approach to a patient with a rare subtype of IgM paraproteinemic neuropathy with a review of the differential diagnoses, red flag features of co-occurring hematologic disorders, and guided workup. We further discuss typical features of this rare diagnosis and therapeutic options.

'Watkins' & 'Watkins2.0': Smart phone applications (Apps) for gait-assessment in normal pressure hydrocephalus and decompensated long-standing overt ventriculomegaly.

OBJECTIVE: Gait disturbance is one of the features of normal pressure hydrocephalus (NPH) and decompensated long-standing overt ventriculomegaly (LOVA). The timed-up-and-go (TUG) test and the timed-10-m-walking test (10MWT) are frequently used assessments tools for gait and balance disturbances in NPH and LOVA, as well as several other disorders. We aimed to make smart-phone apps which perform both the 10MWT and the TUG-test and record the results for individual patients, thus making it possible for patients to have an objective assessment of their progress. Patients with a suitable smart phone can perform repeat assessments in their home environment, providing a measure of progress for them and for their clinical team. METHODS: 10MWT and TUG-test were performed by 50 healthy adults, 67 NPH and 10 LOVA patients, as well as 5 elderly patients as part of falls risk assessment using the Watkins2.0 app. The 10MWT was assessed with timed slow-pace and fast-pace. Statistical analysis used SPSS (version 25.0, IBM) by paired t-test, comparing the healthy and the NPH cohorts. Level of precision of the app as compared to a clinical observer using a stopwatch was evaluated using receiver operating characteristics curve. RESULTS: As compared to a clinical observer using a stopwatch, in 10MWT the app showed 100% accuracy in the measure of time taken to cover distance in whole seconds, 95% accuracy in the number of steps taken with an error ± 1-3 steps, and 97% accuracy in the measure of total distance covered with error of ± 0.25-0.50 m. The TUG test has 100% accuracy in time taken to complete the test in whole seconds, 97% accuracy in the number of steps with an error of ± 1-2 steps and 87.5% accuracy in the distance covered with error of ± 0.50 m. In the measure of time, the app was found to have equal sensitivity as an observer. In measure of number of steps and distance, the app demonstrated high sensitivity and precision (AUC > 0.9). The app also showed significant level of discrimination between healthy and gait-impaired individuals. CONCLUSION: 'Watkins' and 'Watkins2.0' are efficient apps for objective performance of 10MWT and the TUG-test in NPH and LOVA patients and has application in several other pathologies characterised by gait and balance disturbance.

Feasibility and usefulness of video-based markerless two-dimensional automated gait analysis, in providing objective quantification of gait and complementing the evaluation of gait in children with cerebral palsy.

BACKGROUND: Gait analysis aids in evaluation, classification, and follow-up of gait pattern over time in children with cerebral palsy (CP). The analysis of sagittal plane joint kinematics is of special interest to assess flexed knee gait and ankle joint deviations that commonly progress with age and indicate deterioration of gait. Although most children with CP are ambulatory, no objective quantification of gait is currently included in any of the known international follow-up programs. Is video-based 2-dimensional markerless (2D ML) gait analysis with automated processing a feasible and useful tool to quantify deviations, evaluate and classify gait, in children with CP? METHODS: Twenty children with bilateral CP with Gross Motor Function Classification Scale (GMFCS) levels I-III, from five regions in Sweden, were included from the national CP registry. A single RGB-Depth video camera, sensitive to depth and contrast, was positioned laterally to a green walkway and background, with four light sources. A previously validated markerless method was employed to estimate sagittal plane hip, knee, ankle kinematics, foot orientation and spatio-temporal parameters including gait speed and step length. RESULTS: Mean age was 10.4 (range 6.8-16.1) years. Eight children were classified as GMFCS level I, eight as II and four as III. Setup of the measurement system took 15 min, acquisition 5-15 min and processing 50 min per child. Using the 2D ML method kinematic deviations from normal could be determined and used to implement the classification of gait pattern, proposed by Rodda et al. 2001. CONCLUSION: 2D ML assessment is feasible, since it is accessible, easy to perform and well tolerated by the children. The 2D ML adds consistency and quantifies objectively important gait variables. It is both relevant and reasonable to include 2D ML gait assessment in the evaluation of children with CP.

The inertial-based gait normalcy index of dual task cost during turning quantifies gait automaticity improvement in early-stage Parkinson's rehabilitation.

BACKGROUND: The loss of gait automaticity is a key cause of motor deficits in Parkinson's disease (PD) patients, even at the early stage of the disease. Action observation training (AOT) shows promise in enhancing gait automaticity. However, effective assessment methods are lacking. We aimed to propose a novel gait normalcy index based on dual task cost (NIDTC) and evaluate its validity and responsiveness for early-stage PD rehabilitation. METHODS: Thirty early-stage PD patients were recruited and randomly assigned to the AOT or active control (CON) group. The proposed NIDTC during straight walking and turning tasks and clinical scale scores were measured before and after 12 weeks of rehabilitation. The correlations between the NIDTCs and clinical scores were analyzed with Pearson correlation coefficient analysis to evaluate the construct validity. The rehabilitative changes were assessed using repeated-measures ANOVA, while the responsiveness of NIDTC was further compared by t tests. RESULTS: The turning-based NIDTC was significantly correlated with multiple clinical scales. Significant group-time interactions were observed for the turning-based NIDTC (F = 4.669, p = 0.042), BBS (F = 6.050, p = 0.022) and PDQ-39 (F = 7.772, p = 0.011) tests. The turning-based NIDTC reflected different rehabilitation effects between the AOT and CON groups, with the largest effect size (p = 0.020, Cohen's d = 0.933). CONCLUSION: The turning-based NIDTC exhibited the highest responsiveness for identifying gait automaticity improvement by providing a comprehensive representation of motor ability during dual tasks. It has great potential as a valid measure for early-stage PD diagnosis and rehabilitation assessment. Trial registration Chinese Clinical Trial Registry: ChiCTR2300067657.

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.

Effects of basic movements and gait in the case of homolateral imitative synkinesis.

BACKGROUND: Homolateral Imitative Synkinesis (HIS) is a rare form of associative movement between the ipsilateral upper and lower limbs. The incidence of HIS or its correlation with various movements remains uninvestigated. This study expounds on the characteristics of HIS, the frequency at which it occurs, and its relationship with movement, particularly walking. METHODS: This study included 1328 patients with acute stroke admitted to our healthcare facility between October 2019 and February 2022. We evaluated the severity of motor paralysis and sensory impairment in instances where HIS manifested, and assessed the relationship between HIS, basic activities, and gait. RESULTS: HIS was observed in 13/1328 patients. Motor paralysis was mild in all the cases. Each patient displayed a degree of sensory impairment, albeit of varying severity. HIS did not manifest during basic activities but was evident during walking movements in five instances. These patients displayed involuntary repetitive lifting of their upper limbs during the swing phase of their gait. Some individuals expressed discontent with involuntary upper-limb movements, citing them as contributors to a suboptimal gait. CONCLUSIONS: This study identified HIS as a rare syndrome, manifesting at a rate of 0.9%. Focus was more common in patients with damage to the thalamus and parietal lobe. No manifestations of the HIS occurred during basic activities, suggesting a weak correlation between the HIS and such activities. Certain patients exhibit HIS during gait, report suboptimal gait, and have an increased risk of falls, potentially influencing their gait proficiency.

Which Gait Tasks Produce Reliable Outcome Measures of Freezing of Gait in Parkinson's Disease?

Background: Measurement of freezing of gait (FOG) relies on the sensitivity and reliability of tasks to provoke FOG. It is currently unclear which tasks provide the best outcomes and how medication state plays into this. Objective: To establish the sensitivity and test-retest reliability of various FOG-provoking tasks for presence and severity of FOG, with (ON) and without (OFF) dopaminergic medication. Methods: FOG-presence and percentage time frozen (% TF) were derived from video annotations of a home-based FOG-provoking protocol performed in OFF and ON. This included: the four meter walk (4MW), Timed Up and Go (TUG) single (ST) and dual task (DT), 360° turns in ST and DT, a doorway condition, and a personalized condition. Sensitivity was tested at baseline in 63 definite freezers. Test-retest reliability was evaluated over 5 weeks in 26 freezers. Results: Sensitivity and test-retest reliability were highest for 360° turns and higher in OFF than ON. Test-retest intra-class correlation coefficients of % TF varied between 0.63-0.90 in OFF and 0.18-0.87 in ON, and minimal detectable changes (MDCs) were high. The optimal protocol included TUG ST, 360° turns ST, 360° turns DT and a doorway condition, provoking FOG in all freezers in OFF and 91.9% in ON and this could be done reliably in 95.8% (OFF) and 84.0% (ON) of the sample. Combining OFF and ON further improved outcomes. Conclusions: The highest sensitivity and reliability was achieved with a multi-trigger protocol performed in OFF + ON. However, the high MDCs for % TF underscore the need for further optimization of FOG measurement.

Freezing of gait is a very burdensome and episodic symptom in Parkinson's disease that is difficult to measure. Measurement of freezing is needed to determine whether someone has freezing and how severe this is, and relies on observation during a freezing-triggering protocol. However, it is unclear what protocol is sufficiently sensitive to trigger freezing in many freezers, and whether freezing can be triggered reliably at different timepoints. Here, we investigated 1) which tasks can trigger freezing-presence and freezing-severity sensitively and reliably, 2) how medication state influences this, and 3) what task combination was most reliable. Sixty-three patients with daily freezing performed several freezing-triggering tasks in their homes, both with (ON) and without (OFF) anti-Parkinsonian medication. In twenty-six patients, the measurement was repeated 5 weeks later to determine test-retest reliability. First, we found that performing 360° turns in place with a cognitive dual task was the most sensitive and reliable task to trigger FOG. Second, sensitivity and reliability were better in OFF than in ON. Third, the most reliable protocol included: the Timed-Up and Go, 360° turns in place with and without the dual task, and a doorway condition. This protocol triggered freezing in all patients in OFF and 91.9% in ON and did so reliably in 95.8% (OFF) and 84.0% (ON) of the sample. We recommend to measure freezing with this protocol in OFF + ON, which further improved reliability. However, the measurement error for freezing-severity was high, even for this optimal protocol, underscoring the need for further optimization of freezing measurement.

Simultaneous time-frequency analysis of gait signals of both legs in classifying neurodegenerative diseases.

BACKGROUND: Neurodegenerative diseases (NDDs) pose significant challenges due to their debilitating nature and limited therapeutic options. Accurate and timely diagnosis is crucial for optimizing patient care and treatment strategies. Gait analysis, utilizing wearable sensors, has shown promise in assessing motor abnormalities associated with NDDs. RESEARCH QUESTION: Research Question 1 To what extent can analyzing the interaction of both limbs in the time-frequency domain serve as a suitable methodology for accurately classifying NDDs? Research Question 2 How effective is the utilization of color-coded images, in conjunction with deep transfer learning models, for the classification of NDDs? METHODS: GaitNDD database was used, comprising recordings from patients with Huntington's disease, amyotrophic lateral sclerosis, Parkinson's disease, and healthy controls. The gait signals underwent signal preparation, wavelet coherence analysis, and principal component analysis for feature enhancement. Deep transfer learning models (AlexNet, GoogLeNet, SqueezeNet) were employed for classification. Performance metrics, including accuracy, sensitivity, specificity, precision, and F1 score, were evaluated using 5-fold cross-validation. RESULTS: The classification performance of the models varied depending on the time window used. For 5-second gait signal segments, AlexNet achieved an accuracy of 95.91 %, while GoogLeNet and SqueezeNet achieved accuracies of 96.49 % and 92.73 %, respectively. For 10-second segments, AlexNet outperformed other models with an accuracy of 99.20 %, while GoogLeNet and SqueezeNet achieved accuracies of 96.75 % and 95.00 %, respectively. Statistical tests confirmed the significance of the extracted features, indicating their discriminative power for classification. SIGNIFICANCE: The proposed method demonstrated superior performance compared to previous studies, offering a non-invasive and cost-effective approach for the automated diagnosis of NDDs. By analyzing the interaction between both legs during walking using wavelet coherence, and utilizing deep transfer learning models, accurate classification of NDDs was achieved.

Right putamen and claustrum infarction mimicking normal pressure hydrocephalus.

A woman in her 70s presented with approximately 2 years of sudden-onset gait and cognitive problems. She had been diagnosed with normal pressure hydrocephalus (NPH) and underwent ventriculoperitoneal shunt (VPS) placement 1 year prior. Before VPS placement, brain imaging showed ventriculomegaly and chronic infarction of the right putamen and claustrum. A lumbar drain trial resulted in modest improvement of gait dysfunction. She underwent VPS placement for suspected NPH, but her symptoms remained unchanged. Examination revealed mild cognitive impairment, left-sided and lower body predominant parkinsonism, as well as disproportionately prominent postural instability. Gait analysis showed increased gait variability, reduced velocity and shortened step length bilaterally. Motor and gait abnormalities did not change after administration of levodopa. Her symptoms have remained stable for up to 52 months since symptom onset. We postulate that the infarction affecting the right putamen and claustrum could have led to a higher-level gait disorder mimicking NPH.

Psychometric properties of wearable technologies to assess post-stroke gait parameters: A systematic review.

BACKGROUND: Wearable technologies using inertial sensors are an alternative for gait assessment. However, their psychometric properties in evaluating post-stroke patients are still being determined. This systematic review aimed to evaluate the psychometric properties of wearable technologies used to assess post-stroke gait and analyze their reliability and measurement error. The review also investigated which wearable technologies have been used to assess angular changes in post-stroke gait. METHODS: The present review included studies in English with no publication date restrictions that evaluated the psychometric properties (e.g., validity, reliability, responsiveness, and measurement error) of wearable technologies used to assess post-stroke gait. Searches were conducted from February to March 2023 in the following databases: Cochrane Central Registry of Controlled Trials (CENTRAL), Medline/PubMed, EMBASE Ovid, CINAHL EBSCO, PsycINFO Ovid, IEEE Xplore Digital Library (IEEE), and Physiotherapy Evidence Database (PEDro); the gray literature was also verified. The Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) risk-of-bias tool was used to assess the quality of the studies that analyzed reliability and measurement error. RESULTS: Forty-two studies investigating validity (37 studies), reliability (16 studies), and measurement error (6 studies) of wearable technologies were included. Devices presented good reliability in measuring gait speed and step count; however, the quality of the evidence supporting this was low. The evidence of measurement error in step counts was indeterminate. Moreover, only two studies obtained angular results using wearable technology. SIGNIFICANCE: Wearable technologies have demonstrated reliability in analyzing gait parameters (gait speed and step count) among post-stroke patients. However, higher-quality studies should be conducted to improve the quality of evidence and to address the measurement error assessment. Also, few studies used wearable technology to analyze angular changes during post-stroke gait.

Machine learning for automating subjective clinical assessment of gait impairment in people with acquired brain injury - a comparison of an image extraction and classification system to expert scoring.

BACKGROUND: Walking impairment is a common disability post acquired brain injury (ABI), with visually evident arm movement abnormality identified as negatively impacting a multitude of psychological factors. The International Classification of Functioning, Disability and Health (ICF) qualifiers scale has been used to subjectively assess arm movement abnormality, showing strong intra-rater and test-retest reliability, however, only moderate inter-rater reliability. This impacts clinical utility, limiting its use as a measurement tool. To both automate the analysis and overcome these errors, the primary aim of this study was to evaluate the ability of a novel two-level machine learning model to assess arm movement abnormality during walking in people with ABI. METHODS: Frontal plane gait videos were used to train four networks with 50%, 75%, 90%, and 100% of participants (ABI: n = 42, healthy controls: n = 34) to automatically identify anatomical landmarks using DeepLabCut™ and calculate two-dimensional kinematic joint angles. Assessment scores from three experienced neurorehabilitation clinicians were used with these joint angles to train random forest networks with nested cross-validation to predict assessor scores for all videos. Agreement between unseen participant (i.e. test group participants that were not used to train the model) predictions and each individual assessor's scores were compared using quadratic weighted kappa. One sample t-tests (to determine over/underprediction against clinician ratings) and one-way ANOVA (to determine differences between networks) were applied to the four networks. RESULTS: The machine learning predictions have similar agreement to experienced human assessors, with no statistically significant (p < 0.05) difference for any match contingency. There was no statistically significant difference between the predictions from the four networks (F = 0.119; p = 0.949). The four networks did however under-predict scores with small effect sizes (p range = 0.007 to 0.040; Cohen's d range = 0.156 to 0.217). CONCLUSIONS: This study demonstrated that machine learning can perform similarly to experienced clinicians when subjectively assessing arm movement abnormality in people with ABI. The relatively small sample size may have resulted in under-prediction of some scores, albeit with small effect sizes. Studies with larger sample sizes that objectively and automatically assess dynamic movement in both local and telerehabilitation assessments, for example using smartphones and edge-based machine learning, to reduce measurement error and healthcare access inequality are needed.

Circular walking is useful for assessing the risk of falls in early progressive supranuclear palsy.

BACKGROUND: Progressive supranuclear palsy (PSP) is characterized by early onset postural instability and frequent falls. Circular walking necessitates dynamic postural control, which is impaired in patients with PSP. We aimed to explore gait parameters associated with the risk of falls in patients with PSP, focusing on circular walking. METHODS: Sixteen drug-naïve patients with PSP, 22 drug-naïve patients with Parkinson's disease (PD), and 23 healthy controls were enrolled. Stride lengths/velocities and their coefficients of variation (CV) during straight and circular walking (walking around a circle of 1-m diameter) were measured under single-task and cognitive dual-task conditions. Correlation analysis was performed between gait parameters and postural instability and gait difficulty (PIGD) motor subscores, representing the risk of falls. RESULTS: Patients with PSP had significantly higher CVs of stride lengths/velocities during circular walking than those during straight walking, and the extent of exacerbation of CVs in patients with PSP was larger than that in patients with PD under single-task conditions. Stride lengths/velocities and their CVs were significantly correlated with PIGD motor subscores in patients with PSP only during single-task circular walking. In addition, patients with PSP showed progressive decrements of stride lengths/velocities over steps only during single-task circular walking. CONCLUSIONS: Worse gait parameters during circular walking are associated with an increased risk of falls in patients with PSP. Circular walking is a challenging task to demand the compromised motor functions of patients with PSP, unmasking impaired postural control and manifesting sequence effect. Assessing circular walking is useful for evaluating the risk of falls in patients with early PSP.

Cognitive-motor dual task to reveal gait impairments in multiple sclerosis patients at an early stage: A systematic review.

BACKGROUND: Multiple sclerosis can cause locomotor and cognitive impairments even at lower levels of disability, which can impact daily life. The cognitive-motor dual task is commonly used to assess everyday locomotion. Thus, this study aimed to examine the effect of cognitive-motor dual tasks on gait parameters among patients with multiple sclerosis in the early disease stages and to determine whether dual tasks could be used as a clinical test to detect locomotion impairments. METHODS: A systematic search of five databases was conducted in May 2024. The population of interest was patients with multiple sclerosis with an Expanded Disability Status Scale score of 4 or less. The following outcome measures were examined: spatiotemporal and kinematic parameters. The Newcastle-Ottawa Scale was used to assess the quality of the studies. FINDINGS: Eleven studies including 270 patients with multiple sclerosis and 221 healthy controls. Three spatiotemporal parameters were modified both in patients with multiple sclerosis and healthy controls during dual-task performance: gait speed, stride length and the double support phase. No spatiotemporal parameter was affected during dual-task performance in patients with multiple sclerosis alone. INTERPRETATION: Dual-task performance could be useful for assessing gait impairments in patients with multiple sclerosis provided that assessments and protocols are standardized. Nevertheless, the spatiotemporal parameters did not allow discrimination between patients with multiple sclerosis at an early stage and healthy controls. Three-dimensional gait analysis during dual-task performance could be a useful approach for detecting early gait impairments in patients with multiple sclerosis, assessing their progression and adjusting rehabilitation programs.

Automatic Detection and Assessment of Freezing of Gait Manifestations.

Freezing of gait (FOG) is an episodic and highly disabling symptom of Parkinson's disease (PD). Although described as a single phenomenon, FOG is heterogeneous and can express as different manifestations, such as trembling in place or complete akinesia. We aimed to analyze the efficacy of deep learning (DL) trained on inertial measurement unit data to classify FOG into both manifestations. We adapted and compared four state-of-the-art FOG detection algorithms for this task and investigated the advantages of incorporating a refinement model to address oversegmentation errors. We evaluated the model's performance in distinguishing between trembling and akinesia, as well as other forms of movement cessation (e.g., stopping and sitting), against gold-standard video annotations. Experiments were conducted on a dataset of eighteen PD patients completing a FOG-provoking protocol in a gait laboratory. Results showed our model achieved an F1 score of 0.78 and segment F1@50 of 0.75 in detecting FOG manifestations. Assessment of FOG severity was strong for trembling (ICC=0.86, [0.66,0.95]) and moderately strong for akinesia (ICC=0.78, [0.51,0.91]). Importantly, our model successfully differentiated FOG from other forms of movement cessation during 360-degree turning-in-place tasks. In conclusion, our study demonstrates that DL can accurately assess different types of FOG manifestations, warranting further investigation in larger and more diverse verification cohorts.

Prediction of Freezing of Gait in Parkinson's disease based on multi-channel time-series neural network.

Freezing of Gait (FOG) is a noticeable symptom of Parkinson's disease, like being stuck in place and increasing the risk of falls. The wearable multi-channel sensor system is an efficient method to predict and monitor the FOG, thus warning the wearer to avoid falls and improving the quality of life. However, the existing approaches for the prediction of FOG mainly focus on a single sensor system and cannot handle the interference between multi-channel wearable sensors. Hence, we propose a novel multi-channel time-series neural network (MCT-Net) approach to merge multi-channel gait features into a comprehensive prediction framework, alerting patients to FOG symptoms in advance. Owing to the causal distributed convolution, MCT-Net is a real-time method available to give optimal prediction earlier and implemented in remote devices. Moreover, intra-channel and inter-channel transformers of MCT-Net extract and integrate different sensor position features into a unified deep learning model. Compared with four other state-of-the-art FOG prediction baselines, the proposed MCT-Net obtains 96.21% in accuracy and 80.46% in F1-score on average 2 s before FOG occurrence, demonstrating the superiority of MCT-Net.

Cognitive driven gait freezing phase detection and classification for neuro-rehabilitated patients using machine learning algorithms.

BACKGROUND: The significance of diagnosing illnesses associated with brain cognitive and gait freezing phase patterns has led to a recent surge in interest in the study of gait for mental disorders. A more precise and effective way to characterize and classify many common gait problems, such as foot and brain pulse disorders, can improve prognosis evaluation and treatment options for Parkinson patients. Nonetheless, the primary clinical technique for assessing gait abnormalities at the moment is visual inspection, which depends on the subjectivity of the observer and can be inaccurate. RESEARCH QUESTION: This study investigates whether it is possible to differentiate between gait brain disorder and the typical walking pattern using machine learning driven supervised learning techniques and data obtained from inertial measurement unit sensors for brain, hip and leg rehabilitation. METHOD: The proposed method makes use of the Daphnet freezing of Gait Data Set, consisted of 237 instances with 9 attributes. The method utilizes machine learning and feature reduction approaches in leg and hip gait recognition. RESULTS: From the obtained results, it is concluded that among all classifiers RF achieved highest accuracy as 98.9 % and Perceptron achieved lowest i.e. 70.4 % accuracy. While utilizing LDA as feature reduction approach, KNN, RF and NB also achieved promising accuracy and F1-score in comparison with SVM and LR classifiers. SIGNIFICANCE: In order to distinguish between the different gait disorders associated with brain tissues freezing/non-freezing and normal walking gait patterns, it is shown that the integration of different machine learning algorithms offers a viable and prospective solution. This research implies the need for an impartial approach to support clinical judgment.

Insights into Parkinson's Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis.

Parkinson's Disease (PD) is a complex neurodegenerative disorder characterized by a spectrum of motor and non-motor symptoms, prominently featuring the freezing of gait (FOG), which significantly impairs patients' quality of life. Despite extensive research, the precise mechanisms underlying FOG remain elusive, posing challenges for effective management and treatment. This paper presents a comprehensive meta-analysis of FOG prediction and detection methodologies, with a focus on the integration of wearable sensor technology and machine learning (ML) approaches. Through an exhaustive review of the literature, this study identifies key trends, datasets, preprocessing techniques, feature extraction methods, evaluation metrics, and comparative analyses between ML and non-ML approaches. The analysis also explores the utilization of cueing devices. The limited adoption of explainable AI (XAI) approaches in FOG prediction research represents a significant gap. Improving user acceptance and comprehension requires an understanding of the logic underlying algorithm predictions. Current FOG detection and prediction research has a number of limitations, which are identified in the discussion. These include issues with cueing devices, dataset constraints, ethical and privacy concerns, financial and accessibility restrictions, and the requirement for multidisciplinary collaboration. Future research avenues center on refining explainability, expanding and diversifying datasets, adhering to user requirements, and increasing detection and prediction accuracy. The findings contribute to advancing the understanding of FOG and offer valuable guidance for the development of more effective detection and prediction methodologies, ultimately benefiting individuals affected by PD.