Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis

Vision-based motion capture for the gait analysis of neurodegenerative diseases: A review

BACKGROUND

Developments in vision-based systems and human pose estimation algorithms have the potential to detect, monitor and intervene early on neurodegenerative diseases through gait analysis. However, the gap between the technology available and actual clinical practice is evident as most clinicians still rely on subjective observational gait analysis or objective marker-based analysis that is time-consuming.

RESEARCH QUESTION

This paper aims to examine the main developments of vision-based motion capture and how such advances may be integrated into clinical practice.

METHODS

The literature review was conducted in six online databases using Boolean search terms. A commercial system search was also included. A predetermined methodological criterion was then used to assess the quality of the selected articles.

RESULTS

A total of seventeen studies were evaluated, with thirteen studies focusing on gait classification systems and four studies on gait measurement systems. Of the gait classification systems, nine studies utilized artificial intelligence-assisted techniques, while four studies employed statistical techniques. The results revealed high correlations of gait features identified by classifier models with existing clinical rating scales. These systems demonstrated generally high classification accuracies and were effective in diagnosing disease severity levels. Gait measurement systems that extract spatiotemporal and kinematic joint information from video data generally found accurate measurements of gait parameters with low mean absolute errors, high intra- and inter-rater reliability.

SIGNIFICANCE

Low cost, portable vision-based systems can provide proof of concept for the quantification of gait, expansion of gait assessment tools, remote gait analysis of neurodegenerative diseases and a point of care system for orthotic evaluation. However, certain challenges, including small sample sizes, occlusion risks, and selection bias in training models, need to be addressed. Nevertheless, these systems can serve as complementary tools, equipping clinicians with essential gait information to objectively assess disease severity and tailor personalized treatment for enhanced patient care.

Inter-trial variability is higher in 3D markerless compared to marker-based motion capture: Implications for data post-processing and analysis

Markerless motion capture has recently attracted significant interest in clinical gait analysis and human movement science. Its ease of use and potential to streamline motion capture recordings bear great potential for out-of-the-laboratory measurements in large cohorts. While previous studies have shown that markerless systems can achieve acceptable accuracy and reliability for kinematic parameters of gait, they also noted higher inter-trial variability of markerless data. Since increased inter-trial variability can have important implications for data post-processing and analysis, this study compared the inter-trial variability of simultaneously recorded markerless and marker-based data. For this purpose, the data of 18 healthy volunteers were used who were instructed to simulate four different gait patterns: physiological, crouch, circumduction, and equinus gait. Gait analysis was performed using the smartphone-based markerless system OpenCap and a marker-based motion capture system. We compared the inter-trial variability of both systems and also evaluated if changes in inter-trial variability may depend on the analyzed gait pattern. Compared to the marker-based data, we observed an increase of inter-trial variability for the markerless system ranging from 6.6% to 22.0% for the different gait patterns. Our findings demonstrate that the markerless pose estimation pipelines can introduce additionally variability in the kinematic data across different gait patterns and levels of natural variability. We recommend using averaged waveforms rather than single ones to mitigate this problem. Further, caution is advised when using variability-based metrics in gait and human movement analysis based on markerless data as increased inter-trial variability can lead to misleading results.

Fractals in Neuropsychology and Cognitive Neuroscience

The fractal dimension of cognition refers to the idea that the cognitive processes of the human brain exhibit fractal properties. This means that certain patterns of cognitive activity, such as visual perception, memory, language, or problem-solving, can be described using the mathematical concept of fractal dimension.The idea that cognition is fractal has been proposed by some researchers as a way to understand the complex, self-similar nature of the human brain. However, it's a relatively new idea and is still under investigation, so it's not yet clear to what extent cognitive processes exhibit fractal properties or what implications this might have for our understanding of the brain and clinical practice. Indeed, the mission of the "fractal neuroscience" field is to define the characteristics of fractality in human cognition in order to differently characterize the emergence of brain disorders.

Blast Overpressures as a Military and Occupational Health Concern

PURPOSE

This tutorial reviews effects of environmental stressors like blast overpressures and other well-known acoustic contaminants (continuous, intermittent, and impulsive noise) on hearing, tinnitus, vestibular, and balance-related functions. Based on the overall outcome of these effects, detailed consideration is given to the health and well-being of individuals.

METHOD

Because hearing loss and tinnitus are consequential in affecting quality of life, novel neuromodulation paradigms are reviewed for their positive abatement and treatment-related effects. Examples of clinical data, research strategies, and methodological approaches focus on repetitive transcranial magnetic stimulation (rTMS) and electrical stimulation of the vagus nerve paired with tones (VNSt) for their unique contributions to this area.

RESULTS

Acoustic toxicants transmitted through the atmosphere are noteworthy for their propensity to induce hearing loss and tinnitus. Mounting evidence also indicates that high-level rapid onset changes in atmospheric sound pressure can significantly impact vestibular and balance function. Indeed, the risk of falling secondary to loss of, or damage to, sensory receptor cells in otolith organs (utricle and saccule) is a primary reason for this concern. As part of the complexities involved in VNSt treatment strategies, vocal dysfunction may also manifest. In addition, evaluation of temporospatial gait parameters is worthy of consideration based on their ability to detect and monitor incipient neurological disease, cognitive decline, and mortality.

CONCLUSION

Highlighting these respective areas underscores the need to enhance information exchange among scientists, clinicians, and caregivers on the benefits and complications of these outcomes.

Time series modeling characterizes stride time variability to identify individuals with neurodegenerative disorders

The progressive death and dysfunction of neurons causes altered stride-to-stride variability in individuals with Amyotrophic Lateral Sclerosis (ALS) and Huntington's Disease (HD). Yet these altered gait dynamics can manifest differently in these populations based on how and where these neurodegenerative disorders attack the central nervous system. Time series analyses can quantify differences in stride time variability which can help contribute to the detection and identification of these disorders. Here, autoregressive modeling time series analysis was utilized to quantify differences in stride time variability amongst the Controls, the individuals with ALS, and the individuals with HD. For this study, fifteen Controls, 12 individuals with ALS and 15 individuals with HD walked up and down a hallway continuously for 5-min. Participants wore force sensitive resistors in their shoes to collect stride time data. A second order autoregressive (AR) model was fit to the time series created from the stride time data. The mean stride time and two AR model coefficients served as metrics to identify differences in stride time variability amongst the three groups. The individuals with HD walked with significantly greater stride time variability indicating a more chaotic gait while the individuals with ALS adopted more ordered, less variable stride time dynamics (p < 0.001). A plot of the stride time metrics illustrated how each group exhibited significantly different stride time dynamics. The stride time metrics successfully quantified differences in stride time variability amongst individuals with neurodegenerative disorders. This work provided valuable insight about how these neuromuscular disorders disrupt motor coordination leading to the adoption of new gait dynamics.

The Impact of Stimulus Length in Tongue and Lip Movement Pattern Stability in Amyotrophic Lateral Sclerosis

PURPOSE

This study aimed to investigate the effect of stimulus signal length on tongue and lip motion pattern stability in speakers diagnosed with amyotrophic lateral sclerosis (ALS) compared to healthy controls.

METHOD

Electromagnetic articulography was used to derive articulatory motion patterns from individuals with mild (n = 27) and severe (n = 16) ALS and healthy controls (n = 25). The spatiotemporal index (STI) was used as a measure of articulatory stability. Two experiments were conducted to evaluate signal length effects on the STI: (a) the effect of the number of syllables on STI values and (b) increasing lengths of subcomponents of a single phrase. Two-way mixed analyses of variance were conducted to assess the effects of syllable length and group on the STI for the tongue tip (TT), tongue back (TB), and lower lip (LL).

RESULTS

Experiment 1 showed a significant main effect of syllable length (TT, p < .001; TB, p < .001; and LL, p < .001) and group (TT, p = .037; TB, p = .007; and LL, p = .017). TB and LL stability was generally higher with speech stimuli that included a greater number of syllables. Articulatory variability was significantly higher in speakers diagnosed with ALS compared to healthy controls. Experiment 2 showed a significant main effect of length (TT, p < .001; TB, p = .015; and LL, p < .001), providing additional support that STI values tend to be greater when calculated on longer speech signals.

CONCLUSIONS

Articulatory stability is influenced by the length of speech signals and manifests similarly in both healthy speakers and persons with ALS. TT stability may be significantly impacted by phonemic content due to greater movement flexibility. Compared to healthy controls, there was an increase in articulatory variability in those with ALS, which likely reflects deviations in speech motor control.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.24463924.

Altered gait parameters in distracted walking: a bio-evolutionary and prognostic health perspective on passive listening and active responding during cell phone use

The underpinnings of bipedal gait are reviewed from an evolutionary biology and prognostic health perspective to better understand issues and concerns related to cell phone use during ambulation and under conditions of distraction and interference. We also consider gait-related health issues associated with the fear of or risk of falling and include prognostic dimensions associated with cognitive decline, dementia, and mortality. Data were acquired on 21 healthy young adults without hearing loss, vestibular, balance, otological or neurological dysfunction using a computerized walkway (GAITRite® Walkway System) combined with specialized software algorithms to extract gait parameters. Four experimental conditions and seven temporo-spatial gait parameters were studied: gait velocity, cadence, stride length, ambulatory time, single-support time, double-support time, and step count. Significant main effects were observed for ambulation time, velocity, stride velocity, and double-support time. The greatest impact of distraction and interference occurred during the texting condition, although other significant effects occurred when participants were verbally responding to queries and passively listening to a story. These experimental observations show that relatively simple distraction and interference tasks implemented through the auditory sensory modality can induce significant perturbations in gait while individuals were ambulating and using a cell phone. Herein, emphasis is placed on the use of quantifiable gait parameters in medical, psychological, and audiological examinations to serve as a foundation for identifying and potentially averting gait-related disturbances.

Cognitive impairment is associated with gait variability and fall risk in amyotrophic lateral sclerosis

BACKGROUND

In amyotrophic lateral sclerosis (ALS), gait abnormalities contribute to poor mobility and represent a relevant risk for falls. To date, gait studies in ALS patients have focused on the motor dimension of the disease, underestimating the cognitive aspects.

METHODS

Using a wearable gait analysis device, we compared gait patterns in ambulatory ALS patients with mild cognitive impairment (ALS MCI+; n = 18), and without MCI (ALS MCI-; n = 24), and healthy subjects (HS; n = 16) under two conditions: (1) normal gait (single task) and (2) walking while counting backward (dual task). Finally, we examined if the occurrence and number of falls in the 3 months following the baseline test were related to cognition.

RESULTS

In the single task condition, ALS patients, regardless of cognition, displayed higher gait variability than HS, especially for stance and swing time (p < 0.001). The dual task condition revealed additional differences in gait variability parameters between ALS MCI+ and ALS MCI- for cadence (p = 0.005), stance time (p = 0.04), swing time (p = 0.04) and stability index (p = 0.02). Moreover, ALS MCI+ showed a higher occurrence (p = 0.001) and number of falls (p < 0.001) at the follow-up. Regression analyses demonstrated that MCI condition predicted the occurrence of future falls (β = 3.649; p = 0.01) and, together with executive dysfunction, was associated with the number of falls (cognitive impairment: β = 0.63; p < 0.001; executive dysfunction: β = 0.39; p = 0.03), regardless of motor impairment at clinical examination.

CONCLUSION

In ALS, MCI is associated with exaggerated gait variability and predicts the occurrence and number of short-term falls.

Application of vibration to the soles increases long-range correlations in the stride parameters during walking

Temporal fluctuations in the stride parameters during human walking exhibit long-range correlations, but these long-range correlations in the stride parameters decrease due to aging or neuromuscular diseases. These observations suggest that any quantified index of the long-range correlation can be regarded as an indicator of gait functionality. Considering the effect of task-relevant sensory feedback on augmenting human motor performance, we devised shoes with active insoles that could deliver noisy vibration to the soles of feet and assessed their efficacy in enhancing the long-range correlations in the stride parameters for healthy young adults. The vibration could be wirelessly controlled using a smartphone. The actuators, control unit, and battery in the devised shoes were light and embedded in the shoes. By virtue of this compactness, the shoes could be easily used for daily walking outside a laboratory. We performed walking experiments with 20 healthy adults and evaluated the effects of sub- and supra-threshold vibration on long-range correlations in stride interval and length. We performed detrended fluctuation analysis to quantify the long-range correlation of temporal changes in stride interval and length. We found that supra-threshold vibration, applied to the soles with the amplitude of 130 % of the sensory threshold, significantly increased the long-range correlations in stride interval and length by 10.3 % (p = 0.009) and 10.1 % (p = 0.021), respectively. On the other hand, sub-threshold vibration with the amplitude of 90 % of the sensory threshold had no significant effect. These results demonstrate that additional somatosensory feedback through barely detectable vibrations, which are supplied by compact shoes with active insoles, can enhance the indices of "healthy" complexity of locomotor function.

Adaptive walking assistance does not impact long-range stride-to-stride autocorrelations in healthy people

Many studies have demonstrated in the past that the level of long-range autocorrelations in series of stride durations, characterizing natural gait variability, is impacted by external constraints, such as treadmill or metronome, or by pathologies, such as Parkinson's or Huntington's disease. Nevertheless, no one has analyzed the effects on this metric of a gait constrained by a robot-mediated walking assistance, which intrinsically tends to normalize the gait pattern. This paper focuses on the influence of a wearable active pelvis orthosis on the level of long-range autocorrelations in series of stride durations. Ten healthy participants, aged between 55 and 77 yr, performed four overground walking sessions, wearing this orthosis, and with different assistive parameters. This study showed that the adaptive assistance provided by this device has the potential of improving gait metrics that are typically affected by aging, such as the hip range of motion, walking speed, stride length, and stride duration, without impacting natural gait variability, i.e., the level of long-range autocorrelations in series of stride durations. This combination is virtuous toward the design of an assistive device for people with locomotion disorders resulting in deteriorated levels of long-range autocorrelations, such as patients with Parkinson's disease.NEW & NOTEWORTHY This study is the first that investigates the effects of a wearable active pelvis orthosis using an oscillator-based adaptive assistance on the level of long-range autocorrelations in series of stride durations during overground walking. It is also the first to compare the effects of different assistance settings on spatiotemporal gait metrics.

The impact of neck pain on gait health: a systematic review and meta-analysis

BACKGROUND

Evidence exists demonstrating the negative impacts of chronic musculoskeletal pain on key measures of gait. Despite neck pain being the second most common musculoskeletal pain condition, there is a paucity of evidence exploring the impacts of neck pain specifically on these outcomes. The aims of this work were to systematically review the current evidence of the associations between chronic neck pain and measures of gait health and to conduct meta-analysis for quantitative assessment of the effect sizes under different walking conditions.

METHODS

Systematic review was conducted following PRISMA guidelines. Databases searched included MEDLINE, Embase, Web of Science, CINAHL, and PEDro. Eligible study designs included observational studies consisting of an exposure group with chronic neck pain and control group without chronic neck pain and primary outcomes relating to gait health. For outcomes amenable to meta-analysis, a random-effects model was used to derive summary estimates of Hedge's g depicted graphically with forest plots. Other gait outcomes were narratively summarized. Risk of bias was also assessed.

RESULTS

The original search yielded 1918 articles; 12 met final eligibility criteria including 10 cross-sectional studies. Outcomes were grouped first by the five domains of gait: pace, rhythm, asymmetry, variability, and postural control; and second by the tested walking conditions. Meta-analyses for gait speed revealed large effect-sizes indicating that individuals with chronic neck pain had slower measures of gait and lower measures of cadence. Gait outcomes that were narratively summarized supported these findings.

CONCLUSION

The quantitative and qualitative findings of this systematic review and meta-analysis suggest a negative impact of CNNP on measures of gait health, particularly gait speed, under various walking conditions. However, broad interpretation of these results should be cautious. Testing gait under dual task conditions may be particularly sensitive to the impact of CNNP, and future work is needed to better understand how pain disrupts this important functionality of the locomotor system. Additionally, consideration should be made to assess measures of variability and investigate these relationships in the older adult population.

Early Diagnosis of Neurodegenerative Diseases Using CNN-LSTM and Wavelet Transform

Early diagnosis of neurodegenerative diseases has always been a major challenge that physicians and medical practitioners face. Therefore, using any method or device that helps with prognostics is of great importance. In recent years, deep neural networks have become popular in medical fields, and the reason is that these networks can help diagnose diseases quickly and precisely. In this research, two novel models based on a CNN-LSTM network are introduced. The main goal is to classify three neurodegenerative diseases, including ALS, Parkinson's disease, and Huntington's disease, from one another and from healthy control patients using the gait signals, which are transformed into spectrogram images. In the first model, the spectrogram images derived from the gait signals are fed into a CNN-LSTM network directly. This model achieved 99.42% accuracy. In the second model, the same input data was used to be classified using a CNN-LSTM network, which uses wavelet transform as a feature extractor before the LSTM unit. During the experiments with the second model, the detail sub-bands were eliminated one by one, and the classification results were compared. Comparing these two models has shown that using the wavelet transform and, in particular, the approximation sub-bands can result in a lighter and faster prognosis with nearly 103 times fewer training parameters overall. The classification result using only approximation sub-bands was 95.37%, using three sub-bands was 94.04%, and including all sub-bands was 94.53%, which is remarkable.

Neurodegenerative diseases detection and grading using gait dynamics

Detection of neurodegenerative diseases such as Parkinson's disease, Huntington's disease, Amyotrophic Lateral Sclerosis, and grading of these diseases' severity have high clinical significance. These tasks based on walking analysis stand out compared to other methods due to their simplicity and non-invasiveness. This study has emerged to realize an artificial intelligence-based disease detection and severity prediction system for neurodegenerative diseases using gait features obtained from gait signals. For the detection of the disease, the problem is divided into parts which are subgroups of 4 classes consisting of Parkinson's, Huntington's, Amyotrophic Lateral Sclerosis diseases, and the control group. In addition, the disease vs. control subgroup where all diseases are collected under a single label, the subgroups where each disease is separately against the control group. For disease severity grading, each disease was divided into subgroups and a solution was sought for the prediction problem mentioned by various machine and deep learning methods separately for each group. In this context, the resulting detection performance was measured by the metrics of Accuracy, F1 Score, Precision, and Recall while the resulting prediction performance was measured by the metrics such as R, R2, MAE, MedAE, MSE, and RMSE.

Baseline Gait and Motor Function Predict Long-Term Severity of Neurological Outcomes of Viral Infection

Neurological dysfunction following viral infection varies among individuals, largely due to differences in their genetic backgrounds. Gait patterns, which can be evaluated using measures of coordination, balance, posture, muscle function, step-to-step variability, and other factors, are also influenced by genetic background. Accordingly, to some extent gait can be characteristic of an individual, even prior to changes in neurological function. Because neuromuscular aspects of gait are under a certain degree of genetic control, the hypothesis tested was that gait parameters could be predictive of neuromuscular dysfunction following viral infection. The Collaborative Cross (CC) mouse resource was utilized to model genetically diverse populations and the DigiGait treadmill system used to provide quantitative and objective measurements of 131 gait parameters in 142 mice from 23 CC and SJL/J strains. DigiGait measurements were taken prior to infection with the neurotropic virus Theiler's Murine Encephalomyelitis Virus (TMEV). Neurological phenotypes were recorded over 90 days post-infection (d.p.i.), and the cumulative frequency of the observation of these phenotypes was statistically associated with discrete baseline DigiGait measurements. These associations represented spatial and postural aspects of gait influenced by the 90 d.p.i. phenotype score. Furthermore, associations were found between these gait parameters with sex and outcomes considered to show resistance, resilience, or susceptibility to severe neurological symptoms after long-term infection. For example, higher pre-infection measurement values for the Paw Drag parameter corresponded with greater disease severity at 90 d.p.i. Quantitative trait loci significantly associated with these DigiGait parameters revealed potential relationships between 28 differentially expressed genes (DEGs) and different aspects of gait influenced by viral infection. Thus, these potential candidate genes and genetic variations may be predictive of long-term neurological dysfunction. Overall, these findings demonstrate the predictive/prognostic value of quantitative and objective pre-infection DigiGait measurements for viral-induced neuromuscular dysfunction.

A non-invasive method for prediction of neurodegenerative diseases using gait signal features

The steady degeneration of neurons is the hallmark of neurodegenerative illnesses, which are, by definition, incurable. Corticobasal Syndrome (CS), Huntington's Disease (HD), Dementia, Amyotrophic Lateral Sclerosis (ALS), Progressive supranuclear palsy (PSP) and Parkinson's Disease (PD) are some of the common neurodegenerative diseases which has impacted millions of people, predominantly among the older population. Various computational techniques, including but not limited to machine learning, are emerging as discrimination and detection of neuro-related diseases. This research proposed a machine learning-based framework to correctly detect PD, HD, and ALS from the gait signals of subjects both in binary and multi-class detection environment. The detection approach proposed here combines the classification power of Naïve Bayes and Logistic Regression jointly in a modern UltraBoost ensemble framework. The proposed method is unique in its ability to detect neuro diseases with a small number of gait features. The proposed approach ascertains most essential gait features through three state-of-the-art feature selection schemes, infinite feature selection, infinite latent feature selection and Sigmis feature selection. It has been observed that the gait signal features of the subjects are identified through Infinite Feature Selection manifests better detection results than the features obtained through Infinite Latent Feature and Sigmis feature selection while detecting Parkinson's and Huntington's Disease in a multi-class environment. So far as the binary detection environment is concern, the Amyotrophic lateral sclerosis is detected with 99.1% detection accuracy using 18 Sigmis gait features, with 99.1% sensitivity and 98.9% specificity, respectively. Similarly, Huntington's disease was detected with 94.2% detection accuracy, 94.2% sensitivity, and 94.5% specificity using 5 Sigmis gait features. Finally, Parkinson's disease was detected with 98.4% sensitivity, specificity, and detection accuracy.

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

Objective

Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases.

Methods

A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection.

Results

The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance.

Conclusions

This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.

Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors

Mobility impairments are a common symptom of age-related degenerative diseases. Gait features can discriminate those with mobility disorders from healthy individuals, yet phenotyping specific pathologies remains challenging. This study aims to identify if gait parameters derived from two foot-mounted inertial measurement units (IMU) during the 6 min walk test (6MWT) can phenotype mobility impairment from different pathologies (Lumbar spinal stenosis (LSS)-neurogenic diseases, and knee osteoarthritis (KOA)-structural joint disease). Bilateral foot-mounted IMU data during the 6MWT were collected from patients with LSS and KOA and matched healthy controls (N = 30, 10 for each group). Eleven gait parameters representing four domains (pace, rhythm, asymmetry, variability) were derived for each minute of the 6MWT. In the entire 6MWT, gait parameters in all four domains distinguished between controls and both disease groups; however, the disease groups demonstrated no statistical differences, with a trend toward higher stride length variability in the LSS group (p = 0.057). Additional minute-by-minute comparisons identified stride length variability as a statistically significant marker between disease groups during the middle portion of 6WMT (3rd min: p ≤ 0.05; 4th min: p = 0.06). These findings demonstrate that gait variability measures are a potential biomarker to phenotype mobility impairment from different pathologies. Increased gait variability indicates loss of gait rhythmicity, a common feature in neurologic impairment of locomotor control, thus reflecting the underlying mechanism for the gait impairment in LSS. Findings from this work also identify the middle portion of the 6MWT as a potential window to detect subtle gait differences between individuals with different origins of gait impairment.

Parkinson's Disease Wearable Gait Analysis: Kinematic and Dynamic Markers for Diagnosis

Introduction: Gait features differ between Parkinson's disease (PD) and healthy subjects (HS). Kinematic alterations of gait include reduced gait speed, swing time, and stride length between PD patients and HS. Stride time and swing time variability are increased in PD patients with respect to HS. Additionally, dynamic parameters of asymmetry of gait are significantly different among the two groups. The aim of the present study is to evaluate which kind of gait analysis (dynamic or kinematic) is more informative to discriminate PD and HS gait features. Methods: In the present study, we analyzed gait dynamic and kinematic features of 108 PD patients and 88 HS from four cohorts of two datasets. Results: Kinematic features showed statistically significant differences among PD patients and HS for gait speed and time Up and Go test and for selected kinematic dispersion indices (standard deviation and interquartile range of swing, stance, and double support time). Dynamic features did not show any statistically significant difference between PD patients and HS. Discussion: Despite kinematics features like acceleration being directly proportional to dynamic features like ground reaction force, the results of this study showed the so-called force/rhythm dichotomy since kinematic features were more informative than dynamic ones.

Effects of rhythmic auditory cueing on gait variability and voluntary control of walking -a cross-sectional study

Temporal gait variability is strongly associated with motor function and falls in the context of numerous diseases. Rhythmic auditory cueing (RAC) can influence stride-to-stride time, although its effects on temporal gait variability remain unclear. Therefore, the aim of the present cross-disease study was to examine the effects of RAC on stride time variability (STV), as well as the factors affecting changes in STV during walking with RAC. Participants with post-stroke (n = 12) and orthopedic disease (n = 23) performed a random block design under four conditions: comfortable walking speed (CWS) and walking with RAC (RAC 0%, RAC +10%, RAC -10%). STV was measured along with co-contraction and inter-muscular coherence of the shank muscles during walking for each condition. The contributions of the muscle activity pattern and voluntary control to the change in STV between the CWS and RAC 0% conditions were examined using hierarchical multiple regression analysis. STV was significantly lower in the RAC 0% condition than in the CWS condition (p = 0.03). Hierarchical multiple regression analysis revealed that the change in STV was explained by STV in the CWS condition (β = -0.36) and by changes in co-contraction (β = 0.43) and inter-muscular coherence (β = 0.38) during the stance phase between the CWS and RAC 0% conditions (R² = 0.56, p < 0.001). These findings indicate that walking training with RAC is effective in reducing gait variability and immediately improves muscle activity patterns and excessive corticospinal activity.

Data Representativeness in Accessibility Datasets: A Meta-Analysis

As data-driven systems are increasingly deployed at scale, ethical concerns have arisen around unfair and discriminatory outcomes for historically marginalized groups that are underrepresented in training data. In response, work around AI fairness and inclusion has called for datasets that are representative of various demographic groups. In this paper, we contribute an analysis of the representativeness of age, gender, and race & ethnicity in accessibility datasets-datasets sourced from people with disabilities and older adults-that can potentially play an important role in mitigating bias for inclusive AI-infused applications. We examine the current state of representation within datasets sourced by people with disabilities by reviewing publicly-available information of 190 datasets, we call these accessibility datasets. We find that accessibility datasets represent diverse ages, but have gender and race representation gaps. Additionally, we investigate how the sensitive and complex nature of demographic variables makes classification difficult and inconsistent (e.g., gender, race & ethnicity), with the source of labeling often unknown. By reflecting on the current challenges and opportunities for representation of disabled data contributors, we hope our effort expands the space of possibility for greater inclusion of marginalized communities in AI-infused systems.

Multimodal Gait Recognition for Neurodegenerative Diseases

In recent years, single modality-based gait recognition has been extensively explored in the analysis of medical images or other sensory data, and it is recognized that each of the established approaches has different strengths and weaknesses. As an important motor symptom, gait disturbance is usually used for diagnosis and evaluation of diseases; moreover, the use of multimodality analysis of the patient's walking pattern compensates for the one-sidedness of single modality gait recognition methods that only learn gait changes in a single measurement dimension. The fusion of multiple measurement resources has demonstrated promising performance in the identification of gait patterns associated with individual diseases. In this article, as a useful tool, we propose a novel hybrid model to learn the gait differences between three neurodegenerative diseases, between patients with different severity levels of Parkinson's disease, and between healthy individuals and patients, by fusing and aggregating data from multiple sensors. A spatial feature extractor (SFE) is applied to generating representative features of images or signals. In order to capture temporal information from the two modality data, a new correlative memory neural network (CorrMNN) architecture is designed for extracting temporal features. Afterward, we embed a multiswitch discriminator to associate the observations with individual state estimations. Compared with several state-of-the-art techniques, our proposed framework shows more accurate classification results.

Petri net transition times as training features for multiclass models to support the detection of neurodegenerative diseases

This paper proposes the transition times of Petri net models of human gait as training features for multiclass random forests (RFs) and classification trees (CTs). These models are designed to support screening for neurodegenerative diseases. The proposed Petri net describes gait in terms of nine cyclic phases and the timing of the nine events that mark the transition between phases. Since the transition times between strides vary, each is represented as a random variable characterized by its mean and standard deviation. These transition times are calculated using the PhysioNet database of vertical ground reaction forces (VGRFs) generated by feet-ground contact. This database comprises the VGRFs of four groups: amyotrophic lateral sclerosis, the control group, Huntington's disease, and Parkinson disease. The RF produced an overall classification accuracy of 91%, and the specificities and sensitivities for each class were between 80% and 100%. However, despite this high performance, the RF-generated models demonstrated lack of interpretability prompted the training of a CT using identical features. The obtained tree comprised only four features and required a maximum of three comparisons. However, this simplification dramatically reduced the overall accuracy from 90.6% to 62.3%. The proposed set features were compared with those included in PhysioNet database of VGRFs. In terms of both the RF and CT, more accurate models were established using our features than those of the PhysioNet.

Imperative Role of Machine Learning Algorithm for Detection of Parkinson’s Disease: Review, Challenges and Recommendations

Parkinson’s disease (PD) is a neurodegenerative disease that affects the neural, behavioral, and physiological systems of the brain. This disease is also known as tremor. The common symptoms of this disease are a slowness of movement known as ‘bradykinesia’, loss of automatic movements, speech/writing changes, and difficulty with walking at early stages. To solve these issues and to enhance the diagnostic process of PD, machine learning (ML) algorithms have been implemented for the categorization of subjective disease and healthy controls (HC) with comparable medical appearances. To provide a far-reaching outline of data modalities and artificial intelligence techniques that have been utilized in the analysis and diagnosis of PD, we conducted a literature analysis of research papers published up until 2022. A total of 112 research papers were included in this study, with an examination of their targets, data sources and different types of datasets, ML algorithms, and associated outcomes. The results showed that ML approaches and new biomarkers have a lot of promise for being used in clinical decision-making, resulting in a more systematic and informed diagnosis of PD. In this study, some major challenges were addressed along with a future recommendation.

Hyperactivity of Purkinje cell and motor deficits in C9orf72 knockout mice

A hexanucleotide (GGGGCC) repeat expansion in the first intron of the C9ORF72 gene is the most frequently reported genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). The cerebellum has not traditionally been thought to be involved in the pathogenesis of C9ORF72-associated ALS/FTD, but recent evidence suggested a potential role. C9ORF72 is highly expressed in the cerebellum. Decreased C9ORF72 transcript and protein levels were detected in the postmortem cerebellum, suggesting a loss-of-function effect of C9ORF72 mutation. This study investigated the role of loss of C9ORF72 function using a C9orf72 knockout mouse line. C9orf72 deficiency led to motor impairment in rotarod, beam-walking, paw-print, open-field, and grip-strength tests. Purkinje cells are the sole output neurons in the cerebellum, and we next determined their involvement in the motor phenotypes. We found hyperactivity of Purkinje cells in the C9orf72 knockout mouse accompanied by a significant increase of the large-conductance calcium-activated potassium channel (BK) protein in the cerebellum. The link between BK and Purkinje cell firing was demonstrated by the acute application of the BK activator that increased the firing frequency of the Purkinje cells ex vivo. In vivo chemogenetic activation of Purkinje cells in wild-type mice led to similar motor deficits in rotarod and beam-walking tests. Our results highlight that C9ORF72 loss alters the activity of the Purkinje cell and potentially the pathogenesis of the disease. Manipulating the Purkinje cell firing or cerebellar output may contribute to C9ORF72-associated ALS/FTD treatment.

Comparing adaptive fractal and detrended fluctuation analyses of stride time variability: Tests of equivalence

BACKGROUND

Fractal analyses quantify self-similarities in stride-to-stride fluctuations over different time scales. Fractal exponents can be measured with adaptive fractal analysis (AFA) or detrended fluctuation analysis (DFA), though measurements obtained with the algorithms have not been directly compared.

RESEARCH QUESTION

Are stride time fractal exponents measured with AFA and DFA algorithms equivalent?

METHODS

Data from 50 participants with Parkinson's Disease (n = 15), age-similar healthy adults (n = 15) and healthy young adults (n = 20) were analyzed in this cross-sectional, observational study. Participants completed 6-min walks at self-selected speeds overground on a straight walkway and on a treadmill. Stride times were measured with inertial measurement units. Fractal exponents in stride time data were processed using AFA and DFA algorithms and compared with two one-sided tests of equivalence. Mixed ANOVAs were used to compare exponents between groups and conditions.

RESULTS

Fractal exponents computed with AFA and DFA were equivalent neither in the overground (0.796 & 0.830, respectively, p = .587) nor treadmill conditions (0.806 & 0.882, respectively, p = .122). Fractal exponents measured with DFA were higher than when measured with AFA. Standard errors were 22% lower when measured with AFA. Additionally, a group × condition interaction was statistically significant when fractal exponents were processed with the AFA algorithm (F(2,47) = 11.696, p < .001), whereas the group × condition interaction was not statistically significant when DFA exponents were compared (F(2, 47) = 2.144, p = .129).

SIGNIFICANCE

AFA and DFA do not produce equivalent estimates of the fractal exponent α in stride time dynamics. Estimates of the fractal exponent α obtained with AFA or DFA algorithms therefore should not be used interchangeably. Standard errors were lower when derived with AFA. Fractal exponents calculated with AFA may be more sensitive to conditions that influence stride time fractal dynamics than are measures calculated with DFA.

Effects of Transcranial Direct Current Electrical Stimulation over the Supplementary Motor Area Combined with Walking on the Intramuscular Coherence of the Tibialis Anterior in a Subacute Post-Stroke Patient: A Single-Case Study

Motor recovery is related to the corticospinal tract (CST) lesion in post-stroke patients. The CST originating from the supplementary motor area (SMA) affects the recovery of impaired motor function. We confirmed the effects of transcranial direct current stimulation (tDCS) over the SMA combined with walk training on CST excitability. This study involved a stroke patient with severe sensorimotor deficits and a retrospective AB design. Walk training was conducted only in phase A. Phase B consisted of anodal tDCS (1.5 mA) combined with walk training. Walking speed, stride time variability (STV; reflecting gait stability), and beta-band intramuscular coherence—derived from the paired tibialis anterior on the paretic side (reflecting CST excitability)—were measured. STV quantified the coefficient of variation in stride time using accelerometers. Intramuscular coherence during the early stance phase noticeably increased in phase B compared with phase A. Intramuscular coherence in both the stance and swing phases was reduced at follow-up. Walking speed showed no change, while STV was noticeably decreased in phase B compared with phase A. These results suggest that tDCS over the SMA during walking improves gait stability by enhancing CST excitability in the early stance phase.

A mouse model with widespread expression of the C9orf72-linked glycine-arginine dipeptide displays non-lethal ALS/FTD-like phenotypes

Translation of the hexanucleotide G4C2 expansion associated with C9orf72 amyotrophic lateral sclerosis and frontotemporal dementia (ALS/FTD) produces five different dipeptide repeat protein (DPR) species that can confer toxicity. There is yet much to learn about the contribution of a single DPR to disease pathogenesis. We show here that a short repeat length is sufficient for the DPR poly-GR to confer neurotoxicity in vitro, a phenomenon previously unobserved. This toxicity is also reported in vivo in our novel knock-in mouse model characterized by widespread central nervous system (CNS) expression of the short-length poly-GR. We observe sex-specific chronic ALS/FTD-like phenotypes in these mice, including mild motor neuron loss, but no TDP-43 mis-localization, as well as motor and cognitive impairments. We suggest that this model can serve as the foundation for phenotypic exacerbation through second-hit forms of stress.

Neurodegenerative diseases categorization by applying the automatic model selection and hyperparameter optimization method

Neurodegenerative diseases affect a large part of the population in the world and also in Mexico, deteriorating gradually the quality of patients’ life. Therefore, it is important to diagnose them with a high degree of reliability. In order to solve it, various computational methods have been applied in the analysis of biomarkers of human gait. In this study, we propose employing the automatic model selection and hyperparameter optimization method that has not been addressed before for this problem. Our results showed highly competitive percentages of correctly classified instances when discriminating binary and multiclass sets of neurodegenerative diseases: Parkinson’s disease, Huntington’s disease, and Spinocerebellar ataxias.

Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm

Sleep staging is the basis of sleep assessment and plays a crucial role in the early diagnosis and intervention of sleep disorders. Manual sleep staging by a specialist is time-consuming and is influenced by subjective factors. Moreover, some automatic sleep staging algorithms are complex and inaccurate. The paper proposes a single-channel EEG-based sleep staging method that provides reliable technical support for diagnosing sleep problems. In this study, 59 features were extracted from three aspects: time domain, frequency domain, and nonlinear indexes based on single-channel EEG data. Support vector machine, neural network, decision tree, and random forest classifier were used to classify sleep stages automatically. The results reveal that the random forest classifier has the best sleep staging performance among the four algorithms. The recognition rate of the Wake phase was the highest, at 92.13%, and that of the N1 phase was the lowest, at 73.46%, with an average accuracy of 83.61%. The embedded method was adopted for feature filtering. The results of sleep staging of the 11-dimensional features after filtering show that the random forest model achieved 83.51% staging accuracy under the condition of reduced feature dimensions, and the coincidence rate with the use of all features for sleep staging was 94.85%. Our study confirms the robustness of the random forest model in sleep staging, which also represents a high classification accuracy with appropriate classifier algorithms, even using single-channel EEG data. This study provides a new direction for the portability of clinical EEG monitoring.

Adaptive treadmill walking encourages persistent propulsion

BACKGROUND

Adaptive treadmills allow real-time changes in walking speed by responding to changes in step length, propulsion, or position on the treadmill. The stride-to-stride variability, or persistence, of stride time during overground, fixed-speed, and adaptive treadmill walking has been studied, but persistence of propulsion during adaptive treadmill walking remains unknown. Because increased propulsion is often a goal of post-stroke rehabilitation, knowledge of the stride-to-stride variability may aid rehabilitation protocol design.

RESEARCH QUESTION

How do spatiotemporal and propulsive gait variables vary from stride to stride during adaptive treadmill walking, and how do they compare to fixed-speed treadmill walking?

METHODS

Eighteen young healthy subjects walked on an instrumented split-belt treadmill in the adaptive and fixed-speed modes for 10 minutes at their comfortable speed. Kinetic data was collected from the treadmill. Detrended fluctuation analysis was applied to the time series data. Shapiro-Wilk tests assessed normality and one-way repeated measures ANOVAs compared between adaptive, fixed-speed, and randomly shuffled conditions at a Bonferroni-corrected significance level of 0.0055.

RESULTS

Stride time, stride length, step length, and braking impulse were persistent (α > 0.5) in the adaptive and fixed-speed conditions. Adaptive and fixed-speed were different from each other. Stride speed was persistent in the adaptive condition and anti-persistent (α < 0.5) in the fixed-speed condition. Peak propulsive force, peak braking force, and propulsive impulse were persistent in the adaptive condition but not the fixed-speed condition (α ≈ 0.5). Net impulse was non-persistent in the adaptive and fixed-speed conditions. All variables were non-persistent in the shuffled condition.

SIGNIFICANCE

During adaptive treadmill walking, increases in propulsive force and impulse persist for multiple strides. Persistence was stronger on the adaptive treadmill, where increased propulsion translates into increased walking speed. For post-stroke gait rehabilitation where increasing propulsion and speed are goals, the stronger persistence of adaptive treadmill walking may be beneficial.

Applications and limitations of current markerless motion capture methods for clinical gait biomechanics

BACKGROUND

Markerless motion capture has the potential to perform movement analysis with reduced data collection and processing time compared to marker-based methods. This technology is now starting to be applied for clinical and rehabilitation applications and therefore it is crucial that users of these systems understand both their potential and limitations. This literature review aims to provide a comprehensive overview of the current state of markerless motion capture for both single camera and multi-camera systems. Additionally, this review explores how practical applications of markerless technology are being used in clinical and rehabilitation settings, and examines the future challenges and directions markerless research must explore to facilitate full integration of this technology within clinical biomechanics.

METHODOLOGY

A scoping review is needed to examine this emerging broad body of literature and determine where gaps in knowledge exist, this is key to developing motion capture methods that are cost effective and practically relevant to clinicians, coaches and researchers around the world. Literature searches were performed to examine studies that report accuracy of markerless motion capture methods, explore current practical applications of markerless motion capture methods in clinical biomechanics and identify gaps in our knowledge that are relevant to future developments in this area.

RESULTS

Markerless methods increase motion capture data versatility, enabling datasets to be re-analyzed using updated pose estimation algorithms and may even provide clinicians with the capability to collect data while patients are wearing normal clothing. While markerless temporospatial measures generally appear to be equivalent to marker-based motion capture, joint center locations and joint angles are not yet sufficiently accurate for clinical applications. Pose estimation algorithms are approaching similar error rates of marker-based motion capture, however, without comparison to a gold standard, such as bi-planar videoradiography, the true accuracy of markerless systems remains unknown.

CONCLUSIONS

Current open-source pose estimation algorithms were never designed for biomechanical applications, therefore, datasets on which they have been trained are inconsistently and inaccurately labelled. Improvements to labelling of open-source training data, as well as assessment of markerless accuracy against gold standard methods will be vital next steps in the development of this technology.

A Survey of Human Gait-Based Artificial Intelligence Applications

We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.

Analysis and Detection of Erosion in Wind Turbine Blades

This paper studies erosion at the tip of wind turbine blades by considering aerodynamic analysis, modal analysis and predictive machine learning modeling. Erosion can be caused by several factors and can affect different parts of the blade, reducing its dynamic performance and useful life. The ability to detect and quantify erosion on a blade is an important predictive maintenance task for wind turbines that can have broad repercussions in terms of avoiding serious damage, improving power efficiency and reducing downtimes. This study considers both sides of the leading edge of the blade (top and bottom), evaluating the mechanical imbalance caused by the material loss that induces variations of the power coefficient resulting in a loss in efficiency. The QBlade software is used in our analysis and load calculations are preformed by using blade element momentum theory. Numerical results show the performance of a blade based on the relationship between mechanical damage and aerodynamic behavior, which are then validated on a physical model. Moreover, two machine learning (ML) problems are posed to automatically detect the location of erosion (top of the edge, bottom or both) and to determine erosion levels (from 8% to 18%) present in the blade. The first problem is solved using classification models, while the second is solved using ML regression, achieving accurate results. ML pipelines are automatically designed by using an AutoML system with little human intervention, achieving highly accurate results. This work makes several contributions by developing ML models to both detect the presence and location of erosion on a blade, estimating its level and applying AutoML for the first time in this domain.

Pharyngeal Pressure Variability During Volitional Swallowing Maneuvers

PURPOSE

Within-individual pharyngeal swallowing pressure variability differs among pharyngeal regions in healthy individuals and increases with age. It remains unknown if pharyngeal pressure variability is impacted by volitional swallowing tasks. We hypothesized that pressure variability would increase during volitional swallowing maneuvers and differ among pharyngeal regions depending on the type of swallowing task being performed.

METHOD

Pharyngeal high-resolution manometry was used to record swallowing pressure data from 156 healthy participants during liquid (5 cc) or saliva swallows, and during volitional swallowing tasks including effortful swallow, Mendelsohn maneuver, Masako maneuver, or during postural adjustments. The coefficient of variation was used to determine pressure variability of velopharynx, tongue base, hypopharynx, and upper esophageal sphincter regions. Repeated-measures analysis of variance was used on log-transformed data to examine effects of pharyngeal region and swallowing tasks on swallow-to-swallow variability.

RESULTS

There was a significant main effect of task with greater pressure variability for the effortful swallow (p = .002), Mendelsohn maneuver (p < .001), Masako maneuver (p = .002), and the head turn (p = .006) compared with normal effort swallowing. There was also a significant main effect of region (p < .01). In general, swallowing pressure variability was lower for the tongue base and upper esophageal sphincter regions than the hypopharynx. There was no significant interaction of task and region (effortful, p = .182; Mendelsohn, p = .365; Masako, p = .885; chin tuck, p = .840; head turn, p = .059; and inverted, p = .773).

CONCLUSIONS

Pharyngeal swallowing pressure variability increases in healthy individuals during volitional swallowing tasks. Less stable swallow patterns may result when tasks are less automatic and greater in complexity. These findings may have relevance to swallowing motor control integrity in healthy aging and individuals with neurogenic dysphagia.

Comparing the applicability of temporal gait symmetry, variability and laterality in bilateral gait conditions: A feasibility study of healthy individuals and people with diabetic neuropathy

BACKGROUND

Gait symmetry is used to measure pathological gait but is usually applied to unilateral pathology. This study aims to investigate bilateral impairment using existing and new gait symmetry methods.

METHODS

15 healthy volunteers and 14 people with diabetes and distal symmetrical polyneuropathy participated in this study. Three temporal parameters (combined step, stance phase and double stance), expressed as a percentage, were extracted for comparing gait symmetry between healthy volunteers and patients using in-shoe measurements (Pedar-X). Three indices were calculated, including the widely used Symmetry Index; the well-established Variability Index; and the newly developed Laterality Index, that calculates how well distributed a condition is across both legs.

FINDINGS

In all three parameters, Symmetry and Variability Index proved to be significantly greater in the diabetic cohort (p-values range < 0.001-0.0226). The Laterality Index was significantly greater in the diabetic cohort for the stance and double stance phases (p-values 0.03 and < 0.001), but not for the combined step (p-value 0.3953). In both cohorts, Laterality Index <1 (fractional laterality) was associated with small Symmetry Index data, whereas in large Symmetry Index data, the Laterality Index was 1 (unilateral condition).

INTERPRETATIONS

Gait symmetry and variability are useful tools for quantifying locomotion and the effects of aging and diseases. We have shown the ability of these two indices in differentiating two extreme groups of individuals. For cases with small Symmetry Index, the current method of using an arbitrary value is not ideal. The newly developed Laterality Index can be used to decide on the cut-off between symmetrical and asymmetrical gait.

Gait analysis under the lens of statistical physics

Human gait is a fundamental activity, essential for the survival of the individual, and an emergent property of the interactions between complex physical and cognitive processes. Gait is altered in many situations, due both to external constraints, as e.g. paced walk, and to physical and neurological pathologies. Its study is therefore important as a way of improving the quality of life of patients, but also as a door to understanding the inner working of the human nervous system. In this review we explore how four statistical physics concepts have been used to characterise normal and pathological gait: entropy, maximum Lyapunov exponent, multi-fractal analysis and irreversibility. Beyond some basic definitions, we present the main results that have been obtained in this field, as well as a discussion of the main limitations researchers have dealt and will have to deal with. We finally conclude with some biomedical considerations and avenues for further development.

Functional ambulation decline and factors associated in amyotrophic lateral sclerosis

Abstract Introduction: Amyotrophic lateral sclerosis (ALS) is a disabling neurodegenerative disease, which compromises locomotion and functional independence. As the goal of physical therapy is to maintain the individual's locomotion capacity and independence as long as possible, it is necessary to gain a better understanding of the possible factors associated with the loss of this capacity. Objective: To evaluate functional ambulation in patients with ALS and possible factors associated with its decline. Methods: A cross-sectional study was conducted with sporadic ALS patients. Demographic and clinical/functional aspects were evaluated. ALS Functional Rating Scale-Revised (ALSFRS-R), Functional Ambulation Category, Medical Research Council scale and Fatigue Severity Scale were used. Descriptive and comparative analyses were conducted of the groups capable and incapable of functional ambulation. Binary logistic regression (stepwise forward method) was performed to determine potential factors associated with the loss of functional ambulation. Results: Among the 55 patients (mean age: 56.9 ± 11.2 years), 74.5% were able to walk functionally. Differences were found between groups regarding time of diagnosis, number of falls, pain, use of noninvasive ventilation, gastrostomy, ability to turn in bed, mobility aids, home adaptations, functional performance, muscle strength and fatigue. The possible predictors of walking disability were overall muscle strength (OR = 0.837; p = 0.003) and fatigue (OR =1.653; p = 0.034). Conclusion: Muscle strength and fatigue are associated with the decline in ambulation capacity in patients with ALS. In view of the complexity of elements involved in walking, further studies are needed to investigate the influence of these aspects in this population.

CNN-based severity prediction of neurodegenerative diseases using gait data

Neurodegenerative diseases occur because of degeneration in brain cells but can manifest as impairment of motor functions. One of the side effects of this impairment is an abnormality in walking. With the development of sensor technologies and artificial intelligence applications in recent years, the disease severity of patients can be estimated using their gait data. In this way, decision support applications for grading the severity of the disease that the patient suffers in the clinic can be developed. Thus, patients can have treatment methods more suitable for the severity of the disease. The presented research proposes a deep learning-based approach using gait data represented by a Quick Response code to develop an effective and reliable disease severity grading system for neurodegenerative diseases such as amyotrophic lateral sclerosis, Huntington's disease, and Parkinson's disease. The two-dimensional Quick Response data set was created by converting each one-dimensional gait data of the subjects with a novel representation approach to a Quick Response code. This data set was regressed with the convolutional neural network deep learning method, and a solution was sought for the problem of grading disease severity. Further, to demonstrate the success of the results obtained with the novel approach, native machine learning approaches such as Multilayer Perceptron, Random Forest, Extremely Randomized Trees, and K-Nearest Neighbours, and ensemble machine learning methods, such as voting and stacking, were applied on one-dimensional data. Finally, the results obtained on the prediction of disease severity by testing one-dimensional gait data with a convolutional neural network architecture that operates on one-dimensional data were included. The results showed that, in most cases, the two-dimensional convolutional neural network approach performed the best among all methods.

Neural Variability in the Prefrontal Cortex as a Reflection of Neural Flexibility and Stability in Patients With Parkinson Disease

BACKGROUND AND OBJECTIVES

Functional Near-Infrared Spectroscopy (fNIRS) studies provide direct evidence to the important role of the prefrontal cortex (PFC) during walking in aging and Parkinson's disease (PD). Most studies mainly explored mean HbO2 levels, while moment-to-moment variability measures have been rarely investigated. Variability measures can inform on flexibility that is imperative for adaptive function. We hypothesized that patients with PD will show less variability in HbO2 signals during walking compared to healthy controls.

METHODS

206 participants, 57 healthy controls (age: 68.9±1.0 years; 27 women) and 149 idiopathic PD patients (age: 69.8±0.6 years, 50 women, disease duration: 8.27±5.51 years) performed usual walking and dual-task walking (serial 3 subtractions) with an fNIRS system placed on the forehead. HbO2 variability was calculated using the standard deviation (SD), range, and mean detrended time series of fNIRS-derived HbO2 signal evaluated during each walking task. HbO2 variability was compared between groups and between walking tasks using mixed model analyses.

RESULTS

Higher variability (SD, range, mean detrended time series) was observed during dual-task walking, compared to usual walking (p<0.025), but this was derived from the differences within the healthy control group (group X task interaction: p<0.007). On the other hand, task repetition demonstrated reduced variability in healthy controls but increased variability in patients with PD (interaction group*walk-repetition: p<0.048). The MDS-UPDRS motor score correlated with HbO2 range (r=0.142, p=0.050) and HbO2 SD (r=0.173, p=0.018) during usual walking in all participants.

DISCUSSION

In this study, we suggest a new way to interpret changes in HbO2 variability. We relate increased HbO2 variability to flexible adaptation to environmental challenges and decreased HbO2 variability to the stability of performance. Our results show that both are limited in PD however, further investigation of these concepts is required. Moreover, HbO2 variability measures are an important aspect of brain function that adds new insights into the role of PFC during walking with aging and PD.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that patients with PD have more variability within Hb02 signals during usual-walking, compared to healthy controls, but not during dual-task walking.

Recurrence eigenvalues of movements from brain signals

The ability to characterize muscle activities or skilled movements controlled by signals from neurons in the motor cortex of the brain has many useful implications, ranging from biomedical perspectives to brain–computer interfaces. This paper presents the method of recurrence eigenvalues for differentiating moving patterns in non-mammalian and human models. The non-mammalian models of Caenorhabditis elegans have been studied for gaining insights into behavioral genetics and discovery of human disease genes. Systematic probing of the movement of these worms is known to be useful for these purposes. Study of dynamics of normal and mutant worms is important in behavioral genetic and neuroscience. However, methods for quantifying complexity of worm movement using time series are still not well explored. Neurodegenerative diseases adversely affect gait and mobility. There is a need to accurately quantify gait dynamics of these diseases and differentiate them from the healthy control to better understand their pathophysiology that may lead to more effective therapeutic interventions. This paper attempts to explore the potential application of the method for determining the largest eigenvalues of convolutional fuzzy recurrence plots of time series for measuring the complexity of moving patterns of Caenorhabditis elegans and neurodegenerative disease subjects. Results obtained from analyses demonstrate that the largest recurrence eigenvalues can differentiate phenotypes of behavioral dynamics between wild type and mutant strains of Caenorhabditis elegans; and walking patterns among healthy control subjects and patients with Parkinson’s disease, Huntington’s disease, or amyotrophic lateral sclerosis.

A novel method based on matching pursuit decomposition of gait signals for Parkinson's disease, Amyotrophic lateral sclerosis and Huntington's disease detection

BACKGROUND AND OBJECTIVE

An accurate detection of neurodegenerative diseases (NDDs) definitely improves the life of patients and has attracted growing attention.

METHODS

In this paper, a general automatic method for detection of Parkinson's disease (PD), Amyotrophic lateral sclerosis (ALS) and Huntington's disease (HD) has been proposed based on the localized time-frequency information of gait signals. The new main part of the detection method is to obtain a small set of sparse coefficients for the local representation of gait signals with appropriate time and frequency resolution. For this purpose, a hybrid feature set based on sparse matching pursuit decomposition and two sets of nonlinear and linear features has been developed. Then, principal components of the proposed feature have been analyzed using a sparse coding classifier. Results The proposed approach has achieved high average accuracy rates of 93%, 94%, and 97% for PD, ALS, and HD detection, respectively.

CONCLUSIONS

The obtained results have indicated that combination of time and frequency information of the gait signals through adaptive localized window length in MP makes it more efficient in comparison with the existing time, frequency or other time-frequency gait parameters. The great potential of nonlinear sparse features for PD and HD detection and linear ones for ALS detection has also been shown.

Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time-Frequency Spectrogram and Deep Learning Neural Network Features

A novel identification algorithm using a deep learning approach was developed in this study to classify neurodegenerative diseases (NDDs) based on the vertical ground reaction force (vGRF) signal. The irregularity of NDD vGRF signals caused by gait abnormalities can indicate different force pattern variations compared to a healthy control (HC). The main purpose of this research is to help physicians in the early detection of NDDs, efficient treatment planning, and monitoring of disease progression. The detection algorithm comprises a preprocessing process, a feature transformation process, and a classification process. In the preprocessing process, the five-minute vertical ground reaction force signal was divided into 10, 30, and 60 s successive time windows. In the feature transformation process, the time-domain vGRF signal was modified into a time-frequency spectrogram using a continuous wavelet transform (CWT). Then, feature enhancement with principal component analysis (PCA) was utilized. Finally, a convolutional neural network, as a deep learning classifier, was employed in the classification process of the proposed detection algorithm and evaluated using leave-one-out cross-validation (LOOCV) and k-fold cross-validation (k-fold CV, k = 5). The proposed detection algorithm can effectively differentiate gait patterns based on a time-frequency spectrogram of a vGRF signal between HC subjects and patients with neurodegenerative diseases.

Human Gait Analysis in Neurodegenerative Diseases: a Review

This paper reviews the recent literature on technologies and methodologies for quantitative human gait analysis in the context of neurodegnerative diseases. The use of technological instruments can be of great support in both clinical diagnosis and severity assessment of these pathologies. In this paper, sensors, features and processing methodologies have been reviewed in order to provide a highly consistent work that explores the issues related to gait analysis. First, the phases of the human gait cycle are briefly explained, along with some non-normal gait patterns (gait abnormalities) typical of some neurodegenerative diseases. The work continues with a survey on the publicly available datasets principally used for comparing results. Then the paper reports the most common processing techniques for both feature selection and extraction and for classification and clustering. Finally, a conclusive discussion on current open problems and future directions is outlined.

Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers

This study proposes a reliable computer-aided framework to identify gait fluctuations associated with a wide range of degenerative neuromuscular disease (DNDs) and health conditions. Investigated DNDs included amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and Huntington's disease (HD). We further performed a statistical and classification comparison elucidating the discriminative capability of different gait signals, including vertical ground reaction force (VGRF), stride duration, stance duration, and swing duration. Feature representation of these gait signals was based on statistical amplitude quantification using the root mean square (RMS), variance, kurtosis, and skewness metrics. We investigated various decision tree (DT) based ensemble methods such as bagging, adaptive boosting (AdaBoost), random under-sampling boosting (RUSBoost), and random subspace to tackle the challenge of multi-class classification. Experimental results showed that AdaBoost ensembling provided a 6.49%, 0.78%, 2.31%, and 2.72% prediction rate improvement for the VGRF, stride, stance, and swing signals, respectively. The proposed approach achieved the highest classification accuracy of 99.17%, sensitivity of 98.23%, and specificity of 99.43%, using the VGRF-based features and the adaptive boosting classification model. This work demonstrates the effective capability of using simple gait fluctuation analysis and machine learning approaches to detect DNDs. Computer-aided analysis of gait fluctuations provides a promising advent to enhance clinical diagnosis of DNDs.

Inertial sensing of step kinematics in ambulatory patients with ALS and related motor neuron diseases

Amyotrophic lateral sclerosis (ALS) is a progressive disorder which impairs gait and elevates the risk for falls. Current methods of assessing gait in these patients are infrequent and subjective. The goal of this study was to evaluate wearable-based methods for assessing gait to facilitating better monitoring of ambulatory health and ultimately lessen fall risk. Thirty ambulatory patients seen in ALS clinic were guided by a physical therapist on a short walk, during which inertial sensors recorded their movement. Two methods, utilising sensors at the waist or foot, were used independently to estimate gait parameters. Decreased stride length, increased stride duration and decreased walking speed were associated with lower functional walking scores, and the presence of a cane or walker. Overall, there was no group-wide mean walking speed differences between methods, though the waist method overestimated stride length and walking speed in those with more significant gait dysfunction compared to the foot method. Reconstruction of movement using the foot-based sensor resulted in route segments that were 94 ± 1% standard error of the mean (SEM) the length of a centre-to-centre hallway reference vector, with an angular error of 0.66 ± 0.28° SEM.

Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques

Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.

Gait Stability Measurement by Using Average Entropy

Gait stability has been measured by using many entropy-based methods. However, the relation between the entropy values and gait stability is worth further investigation. A research reported that average entropy (AE), a measure of disorder, could measure the static standing postural stability better than multiscale entropy and entropy of entropy (EoE), two measures of complexity. This study tested the validity of AE in gait stability measurement from the viewpoint of the disorder. For comparison, another five disorders, the EoE, and two traditional metrics methods were, respectively, used to measure the degrees of disorder and complexity of 10 step interval (SPI) and 79 stride interval (SI) time series, individually. As a result, every one of the 10 participants exhibited a relatively high AE value of the SPI when walking with eyes closed and a relatively low AE value when walking with eyes open. Most of the AE values of the SI of the 53 diseased subjects were greater than those of the 26 healthy subjects. A maximal overall accuracy of AE in differentiating the healthy from the diseased was 91.1%. Similar features also exists on those 5 disorder measurements but do not exist on the EoE values. Nevertheless, the EoE versus AE plot of the SI also exhibits an inverted U relation, consistent with the hypothesis for physiologic signals.

Video‐triggered EEG‐emotion public databases and current methods: A survey

Emotions, formed in the process of perceiving external environment, directly affect human daily life, such as social interaction, work efficiency, physical wellness, and mental health. In recent decades, emotion recognition has become a promising research direction with significant application values. Taking the advantages of electroencephalogram (EEG) signals (i.e., high time resolution) and video‐based external emotion evoking (i.e., rich media information), video‐triggered emotion recognition with EEG signals has been proven as a useful tool to conduct emotion‐related studies in a laboratory environment, which provides constructive technical supports for establishing real‐time emotion interaction systems. In this paper, we will focus on video‐triggered EEG‐based emotion recognition and present a systematical introduction of the current available video‐triggered EEG‐based emotion databases with the corresponding analysis methods. First, current video‐triggered EEG databases for emotion recognition (e.g., DEAP, MAHNOB‐HCI, SEED series databases) will be presented with full details. Then, the commonly used EEG feature extraction, feature selection, and modeling methods in video‐triggered EEG‐based emotion recognition will be systematically summarized and a brief review of current situation about video‐triggered EEG‐based emotion studies will be provided. Finally, the limitations and possible prospects of the existing video‐triggered EEG‐emotion databases will be fully discussed.