Prevalence and burden of gait disorders in elderly men and women aged 60-97 years: a population-based study

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.

Pedestrian behavior among Chinese seniors: Insights from factor analysis and structural equation modeling

Pedestrians contribute significantly to the total number of road fatalities and injuries, with their behavior playing a pivotal role in traffic mishaps. Despite this, a limited body of research has delved into the walking behaviors of Chinese seniors. Given this gap, our study rigorously examines the patterns of seniors' walking behaviors and their influencing factors. We employed exploratory factor analysis to decipher the intrinsic component structures of seniors' walking patterns in China. Subsequently, structural equation modeling was utilized to analyze the impact of demographic attributes and personality characteristics on these behaviors. The findings revealed a four-dimensional structure for senior walking behaviors: transgression, inattention, aggression, and positive behaviors. Introducing personality traits as variables notably enhanced the explanatory power of our model. Specifically, anger, altruism, and normlessness significantly influenced certain dimensions of walking behaviors, while sensation-seeking did not exhibit any notable effect. This study not only highlights the complexity and diversity of elderly walking behaviors but also underscores the importance of tailored interventions to improve walking safety and quality of life for seniors.

Immediate changes in stroke patients' gait following the application of lower extremity elastic strap binding technique

Objective

To ascertain the immediate changes in stroke patients' temporal and spatial parameters of gait and the joint angles of stroke patients throughout the entire gait cycle following the application of lower extremity elastic strap binding technique.

Methods

Twenty-nine stroke patients were invited as the study participants. The patient seated, flexed the hip and knee, utilized a 5 cm-wide elastic strap, positioning its midpoint beneath the affected foot and crossing it anterior to the ankle joint. Upon standing, the strap encircled the posterior aspect of the lower leg, proceeded around the back of the knee, and ascended the thigh on the affected side. Crossing anteriorly over the thigh, it then encircled the back of the waist before being secured in place. Using Qualisys motion capture system to collect kinematic data of the lower extremities during walking while wearing shoes only or strapping. A paired sample t-test was used to analyze the effects of the technique on gait spatiotemporal parameters and joint angles in stroke patients.

Results

The patients' step length decreased (P = 0.024), and step width increased (P = 0.008) during the gait cycle after the strapping. In the gait cycle between 0% and 2%, 7%-77%, and 95%-100%, the hip flexion angle on the affected side was significantly larger after the strapping (P < 0.05). In the gait cycle between 0% to 69% and 94%-100%, the knee flexion angle on the affected side was significantly larger after the strapping (P < 0.05). In the gait cycle between 0% to 57% and 67%-100%, the ankle dorsiflexion angle on the affected side was significantly smaller after the strapping (P < 0.05), and in the gait cycle between 0% to 35% and 68%-100%, the ankle inversion angle on the affected side was significantly smaller after the strapping (P < 0.05).

Conclusion

The lower extremity elastic strap binding technique can decrease the hip flexion and knee flexion limitations in stroke patients during walking, and reduce the ankle plantar flexion and ankle inversion angle of stroke patients. The lower extremity elastic strap binding technique enabled stroke patients to adopt a more stable gait pattern.

Sensor-Based Gait and Balance Assessment in Healthy Adults: Analysis of Short-Term Training and Sensor Placement Effects

While the analysis of gait and balance can be an important indicator of age- or disease-related changes, it remains unclear if repeated performance of gait and balance tests in healthy adults leads to habituation effects, if short-term gait and balance training can improve gait and balance performance, and whether the placement of wearable sensors influences the measurement accuracy. Healthy adults were assessed before and after performing weekly gait and balance tests over three weeks by using a force plate, motion capturing system and smartphone. The intervention group (n = 25) additionally received a home-based gait and balance training plan. Another sample of healthy adults (n = 32) was assessed once to analyze the impact of sensor placement (lower back vs. lower abdomen) on gait and balance analysis. Both the control and intervention group exhibited improvements in gait/stance. However, the trends over time were similar for both groups, suggesting that targeted training and repeated task performance equally contributed to the improvement of the measured variables. Since no significant differences were found in sensor placement, we suggest that a smartphone used as a wearable sensor could be worn both on the lower abdomen and the lower back in gait and balance analyses.

Frontal cortex neurometabolites and mobility in older adults: a preliminary study

BACKGROUND

The frontal cortex, relevant to global cognition and motor function, is recruited to compensate for mobility dysfunction in older adults. However, the in vivo neurophysiological (e.g., neurometabolites) underpinnings of the frontal cortex compensation for mobility dysfunction remain poorly understood. The purpose of this study was to investigate the relationships among frontal cortex neurophysiology, mobility, and cognition in healthy older adults.

METHODS

Magnetic Resonance Spectroscopy (MRS) quantified N-acetylasparate (tNAA) and total choline (tCho) concentrations and ratios in the frontal cortex in 21 older adults. Four inertial sensors recorded the Timed Up & Go (TUG) test. Cognition was assessed using the Flanker Inhibitory Control and Attention Test which requires conflict resolution because of response interference from flanking distractors during incongruent trials. Congruent trials require no conflict resolution.

RESULTS

tNAA concentration significantly related to the standing (p = 0.04) and sitting (p = 0.03) lean angles. tCho concentration (p = 0.04) and tCho ratio (p = 0.02) significantly related to TUG duration. tCho concentration significantly related to incongruent response time (p = 0.01). tCho ratio significantly related to both congruent (p = 0.009) and incongruent (p < 0.001) response times. Congruent (p = 0.02) and incongruent (p = 0.02) Flanker response times significantly related to TUG duration.

CONCLUSIONS

Altered levels of frontal cortex neurometabolites are associated with both mobility and cognitive abilities in healthy older adults. Identifying neurometabolites associated with frontal cortex compensation of mobility dysfunction could improve targeted therapies aimed at improving mobility in older adults.

[Influence of neurological diseases on mobility and ability to drive]

Neurology deals with organic diseases of the muscles, the peripheral nerves of the trunk and extremities, and the central nervous system (spinal cord, brain, stem, cerebellum, and cerebrum). Diseases that lead to dysfunction of these structures can cause both physical and cognitive problems. Therefore, neurological diseases can particularly impair personal mobility through both physical limitations and cognitive deficits. Many of the diseases show a significant increase in frequency with age.Physical impairments in mobility primarily manifest as gait disorders. These are found to a relevant extent in two-thirds of people older than 80 years of age and are a common cause of falls, often with considerable sequelae. Driving a car can have negative effects, for example, on reaction speed, braking power, and looking over the shoulder. Parkinson's disease as well as paralysis and sensory disorders in the context of polyneuropathies can be responsible for this.Driving a car is an obvious compensatory mechanism with respect to impaired walking ability. However, the cause of many diseases that affect the fitness to walk lies in the central nervous system, often in the area of the cerebrum. Consequently, cognitive deficits manifest themselves in addition to physical ones, which further restrict mobility through the loss of the fitness to drive. Neurological diseases typical of old age that limit mobility in this way include Parkinson's disease and circulatory disorders of the brain. In addition, epileptic seizures occur more frequently in old age as a symptom of other diseases.

The mechanism of static postural control in the impact of lower limb muscle strength asymmetry on gait performance in the elderly

Background

Abnormal gait is prevalent among the elderly population, leading to reduced physical activity, increased risk of falls, and the potential development of dementia and disabilities, thus degrading the quality of life in later years. Numerous studies have highlighted the crucial roles of lower limb muscle strength asymmetry and static postural control in gait, and the reciprocal influence of lower limb muscle strength asymmetry on static postural control. However, research exploring the interrelationship between lower limb muscle strength asymmetry, static postural control, and gait performance has been limited.

Methods

A total of 55 elderly participants aged 60 to 75 years were recruited. Isokinetic muscle strength testing was used to assess bilateral knee extension strength, and asymmetry values were calculated. Participants with asymmetry greater than 15% were categorized as the Asymmetry Group (AG), while those with asymmetry less than 15% were classified in the Symmetry Group (SG). Gait parameters were measured using a plantar pressure gait analysis system to evaluate gait performance, and static postural control was assessed through comfortable and narrow stance tests.

Results

First, participants in the AG demonstrated inferior gait performance, characterized by slower gait speed, longer stance time and percentage of stance time in gait, and smaller swing time and percentage of swing time in gait. Spatial-temporal gait parameters of the weaker limb tended to be abnormal. Second, static postural control indices were higher in AG compared to SG in all aspects except for the area of ellipse during the comfortable stance with eyes open test. Third, abnormal gait parameters were associated with static postural control.

Conclusion

Firstly, elderly individuals with lower limb muscle strength asymmetry are prone to abnormal gait, with the weaker limb exhibiting poorer gait performance. Secondly, lower limb muscle strength asymmetry contributes to diminished static postural control in the elderly. Thirdly, the mechanism underlying abnormal gait in the elderly due to lower limb muscle strength asymmetry may be linked to a decline in static postural control.

Gait analysis and geriatric syndromes: An association among elderly patients attending a teaching hospital of Delhi

Background

Gait abnormalities are prevalent, affecting a substantial portion of the elderly population, and leading to mobility limitations, reduced quality of life, falls, hospitalizations, and premature death.

Objectives

The study aims to assess gait patterns among individuals aged 75 years and above attending the geriatric OPD of a tertiary care hospital in New Delhi and evaluate their association with various geriatric syndromes.

Materials and Methods

This cross-sectional study, conducted at a tertiary care hospital in Delhi, from May 2019 to November 2021, involved 100 participants aged 75 and above. It encompassed a thorough assessment protocol covering demographics, health history, clinical and functional evaluations, depression, cognition, balance, frailty, urinary incontinence, polypharmacy, nutrition, comorbidities, and gait analysis.

Results

In this study of elderly individuals, the mean age was 78.56 years, and the mean BMI was 23.11. The participants had an average of 1.74 comorbidities, with hypertension being the most prevalent (62%), followed by diabetes (25%), chronic obstructive airway disease (COAD) (11%), and coronary artery disease (15%). Geriatric assessments revealed varying proportions of frailty (34%), polypharmacy (40%), and urinary incontinence (9%). The mean scores for activities of daily living, instrumental activities of daily living, nutritional status, cognitive function, Timed Up and Go Test, and depression scale were also reported. Various gait parameters demonstrated significant correlations with these geriatric factors, including frailty, comorbidities, BMI, and mobility scores.

Conclusion

The study identified significant associations between gait patterns and various geriatric syndromes, emphasizing the importance of gait analysis in assessing the health and mobility of elderly individuals.

Overestimation of Balance Ability Among Older Adults at Risk for Falls

Objective: This study examined alignment of subjective balance confidence with Stopping Elderly Accidents, Deaths and Injuries (STEADI) fall risk. Methods: Cross-sectional analysis of 155 community-dwelling adults (60 + y/o) from 2016 to 2018 who completed a STEADI fall assessment. Descriptive statistics, Chi-Square analysis, and biserial point correlations were applied. Results: Adults who overestimate balance confidence, 55.6% (n = 50) reported a fall in the past year, 62.2% (n = 56) were worried about falling, 48.9% (n = 44) felt unsteady when standing/walking, and 70.0% (n = 63) had a score of ≥4 on the Stay Independent Questionnaire (SIQ). Physical performance for these adults were mean TUG score 10.9s (SD = 3.4), mean 30 second chair stands 10.8 (SD = 3.5), and mean 4-stage balance score 3.1 (SD = .76). Discussion: Older adults are more likely to overestimate their subjective balance confidence. Individuals are equally likely to have reported a fall in the past year if they were "at fall risk," regardless of their subjective balance confidence.

Bidirectional mediation of bone mineral density and brain atrophy on their associations with gait variability

This mediation analysis aimed to investigate the associations among areal bone mineral density, mobility-related brain atrophy, and specific gait patterns. A total of 595 participants from the Taizhou Imaging Study, who underwent both gait and bone mineral density measurements, were included in this cross-sectional analysis. We used a wearable gait tracking device to collect quantitative gait parameters and then summarized them into independent gait domains with factor analysis. Bone mineral density was measured in the lumbar spine, femoral neck, and total hip using dual-energy X-ray absorptiometry. Magnetic resonance images were obtained on a 3.0-Tesla scanner, and the volumes of brain regions related to mobility were computed using FreeSurfer. Lower bone mineral density was found to be associated with higher gait variability, especially at the site of the lumbar spine (β = 0.174, FDR = 0.001). Besides, higher gait variability was correlated with mobility-related brain atrophy, like the primary motor cortex (β = 0.147, FDR = 0.006), sensorimotor cortex (β = 0.153, FDR = 0.006), and entorhinal cortex (β = 0.106, FDR = 0.043). Bidirectional mediation analysis revealed that regional brain atrophy contributed to higher gait variability through the low lumbar spine bone mineral density (for the primary motor cortex, P = 0.018; for the sensorimotor cortex, P = 0.010) and the low lumbar spine bone mineral density contributed to higher gait variability through the primary motor and sensorimotor cortices (P = 0.026 and 0.010, respectively).

SensAA-Design and Verification of a Cloud-Based Wearable Biomechanical Data Acquisition System

Exoskeletons designed to assist patients with activities of daily living are becoming increasingly popular, but still are subject to research. In order to gather requirements for the design of such systems, long-term gait observation of the patients over the course of multiple days in an environment of daily living are required. In this paper a wearable all-in-one data acquisition system for collecting and storing biomechanical data in everyday life is proposed. The system is designed to be cost efficient and easy to use, using off-the-shelf components and a cloud server system for centralized data storage. The measurement accuracy of the system was verified, by measuring the angle of the human knee joint at walking speeds between 3 and 12 km/h in reference to an optical motion analysis system. The acquired data were uploaded to a cloud database via a smartphone application. Verification results showed that the proposed toolchain works as desired. The system reached an RMSE from 2.9° to 8°, which is below that of most comparable systems. The system provides a powerful, scalable platform for collecting and processing biomechanical data, which can help to automize the generation of an extensive database for human kinematics.

Mobility-related brain regions linking carotid intima-media thickness to specific gait performances in old age

BACKGROUND

Gait disturbance is common in older adults with vascular diseases. However, how carotid atherosclerosis affects gait remains poorly understood. The objectives were to investigate the associations between carotid intima-media thickness and specific gait performances and explore the potential role of brain structure in mediating these associations.

METHODS

A cross-sectional analysis of data from the Taizhou Imaging Study was conducted, including 707 individuals who underwent both gait and carotid ultrasound examinations. Gait assessments include the Timed-Up-and-Go test, the Tinetti test, and quantitative gait assessment using a wearable device. Quantitative parameters were summarized into independent gait domains with factor analysis. Magnetic resonance images were obtained on a 3.0-Tesla scanner, and the volumes of fifteen brain regions related to motor function (primary motor, sensorimotor), visuospatial attention (inferior posterior parietal lobules, superior posterior parietal lobules), executive control function (dorsolateral prefrontal cortex, anterior cingulate), memory (hippocampus, entorhinal cortex), motor imagery (precuneus, parahippocampus, posterior cingulated cortex), and balance (basal ganglia: pallidum, putamen, caudate, thalamus) were computed using FreeSurfer and the Desikan-Killiany atlas. Mediation analysis was conducted with carotid intima-media thickness as the predictor and mobility-related brain regions as mediators.

RESULTS

Carotid intima-media thickness was found to be associated with the Timed-Up-and-Go performance (β = 0.129, p = 0.010) as well as gait performances related to pace (β=-0.213, p < 0.001) and symmetry (β = 0.096, p = 0.045). Besides, gait performances were correlated with mobility-related brain regions responsible for motor, visuospatial attention, executive control, memory, and balance (all FDR < 0.05). Notably, significant regions differed depending on the gait outcomes measured. The primary motor (41.9%), sensorimotor (29.3%), visuospatial attention (inferior posterior parietal lobules, superior posterior parietal lobules) (13.8%), entorhinal cortex (36.4%), and motor imagery (precuneus, parahippocampus, posterior cingulated cortex) (27.3%) mediated the association between increased carotid intima-media thickness and poorer Timed-Up-and-Go performance. For the pace domain, the primary motor (37.5%), sensorimotor (25.8%), visuospatial attention (12.3%), entorhinal cortex (20.7%), motor imagery (24.9%), and balance (basal ganglia: pallidum, putamen, caudate, thalamus) (11.6%) acted as mediators.

CONCLUSIONS

Carotid intima-media thickness is associated with gait performances, and mobility-related brain volume mediates these associations. Moreover, the distribution of brain regions regulating mobility varies in the different gait domains. Our study adds value in exploring the underlying mechanisms of gait disturbance in the aging population.

A Movement Classification of Polymyalgia Rheumatica Patients Using Myoelectric Sensors

Gait disorder is common among people with neurological disease and musculoskeletal disorders. The detection of gait disorders plays an integral role in designing appropriate rehabilitation protocols. This study presents a clinical gait analysis of patients with polymyalgia rheumatica to determine impaired gait patterns using machine learning models. A clinical gait assessment was conducted at KATH hospital between August and September 2022, and the 25 recruited participants comprised 18 patients and 7 control subjects. The demographics of the participants follow: age 56 years ± 7, height 175 cm ± 8, and weight 82 kg ± 10. Electromyography data were collected from four strained hip muscles of patients, which were the rectus femoris, vastus lateralis, biceps femoris, and semitendinosus. Four classification models were used-namely, support vector machine (SVM), rotation forest (RF), k-nearest neighbors (KNN), and decision tree (DT)-to distinguish the gait patterns for the two groups. SVM recorded the highest accuracy of 85% among the classifiers, while KNN had 75%, RF had 80%, and DT had the lowest accuracy of 70%. Furthermore, the SVM classifier had the highest sensitivity of 92%, while RF had 86%, DT had 90%, and KNN had the lowest sensitivity of 84%. The classifiers achieved significant results in discriminating between the impaired gait pattern of patients with polymyalgia rheumatica and control subjects. This information could be useful for clinicians designing therapeutic exercises and may be used for developing a decision support system for diagnostic purposes.

Powered single hip joint exoskeletons for gait rehabilitation: a systematic review and Meta-analysis

BACKGROUND

Gait disorders and as a consequence, robotic rehabilitation techniques are becoming increasingly prevalent as the population ages. In the area of rehabilitation robotics, using lightweight single hip joint exoskeletons are of significance. Considering no prior systematic review article on clinical outcomes, we aim to systematically review powered hip exoskeletons in terms of gait parameters and metabolic expenditure effects.

METHODS

Three databases of PubMed, Scopus, and Web of science were searched for clinical articles comparing outcomes of gait rehabilitation using hip motorized exoskeleton with conventional methods, on patients with gait disorder or healthy individuals. Of total number of 37 reviewed articles, 14 trials were quantitatively analyzed. Analyses performed in terms of gait spatiotemporal parameters like speed (self-speed and maximum speed), step length, stride length, cadence, and oxygen consumption.

RESULTS

Improved clinical outcomes of gait spatiotemporal parameters with hip joint exoskeletons are what our review's findings show. In terms of gait values, meta-analysis indicates that rehabilitation with single hip joint exoskeleton enhanced parameters of maximum speed by 0.13 m/s (0.10-0.17) and step length by 0.06 m (0.05-0.07). For the remaining investigated gait parameters, no statistically significant difference was observed. Regarding metabolic parameters, oxygen consumption was lower in individuals treated with hip exoskeleton (- 1.23 ml/min/kg; range - 2.13 to - 0.32).

CONCLUSION

Although the analysis demonstrated improvement with just specific gait measures utilizing powered hip exoskeletons, the lack of improvement in all parameters is likely caused by the high patient condition heterogeneity among the evaluated articles. We also noted in patients who rehabilitated with the hip exoskeleton, the oxygen cost was lower. More randomized controlled trials are needed to verify both the short- and long-term clinical outcomes, including patient-reported measures.

LEVEL OF EVIDENCE

Level I (systematic review and meta-analysis).

Validation of a modified visual analogue scale to measure user-perceived comfort of a lower-limb exoskeleton

User perceived exoskeleton comfort is likely important for device acceptance, but there is currently no validated instrument to measure it. The Visual Analogue Scale (VAS) is an existing tool to measure subjective human feedback by asking the user to mark a point on a line with each end of the line representing an opposing anchor statement. It can be modified to show the previous response, allowing the subject to directly indicate if the current condition is better or worse than the previous one. The goal of this study was to determine how well the modified VAS could measure user-perceived comfort as the exoskeleton control parameters were varied. To validate the survey, 14 healthy subjects walked in a pair of ankle exoskeletons with approximately ten distinct sets of control parameters tested in a prescribed order. Each set of control parameters was tested twice. After each trial, user-perceived comfort was measured using a two-question VAS survey. The repeatability coefficient was approximately 40 mm, similar to the total range of responses. The results were also inconsistent, with relative rankings between consecutive pairs of conditions matching for approximately 50% of comparisons. Thus, as tested, the VAS was not repeatable or consistent. It is possible that subject adaptation within the trial and over the course of the experiment may have impacted the results. Additional work is needed to develop a repeatable method to measure comfort and to determine how perceived comfort varies as subjects' gain exoskeleton experience.

A global bibliometric and visualized analysis of gait analysis and artificial intelligence research from 1992 to 2022

Gait is an important basic function of human beings and an integral part of life. Many mental and physical abnormalities can cause noticeable differences in a person's gait. Abnormal gait can lead to serious consequences such as falls, limited mobility and reduced life satisfaction. Gait analysis, which includes joint kinematics, kinetics, and dynamic Electromyography (EMG) data, is now recognized as a clinically useful tool that can provide both quantifiable and qualitative information on performance to aid in treatment planning and evaluate its outcome. With the assistance of new artificial intelligence (AI) technology, the traditional medical environment has undergone great changes. AI has the potential to reshape medicine, making gait analysis more accurate, efficient and accessible. In this study, we analyzed basic information about gait analysis and AI articles that met inclusion criteria in the WoS Core Collection database from 1992-2022, and the VosViewer software was used for web visualization and keyword analysis. Through bibliometric and visual analysis, this article systematically introduces the research status of gait analysis and AI. We introduce the application of artificial intelligence in clinical gait analysis, which affects the identification and management of gait abnormalities found in various diseases. Machine learning (ML) and artificial neural networks (ANNs) are the most often utilized AI methods in gait analysis. By comparing the predictive capability of different AI algorithms in published studies, we evaluate their potential for gait analysis in different situations. Furthermore, the current challenges and future directions of gait analysis and AI research are discussed, which will also provide valuable reference information for investors in this field.

A Systematic Review of Gait Analysis in the Context of Multimodal Sensing Fusion and AI

BACKGROUND

Neurological diseases are a leading cause of disability and mortality. Gait, or human walking, is a significant predictor of quality of life, morbidity, and mortality. Gait patterns and other kinematic, kinetic, and balance gait features are accurate and powerful diagnostic and prognostic tools.

OBJECTIVE

This review article focuses on the applicability of gait analysis using fusion techniques and artificial intelligence (AI) models. The aim is to examine the significance of mixing several types of wearable and non-wearable sensor data and the impact of this combination on the performance of AI models.

METHOD

In this systematic review, 66 studies using more than two modalities to record and analyze gait were identified. 40 studies incorporated multiple gait analysis modalities without the use of artificial intelligence to extract gait features such as kinematic, kinetic, margin of stability, temporal, and spatial gait parameters, as well as cerebral activity. Similarly, 26 studies analyzed gait data using multimodal fusion sensors and AI algorithms.

RESULTS

The research summarized here demonstrates that the quality of gait analysis and the effectiveness of AI models can both benefit from the integration of data from many sensors. Meanwhile, the utilization of EMG signals in fusion data is especially advantageous.

CONCLUSION

The findings of this review suggest that a smart, portable, wearable-based gait and balance assessment system can be developed using multimodal sensing of the most cutting-edge, clinically relevant tools and technology available. The information presented in this article may serve as a vital springboard for such development.

Presence of Post-traumatic Limps in an Adult Population Diagnosed on a "Catwalk Video": A Comparative Study Between Healthcare and Nonhealthcare Individuals

Background Walking is an everyday activity but also complex in nature. Gait disorders have the potential to drastically affect an individual's quality of life and their ability to be independent. The causes of gait disorders are numerous. To identify abnormal gait, clinicians utilize gait analysis. The aim of this study is to assess how well individuals can identify limps in postoperative traumatized individuals with lower extremity deformities. Methods Participants observed a video compiled of individuals with various gait abnormalities and severities of limps. In the video, there were nine abnormal gait presentations, four obvious limps, and five subtle limps, while the other 10 gait presentations were normal gaits. Classifications for gait presentations were assigned by the research team. Participants assigned a classification to each limp case presented in the video on a survey. The participants were separated into two groups: those with healthcare experience and lay individuals. A Mann-Whitney U-test was used to compare healthcare experience and lay individuals' ability to identify limps correctly. In addition, the observers were evaluated on their ability to perform a screening diagnosis of a limp. Results A total of 100 participants were included in the study, 46 with healthcare experience and 54 individuals without. All tests, identification of limp and subtle limp, using the Mann-Whitney U-test yielded non-significant differences between healthcare and nonhealthcare experience. Overall lowest correctness between both groups came when attempting to identify subtle limp (healthcare = 57.39%, nonhealthcare = 56.67%) while the highest correctness yield was when identifying limp (healthcare = 96.74%, nonhealthcare = 95.37%). Analysis of the observers' ability to perform a screening diagnosis of limp provided close to gold standard results (sensitivity = 96.0%, specificity = 98.7%, positive predictive value = 99.2%, negative predictive value = 98.4%). Conclusion This study showed that nonhealthcare individuals can accurately perform gait analysis from a video, particularly in identifying the presence of a limp, to a similar extent as individuals with healthcare experience. The implementation of two-dimensional catwalk videos taken from a smartphone is beneficial due to accessibility and cost-effectiveness. It also suggested that limp diagnosis can be done as a screening test, using individuals as the screener.

Effects of tai chi on postural balance and quality of life among the elderly with gait disorders: A systematic review

BACKGROUND

Tai Chi is good for improving the physical fitness of older adults. But few studies have reported the effects of Tai Chi on the postural balance and quality of life of older adults with gait disorders.

OBJECTIVE

This review aimed to assess the influence of tai chi on postural stability and quality of life in older adults with abnormal gait.

METHOD

According to the literature retrieval principles, the works published from the inception date to May 2023 were retrieved, including the following databases: PubMed, Scopus, Web of Science, China National Knowledge Infrastructure, EBSCOhost, and Google Scholar. Subsequently, literature screening and quality assessment were performed.

RESULTS

A total of 16 randomized controlled trials were included in this study, Tai Chi intervention can affect populations with Parkinson's disease (PD), no exercise, mild cognitive impairment (MCI), chronic stroke, sedentary, fear of falling, or history of falling. Postural instability is associated with balance, gait, the Unified Parkinson's Disease Rating Scale Motor Subscale 3 (UPDRS III), mobility, lower body strength, and falls. Only two articles looked at quality of life. The Yang style is the most commonly used in the intervention. Nonetheless, most studies were performed on female participants, hence, more research on older male populations is needed.

CONCLUSION

Tai Chi intervention benefits postural balance in patients with gait disorders. 12 weeks is the most common intervention period for patients with gait disorders. The frequency of intervention is seven articles twice a week, and the intervention time is about 60 minutes. The Tai Chi intervention methods in this study involve Yang Style, Sun Style, Taoist Tai Chi, and Health Qigong Tai Chi, but the Yang Style Tai Chi intervention is the most widely used.

Relationship between gait parameters and cognitive indexes in adult aging

PURPOSE

This study aimed to identify the most effective summary cognitive index predicted from spatio-temporal gait features (STGF) extracted from gait patterns.

METHODS

The study involved 125 participants, including 40 young (mean age: 27.65 years, 50% women), and 85 older adults (mean age: 73.25 years, 62.35% women). The group of older adults included both healthy adults and those with Mild Cognitive Impairment (MCI). Participant´s performance in various cognitive domains was evaluated using 12 cognitive measures from five neuropsychological tests. Four summary cognitive indexes were calculated for each case: 1) the z-score of Mini-Mental State Examination (MMSE) from a population norm (MMSE z-score); 2) the sum of the absolute z-scores of the patients' neuropsychological measures from a population norm (ZSum); 3) the first principal component scores obtained from the individual cognitive variables z-scores (PCCog); and 4) the Mahalanobis distance between the vector that represents the subject's cognitive state (defined by the 12 cognitive variables) and the vector corresponding to a population norm (MDCog). The gait patterns were recorded using a body-fixed Inertial Measurement Unit while participants executed four walking tasks (normal, fast, easy- and hard-dual tasks). Sixteen STGF for each walking task, and the dual-task costs for the dual tasks (when a subject performs an attention-demanding task and walks at the same time) were computed. After applied Principal Component Analysis to gait measures (96 features), a robust regression was used to predict each cognitive index and individual cognitive variable. The adjusted proportion of variance (adjusted-R2) coefficients were reported, and confidence intervals were estimated using the bootstrap procedure.

RESULTS

The mean values of adjusted-R2 for the summary cognitive indexes were as follows: 0.0248 for MMSE z-score, 0.0080 for ZSum, 0.0033 for PCCog, and 0.4445 for MDCog. The mean adjusted-R2 values for the z-scores of individual cognitive variables ranged between 0.0009 and 0.0693. Multiple linear regression was only statistically significant for MDCog, with the highest estimated adjusted-R2 value.

CONCLUSIONS

The association between individual cognitive variables and most of the summary cognitive indexes with gait parameters was weak. However, the MDCog index showed a stronger and significant association with the STGF, exhibiting the highest value of the proportion of the variance that can be explained by the predictor variables. These findings suggest that the MDCog index may be a useful tool in studying the relationship between gait patterns and cognition.

New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease

Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.

Multimorbidity Patterns and Associations with Gait, Balance and Lower Extremity Muscle Function in the Elderly: A Cross-Sectional Study in Northwest China

Purpose

Fall is a common geriatric syndrome leading to various adverse outcomes in the elderly. Gait and balance disorders and decreased lower extremity muscle function are the major intrinsic risk factors of falls, and studies suggested that they were closely related to the underlying chronic conditions. This study aimed to explore the patterns of multimorbidity and determine the associations of these multimorbidity patterns with gait, balance and lower extremity muscle function.

Patients and Methods

A cross-sectional survey of 4803 participants aged ≥60 years in Shaanxi Province, China was conducted and the self-reported chronic conditions were investigated. The 6-m walk test, timed-up-and-go test (TUG) and 5-sit-to-stand test (5-STS) were conducted to evaluate gait, balance, and lower extremity muscle function respectively. Latent class analysis was used to explore patterns of multimorbidity, and multivariate regression analysis was used to determine the associations of multimorbidity patterns with gait, balance, and lower extremity muscle function.

Results

Five multimorbidity patterns were identified: Degenerative Disease Class, Cardio-metabolic Class, Stroke-Respiratory-Depression Class, Gastrointestinal Class, and Very sick Class, and they were differently associated with gait and balance disorders and decreased lower extremity muscle function. In particular, the multimorbidity patterns of Degenerative Disease Class and Stroke-Respiratory-Depression Class were closely associated with all the three risk factors of falls.

Conclusion

There are significant differences in the impact of different multimorbidity patterns on the major intrinsic risk factors of falls in the elderly population, and appropriate multimorbidity patterns are closely related to the prediction of falls and can help to develop fall prevention strategies in the elderly.

STRIDE: Systematic Radar Intelligence Analysis for ADRD Risk Evaluation with Gait Signature Simulation and Deep Learning

Abnormal gait is a significant non-cognitive biomarker for Alzheimer's disease (AD) and AD-related dementia (ADRD). Micro-Doppler radar, a non-wearable technology, can capture human gait movements for potential early ADRD risk assessment. In this research, we propose to design STRIDE integrating micro-Doppler radar sensors with advanced artificial intelligence (AI) technologies. STRIDE embeds a new deep learning (DL) classification framework. As a proof of concept, we develop a "digital-twin" of STRIDE, consisting of a human walking simulation model and a micro-Doppler radar simulation model, to generate a gait signature dataset. Taking established human walking parameters, the walking model simulates individuals with ADRD under various conditions. The radar model based on electromagnetic scattering and the Doppler frequency shift model is employed to generate micro-Doppler signatures from different moving body parts (e.g., foot, limb, joint, torso, shoulder, etc.). A band-dependent DL framework is developed to predict ADRD risks. The experimental results demonstrate the effectiveness and feasibility of STRIDE for evaluating ADRD risk.

Evidence for the Efficacy of Commercially Available Wearable Biofeedback Gait Devices: Consumer-Centered Review

BACKGROUND

The number of wearable technological devices or sensors that are commercially available for gait training is increasing. These devices can fill a gap by extending therapy outside the clinical setting. This was shown to be important during the COVID-19 pandemic when people could not access one-on-one treatment. These devices vary widely in terms of mechanisms of therapeutic effect, as well as targeted gait parameters, availability, and strength of the evidence supporting the claims.

OBJECTIVE

This study aimed to create an inventory of devices targeting improvement in gait pattern and walking behavior and identify the strength of the evidence underlying the claims of effectiveness for devices that are commercially available to the public.

METHODS

As there is no systematic or reproducible way to identify gait training technologies available to the public, we used a pragmatic, iterative approach using both the gray and published literature. Four approaches were used: simple words, including some suggested by laypersons; devices endorsed by condition-specific organizations or charities; impairment-specific search terms; and systematic reviews. A findable list of technological devices targeting walking was extracted separately by 3 authors. For each device identified, the evidence for efficacy was extracted from material displayed on the websites, and full-text articles were obtained from the scientific databases PubMed, Ovid MEDLINE, Scopus, or Google Scholar. Additional information on the target population, mechanism of feedback, evidence for efficacy or effectiveness, and commercial availability was obtained from the published material or websites. A level of evidence was assigned to each study involving the device using the Oxford Centre for Evidence-Based Medicine classification. We also proposed reporting guidelines for the clinical appraisal of devices targeting movement and mobility.

RESULTS

The search strategy for this consumer-centered review yielded 17 biofeedback devices that claim to target gait quality improvement through various sensory feedback mechanisms. Of these 17 devices, 11 (65%) are commercially available, and 6 (35%) are at various stages of research and development. Of the 11 commercially available devices, 4 (36%) had findable evidence for efficacy potential supporting the claims. Most of these devices were targeted to people living with Parkinson disease. The reporting of key information about the devices was inconsistent; in addition, there was no summary of research findings in layperson's language.

CONCLUSIONS

The amount of information that is currently available to the general public to help them make an informed choice is insufficient, and, at times, the information presented is misleading. The evidence supporting the effectiveness does not cover all aspects of technology uptake. Commercially available technologies help to provide continuity of therapy outside the clinical setting, but there is a need to demonstrate effectiveness to support claims made by the technologies.

Temporal Muscle Thickness Correlates with Sarcopenia in Parkinson’s Disease

Background: Sarcopenia is characterized by a progressive loss of muscle mass, strength, and function resulting in adverse health outcomes. Current assessment strategies are bothersome and means to simplify the diagnosis are an unmet medical need in Parkinson’s disease (PD). Objective: To evaluate temporal muscle thickness (TMT) obtained on routine cranial MRI as a surrogate marker of sarcopenia in PD patients. Methods: We correlated TMT from axial non-contrast-enhanced T1-weighted sequences of MRI close (±12 months) to an outpatient visit including sarcopenia (EWGSOP1, EWGSOP2, SARC-F), frailty (Fried’s criteria, clinical frailty scale), and disease characteristics of Parkinson’s patients (Hoehn and Yahr-scale, Movement Disorder Society-Unified Parkinson’s Disease Rating Scale, quality of life with the Parkinson’s Disease Questionnaire-8) assessments. Results: Cranial MRI was available in 32 patients with a mean age of 73.56±5.14 years, mean disease duration of 11.46±5.66 years, and median Hoehn and Yahr stage of 2.5. The mean TMT was 7.49±2.76 (7.15) mm. Mean TMT was significantly associated with sarcopenia (EWGSOP2, p = 0.018; EWGSOP1, p = 0.023) and frailty status (physical phenotype; p = 0.045). Moreover, there were significant moderate to strong correlations between TMT measurement and appendicular skeletal muscle mass index (r: 0.437, p = 0.012), as well as handgrip strength (r: 0.561, p <  0.001). Conclusion: Reduced TMT seems to be a promising surrogate marker for sarcopenia (EWGSOP2) and muscle strength in this pilot study in PD patients.

Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data

Walking gait data acquired with force platforms may be used for person re-identification (re-ID) in various authentication, surveillance, and forensics applications. Current force platform-based re-ID systems classify a fixed set of identities (IDs), which presents a problem when IDs are added or removed from the database. We formulated force platform-based re-ID as a deep metric learning (DML) task, whereby a deep neural network learns a feature representation that can be compared between inputs using a distance metric. The force platform dataset used in this study is one of the largest and the most comprehensive of its kind, containing 193 IDs with significant variations in clothing, footwear, walking speed, and time between trials. Several DML model architectures were evaluated in a challenging setting where none of the IDs were seen during training (i.e., zero-shot re-ID) and there was only one prior sample per ID to compare with each query sample. The best architecture was 85% accurate in this setting, though an analysis of changes in walking speed and footwear between measurement instances revealed that accuracy was 28% higher on same-speed, same-footwear comparisons, compared to cross-speed, cross-footwear comparisons. These results demonstrate the potential of DML algorithms for zero-shot re-ID using force platform data, and highlight challenging cases.

Can We Predict Imbalance in Patients? Analysis of the CDC National Health and Nutrition Examination Survey

Understanding global body balance can optimize the postoperative course for patients undergoing spinal or lower limb surgical realignment. This observational cohort study aimed to characterize patients with reported imbalance and identify predictors. The CDC establishes a representative sample annually via the NHANES. All participants who said "yes" (Imbalanced) or "no" (Balanced) to the following question were identified from 1999-2004: "During the past 12 months, have you had dizziness, difficulty with balance or difficulty with falling?" Univariate analyses compared Imbalanced versus Balanced subjects and binary logistic regression modeling predicted for Imbalance. Of 9964 patients, imbalanced (26.5%) were older (65.4 vs. 60.6 years), with more females (60% vs. 48%). Imbalanced subjects reported higher rates of comorbidities, including osteoporosis (14.4% vs. 6.6%), arthritis (51.6% vs. 31.9%), and low back pain (54.4% vs 32.7%). Imbalanced patients had more difficulty with activities, including climbing 10 steps (43.8% vs. 21%) and stooping/crouching/kneeling (74.3% vs. 44.7%), and they needed greater time to walk 20 feet (9.5 vs. 7.1 s). Imbalanced subjects had significantly lower caloric and dietary intake. Regression revealed that difficulties using fingers to grasp small objects (OR: 1.73), female gender (OR: 1.43), difficulties with prolonged standing (OR: 1.29), difficulties stooping/crouching/kneeling (OR: 1.28), and increased time to walk 20 feet (OR: 1.06) were independent predictors of Imbalance (all p < 0.05). Imbalanced patients were found to have identifiable comorbidities and were detectable using simple functional assessments. Structured tests that assess dynamic functional status may be useful for preoperative optimization and risk-stratification for patients undergoing spinal or lower limb surgical realignment.

Experiment protocols for brain-body imaging of locomotion: A systematic review

Introduction

Human locomotion is affected by several factors, such as growth and aging, health conditions, and physical activity levels for maintaining overall health and well-being. Notably, impaired locomotion is a prevalent cause of disability, significantly impacting the quality of life of individuals. The uniqueness and high prevalence of human locomotion have led to a surge of research to develop experimental protocols for studying the brain substrates, muscle responses, and motion signatures associated with locomotion. However, from a technical perspective, reproducing locomotion experiments has been challenging due to the lack of standardized protocols and benchmarking tools, which impairs the evaluation of research quality and the validation of previous findings.

Methods

This paper addresses the challenges by conducting a systematic review of existing neuroimaging studies on human locomotion, focusing on the settings of experimental protocols, such as locomotion intensity, duration, distance, adopted brain imaging technologies, and corresponding brain activation patterns. Also, this study provides practical recommendations for future experiment protocols.

Results

The findings indicate that EEG is the preferred neuroimaging sensor for detecting brain activity patterns, compared to fMRI, fNIRS, and PET. Walking is the most studied human locomotion task, likely due to its fundamental nature and status as a reference task. In contrast, running has received little attention in research. Additionally, cycling on an ergometer at a speed of 60 rpm using fNIRS has provided some research basis. Dual-task walking tasks are typically used to observe changes in cognitive function. Moreover, research on locomotion has primarily focused on healthy individuals, as this is the scenario most closely resembling free-living activity in real-world environments.

Discussion

Finally, the paper outlines the standards and recommendations for setting up future experiment protocols based on the review findings. It discusses the impact of neurological and musculoskeletal factors, as well as the cognitive and locomotive demands, on the experiment design. It also considers the limitations imposed by the sensing techniques used, including the acceptable level of motion artifacts in brain-body imaging experiments and the effects of spatial and temporal resolutions on brain sensor performance. Additionally, various experiment protocol constraints that need to be addressed and analyzed are explained.

Characterizing The State Of Heart Rate Autonomic Regulation And EEG In Elderly Women With Falls Syndrome

Objective — The goal of our study was to evaluate the contribution of the central and autonomic nervous systems to the maintenance of postural balance in the elderly. Material and Methods ― This study included 120 healthy women 60-74 YO living in the community. They were distributed among two subgroups: (1) fallers (n=60) and (2) non-fallers (n=60). Fallers had falls in the past 12 months, while non-fallers had none. Walk tests were performed using Balance Master® posturography machine (NeuroCom, Natus, USA). The state of the central nervous system was assessed by Geodesic 300 EEG system (GSN; Electrical Geodesics, Inc.; Eugene, OR, USA). The state of the autonomic nervous system was assessed by the VNS-Spectrum (Neurosoft, Russia) via cardiointervalography. Results ― In Sit to Stand test, the postural sway velocity was higher (p=0.047) and the walking speed (p=0.008) in Tandem Walk test was lower in fallers, compared with non-fallers. The results of the Step Quick Turn test showed that the movement times of the left and right legs were longer for the fallers vs. non-fallers (p=0.044 and p=0.036, respectively), and postural sway values during turning left or right were higher in fallers as well (p=0.001 and p=0.003, correspondingly). At the same time, Step Up and Over test revealed that lift-up index values were higher in fallers vs. non-fallers (p=0.016). There were no statistically significant differences between the groups in the state of the autonomic nervous system. The absolute spectral power of the EEG was lower in the frontal region of the right hemisphere in the alpha and beta EEG bands in the study group, compared with the control. Conclusion ― A deterioration in the quality of performing complex motor actions and a decrease in postural control in elderly women with falls syndrome were which may have caused their postural instability. Our results confirmed the need for early assessment of the dynamic components of postural control to prevent the development of falls as a geriatric syndrome.

Association of enlarged perivascular spaces with upper extremities and gait impairment: An observational, prospective cohort study

Background and objective

Gait disturbances are common in the elderly and can lead to the loss of functional independence and even death. Enlarged perivascular space (EPVS) and motor performance may be related, but only few studies have explored this relationship. The aim of our study was to investigate the effects of both the severity and location of EPVS on movement disorders.

Method

Two hundred and six participants aged between 45 and 85 years old with complete magnetic resonance imaging (MRI) data were included in our analysis. EPVS were divided into basal ganglia (BG) and centrum semiovale (CSO), and their grades were measured. Gait was assessed quantitatively using a 4-m walkway and TUG test as well as semi-quantitatively using the Tinetti and SPPB tests. The function of upper extremities was evaluated by 10-repeat pronation–supination, 10-repeat finger-tapping, and 10-repeat opening and closing of the hands.

Results

Both high-grade EPVS, whether in BG and CSO, were independently correlated with gait parameters, the TUG time, Tinetti, and SPPB tests. The EPVS located in BG had a significant association with 10-repeat finger-tapping time (β = 0.231, P = 0.025) and a similar association was also observed between CSO-EPVS and 10-repeat pronation–supination time (β = 0.228, P = 0.014).

Conclusion

Our results indicated that EPVS was associated with gait disturbances, and a further investigation found that EPVS has an association with upper extremities disorder. EPVS should be considered as a potential target for delaying gait and upper extremities damage since CSVD can be prevented to some extent.

A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction

Musculoskeletal and neurological disorders are the most common causes of walking problems among older people, and they often lead to diminished quality of life. Analyzing walking motion data manually requires trained professionals and the evaluations may not always be objective. To facilitate early diagnosis, recent deep learning-based methods have shown promising results for automated analysis, which can discover patterns that have not been found in traditional machine learning methods. We observe that existing work mostly applies deep learning on individual joint features such as the time series of joint positions. Due to the challenge of discovering inter-joint features such as the distance between feet (i.e. the stride width) from generally smaller-scale medical datasets, these methods usually perform sub-optimally. As a result, we propose a solution that explicitly takes both individual joint features and inter-joint features as input, relieving the system from the need of discovering more complicated features from small data. Due to the distinctive nature of the two types of features, we introduce a two-stream framework, with one stream learning from the time series of joint position and the other from the time series of relative joint displacement. We further develop a mid-layer fusion module to combine the discovered patterns in these two streams for diagnosis, which results in a complementary representation of the data for better prediction performance. We validate our system with a benchmark dataset of 3D skeleton motion that involves 45 patients with musculoskeletal and neurological disorders, and achieve a prediction accuracy of 95.56%, outperforming state-of-the-art methods.

Mobile electroencephalography captures differences of walking over even and uneven terrain but not of single and dual-task gait

Walking on natural terrain while performing a dual-task, such as typing on a smartphone is a common behavior. Since dual-tasking and terrain change gait characteristics, it is of interest to understand how altered gait is reflected by changes in gait-associated neural signatures. A study was performed with 64-channel electroencephalography (EEG) of healthy volunteers, which was recorded while they walked over uneven and even terrain outdoors with and without performing a concurrent task (self-paced button pressing with both thumbs). Data from n = 19 participants (M = 24 years, 13 females) were analyzed regarding gait-phase related power modulations (GPM) and gait performance (stride time and stride time-variability). GPMs changed significantly with terrain, but not with the task. Descriptively, a greater beta power decrease following right-heel strikes was observed on uneven compared to even terrain. No evidence of an interaction was observed. Beta band power reduction following the initial contact of the right foot was more pronounced on uneven than on even terrain. Stride times were longer on uneven compared to even terrain and during dual- compared to single-task gait, but no significant interaction was observed. Stride time variability increased on uneven terrain compared to even terrain but not during single- compared to dual-tasking. The results reflect that as the terrain difficulty increases, the strides become slower and more irregular, whereas a secondary task slows stride duration only. Mobile EEG captures GPM differences linked to terrain changes, suggesting that the altered gait control demands and associated cortical processes can be identified. This and further studies may help to lay the foundation for protocols assessing the cognitive demand of natural gait on the motor system.

Gait Characteristics of Dynapenia, Sarcopenia, and Presarcopenia in Community-Dwelling Japanese Older Women: A Cross-Sectional Study

Age-related decline in skeletal muscle mass and function are risk factors for reduced walking ability. This study aimed to understand the characteristic gait parameters of presarcopenia (low muscle mass only), dynapenia (low muscle function only), and sarcopenia (low muscle mass and function), which have differing skeletal muscle characteristics. Skeletal muscle mass, grip strength, and gait parameters (walking speed, cadence, step length, step width, gait angle, foot angle, stance time, swing time, and double stance time) were evaluated in 307 older Japanese women. Low muscle function was determined by grip strength and normal walking speed. Participants were assessed and divided into the normal (60.9%, n = 187), presarcopenia (25.7%, n = 79), dynapenia (5.2%, n = 16), and sarcopenia (8.1%, n = 25) groups. When compared to the normal group, the sarcopenia group had significantly slower walking speed and shorter step length (p < 0.05); the dynapenia group had significantly slower walking speed, smaller cadence, shorter step length, wider step width, and longer stance time (p < 0.05); and the presarcopenia group showed no differences. Skeletal muscle function may therefore be more strongly related to reduced walking function in older adults than body composition factors. The decrease in walking function was most pronounced in older women with dynapenia.

mVEGAS - mobile smartphone-based spatiotemporal gait analysis in healthy and ataxic gait disorders

BACKGROUND

Quantitative gait assessment is increasingly applied in fall risk stratification, diagnosis, and disease monitoring of neuro-geriatric gait disorders. Its broad application, however, demands for low-cost and mobile solutions that facilitate high-quality assessment outside laboratory settings. The aim of this study was to present and evaluate the concurrent validity of a mobile and low-cost gait assessment tool (mVEGAS) that combines body-fixed inertial sensors and a smartphone-based video capture for spatiotemporal identification of gait sequences.

METHODS

Initially, we examined potential interferences of wearing mVEGAS with walking performance in a cohort of 20 young healthy individuals (31.1 ± 10.1 years; 8 females). Subsequently, we assessed the concurrent validity of mVEGAS as compared to a pressure-sensitive gait carpet (GAITRite) in a cohort of 26 healthy individuals (55.8 ± 14.3 years; 10 females) and 26 patients (55.7 ± 14.0; 14 females) with moderate to severe degrees of cerebellar gait ataxia. All participants were instructed to walk at preferred, slow, and fast walking speed and standard average and variability gait measures including velocity, stride length, stride time, base of support, swing and double support phase were examined for agreement between the two systems by absolute error and intraclass correlation coefficients (ICC).

RESULTS

Wearing mVEGAS did only marginally interfere with normal walking behavior. mVEGAS-derived average and variability gait measures exhibited good to excellent concurrent validity in healthy individuals (ICCs ranging between 0.645 and 1.000) and patients with gait ataxia (ICCs ranging between 0.788 and 1.000) SIGNIFICANCE: mVEGAS may facilitate high-quality and long-term gait monitoring in different non-specialized environments such as medical practices, nursing homes or community centers.

Superior gait performance and balance ability in Latin dancers

Background

Latin dance consists of various fast and stability-challenging movements that require constant body adjustments to maintain proper posture and balance. Although human gaits are assumed to be symmetrical, several factors can contribute to asymmetrical behavior of the lower extremities in healthy adults. These include lower limb dominance, ground reaction forces, lower limb muscle power, foot placement angle, and range of joint motion. Gait impairment can lead to a high risk of falling, diminished mobility, and even cognition impairment. We hypothesized that Latin dancers might have a more symmetric gait pattern and better balance ability than healthy non-dancer controls.

Methods

We investigated the impact of Latin dance training on gait behaviors and body balance. We recruited twenty Latin dancers and 22 normal healthy subjects to conduct walking experiments and one-leg stance tests, and we measured their kinematic data by inertial measurement units. We then defined four performance indexes to assess gait performance and body stability to quantify the potential advantages of dance training.

Results

We found that the two gait asymmetric indexes during the walking test and the two performance indexes during the one-leg stance tests were better in Latin dancers compared with the healthy control group. The results confirmed the superiority of Latin dancers over the healthy control group in gait symmetry and balance stability. Our results suggest that Latin dancing training could effectively strengthen lower limb muscles and core muscle groups, thereby improving coordination and enhancing gait performance and balance.

Conclusion

Latin dance training can benefit gait performance and body balance. Further studies are needed to investigate the effect of Latin dance training on gait and balance outcomes in healthy subjects and patients with gait disorders.

Stable Sparse Classifiers predict cognitive impairment from gait patterns

Background

Although gait patterns disturbances are known to be related to cognitive decline, there is no consensus on the possibility of predicting one from the other. It is necessary to find the optimal gait features, experimental protocols, and computational algorithms to achieve this purpose.

Purposes

To assess the efficacy of the Stable Sparse Classifiers procedure (SSC) for discriminating young and healthy older adults (YA vs. HE), as well as healthy and cognitively impaired elderly groups (HE vs. MCI-E) from their gait patterns. To identify the walking tasks or combinations of tasks and specific spatio-temporal gait features (STGF) that allow the best prediction with SSC.

Methods

A sample of 125 participants (40 young- and 85 older-adults) was studied. They underwent assessment with five neuropsychological tests that explore different cognitive domains. A summarized cognitive index (MDCog), based on the Mahalanobis distance from normative data, was calculated. The sample was divided into three groups (young adults, healthy and cognitively impaired elderly adults) using k-means clustering of MDCog in addition to Age. The participants executed four walking tasks (normal, fast, easy- and hard-dual tasks) and their gait patterns, measured with a body-fixed Inertial Measurement Unit, were used to calculate 16 STGF and dual-task costs. SSC was then employed to predict which group the participants belonged to. The classification's performance was assessed using the area under the receiver operating curves (AUC) and the stable biomarkers were identified.

Results

The discrimination HE vs. MCI-E revealed that the combination of the easy dual-task and the fast walking task had the best prediction performance (AUC = 0.86, sensitivity: 90.1%, specificity: 96.9%, accuracy: 95.8%). The features related to gait variability and to the amplitude of vertical acceleration had the largest predictive power. SSC prediction accuracy was better than the accuracies obtained with linear discriminant analysis and support vector machine classifiers.

Conclusions

The study corroborated that the changes in gait patterns can be used to discriminate between young and healthy older adults and more importantly between healthy and cognitively impaired adults. A subset of gait tasks and STGF optimal for achieving this goal with SSC were identified, with the latter method superior to other classification techniques.

Varied presentations of cervical spondylotic myelopathy presenting to a chiropractic clinic: a report of 3 cases

Cervical spondylotic myelopathy (CSM) is the leading cause of acquired spinal cord dysfunction worldwide and may be expected to increase in prevalence due to an aging global population. Clinical features of CSM are highly variable, and chiropractors frequently manage patients with common signs and symptoms of CSM such as neck pain, extremity weakness, and gait imbalances. Early recognition of signs consistent with myelopathy may mitigate future disability and improve quality of life. Key predictors of patient outcome are the age of initial presentation, baseline CSM severity (as measured by mJOA score), and the presence of gait disturbances. This report describes three cases of CSM presenting to a chiropractic clinic. Each case illustrates a unique manifestation of CSM, including myelopathy, myeloradiculopathy, and distal neuropathic pain (funicular referral). In addition, a review of CSM terminology, epidemiology, pathobiology, clinical features, imaging, and management is provided.

The Relationship between the Activity Balance Confidence and Mobility Tests among Older Adults in Indonesia

Introduction. Unsteady gait, instability, and lower extremity muscle weakness are some of the risk factors for falls. Reduced balance is a further precursor of falls, and injuries adversely affect the instability. In doing an activity without losing their balance, confidence among older adults is also crucial because it will influence their mobility. Objectives. The objective of this study is to examine the association between activity balance confidence and functional mobility, including gait, balance, and strength, among older adults. Methods. A cross-sectional study was conducted among older adults living in long-term care facilities and community dwellings. A total of 326 older adults (>60 years old) participated in this study from three provinces in Java Island, Indonesia. The inclusion criteria were older adults living independently and without obstacles in communication, who have no hearing loss, and who agreed to be respondents. The activity-specific balance confidence (ABC) scale determines the level of confidence. The participants were asked about their balance confidence not to lose their balance while doing 16 activities. The dependent variable is the mobility test, including a gait test using TUG (times up and go) to see how the subjects stand, walk, and turn around; a balance test (four stages); and a strength test (30-second chair stand). Results. The results of the ABC scale showed the respondents felt the most confidence not to lose their balance when they walk around the house (82.01%) and the less confidence when they stepped onto or off an escalator while holding onto a railing (37.7%). The gait, balance, and strength test revealed that 51.2% of the respondents showed an unsteady gait, 63.8% showed instability that felt awkward and unusual when standing on one leg, and 60.1% of the participants showed muscle weakness. The bivariate analysis significantly correlated the ABC scale test and all mobility tests. The older adult participants who are not confident will have 12.03 times higher the unstable result of the gait test, 8.4 times higher the unstable result of the balance test, and 7.47 times higher the less strength result of the strength test who are confident. Conclusion. Older adults who lack balance confidence showed significantly poorer results in mobility tests.

Differential diagnosis between Parkinson's disease and atypical parkinsonism based on gait and postural instability: Artificial intelligence using an enhanced weight voting ensemble model

BACKGROUND

Parkinsonian diseases and cerebellar ataxia among movement disorders, are representative diseases which present with distinct pathological gaits. We proposed a machine learning system that can differentiate Parkinson's disease (PD), cerebellar ataxia and progressive supranuclear palsy Richardson syndrome (PSP-RS) based on postural instability and gait analysis.

METHODS

We screened 1467 gait (GAITRite) and postural instability (Pedoscan) analyses performed in Samsung Medical Center from January 2019 to December 2020. PD, probable PSP-RS, and cerebellar ataxia (i.e., probable MSA-C, hereditary ataxia, and sporadic adult-onset ataxia) were included in the study. The gated recurrent units for GaitRite and the deep neural network for Pedoscan were applied. The enhanced weight voting ensemble (EWVE) method was applied to incorporate the two modalities.

RESULTS

We included 551 PD, 38 PSP-RS, 113 cerebellar ataxia and among them, 71 were MSA-C. Pedoscan-based and Gait-based model showed high sensitivity but low specificity in differentiating atypical parkinsonism from PD. The EWVE showed significantly improved specificity and reliable performance in differentiation between PD vs. ataxia patients (AUC 0.974 ± 0.036, sensitivity 0.829 ± 0.217, specificity 0.969 ± 0.038), PD vs. MSA-C (AUC 0.975 ± 0.020, sensitivity 0.823 ± 0.162, specificity 0.932 ± 0.030) and PD vs. PSP-RS (AUC 0.963 ± 0.028, sensitivity 0.555 ± 0.157, specificity 0.936 ± 0.031).

CONCLUSION

We proposed reliable Pedoscan-based, Gait-based and EWVE model in differentiating gait disorders by integrating information from gait and postural instability. This model can provide diagnosis guidelines to primary caregivers and assist in differential diagnosis of PD from atypical parkinsonism for neurologists.

Impact of medium-chain triglycerides on gait performance and brain metabolic network in healthy older adults: a double-blind, randomized controlled study

Nutritional supplementation with medium-chain triglycerides (MCTs) has the potential to increase memory function in elderly patients with frailty and dementia. Our aim was to investigate the effects of MCT on cognitive and gait functions and their relationships with focal brain metabolism and functional connectivity even in healthy older adults. Participants were blindly randomized and allocated to two groups: 18 g/day of MCT oil and matching placebo formula (control) administered as a jelly stick (6 g/pack, ingested three times a day). Gait analysis during the 6-m walk test, cognition, brain focal glucose metabolism quantified by ¹⁸F-fluorodeocyglucose positron emission tomography, and magnetic resonance imaging-based functional connectivity were assessed before and after a 3-month intervention. Sixty-three healthy, normal adults (females and males) were included. Compared with the control group, the MCT group showed better balance ability, as represented by the lower Lissajous index (23.1 ± 14.4 vs. 31.3 ± 18.9; P < 0.01), although no time × group interaction was observed in cognitive and other gait parameters. Moreover, MCT led to suppressed glucose metabolism in the right sensorimotor cortex compared with the control (P < 0.001), which was related to improved balance (r = 0.37; P = 0.04) along with increased functional connectivity from the ipsilateral cerebellar hemisphere. In conclusion, a 3-month MCT supplementation improves walking balance by suppressing glucose metabolism, which suggests the involvement of the cerebro-cerebellar network. This may reflect, at least in part, the inverse reaction of the ketogenic switch as a beneficial effect of long-term MCT dietary treatment.

Using Machine Learning to Identify Feelings of Energy and Fatigue in Single-Task Walking Gait: An Exploratory Study

The objective of this study was to use machine learning to identify feelings of energy and fatigue using single-task walking gait. Participants (n = 126) were recruited from a university community and completed a single protocol where current feelings of energy and fatigue were measured using the Profile of Moods Survey–Short Form approximately 2 min prior to participants completing a two-minute walk around a 6 m track wearing APDM mobility monitors. Gait parameters for upper and lower extremity, neck, lumbar and trunk movement were collected. Gradient boosting classifiers were the most accurate classifiers for both feelings of energy (74.3%) and fatigue (74.2%) and Random Forest Regressors were the most accurate regressors for both energy (0.005) and fatigue (0.007). ANCOVA analyses of gait parameters comparing individuals who were high or low energy or fatigue suggest that individuals who are low energy have significantly greater errors in walking gait compared to those who are high energy. Individuals who are high fatigue have more symmetrical gait patterns and have trouble turning when compared to their low fatigue counterparts. Furthermore, these findings support the need to assess energy and fatigue as two distinct unipolar moods as the signals used by the algorithms were unique to each mood.

Unilateral non-electric assistive walking device helps neurological and orthopedic patients to improve gait patterns

BACKGROUND

Pathological gait patterns are common in neurological and orthopedic patients. These put them at risk of falling and restrict their autonomy and social participation. Novel assistive walking devices are designed to actively support physiological gait patterns by means of motor guidance and mechanical support of the lower limbs.

RESEARCH QUESTION

Does a non-electric assistive walking device powered by a cam-spring mechanism (aLQ, Imasen) improve or otherwise affect pathological gait patterns in neurological and orthopedic patients?

METHODS

A three-dimensional instrumented gait analysis was conducted on a treadmill (quasar, hp cosmos) using spatiotemporal, kinetic, and kinematic data obtained from synchronized motion capturing (Miqus M3, Qualisys), surface EMG (sEMG; Ultium, Noraxon), and pressure distribution measurements (FMD-T, Zebris). Participants with impaired walking were tested in a randomized repeated measures design (assisted/unassisted; at preferred/fast speed) and analyzed with regard to their medical condition (orthopedic or neurological group, n = 20 each).

RESULTS

In both groups, participants showed a significant increase of step length and decrease of cadence during assisted walking compared to baseline. Immediate kinematic effects included enhanced sagittal hip flexion but reduced extension. On the contrary, knee joint angles and muscle activity of M. gastrocnemius and M. rectus femoris seemed to be unaffected by the aLQ device.

SIGNIFICANCE

Participants appear to benefit from the assistive walking device regarding gait and movement patterns, which suggests that the tested device may help to improve patients' functional health status and quality of life. Activities of daily living (ADLs) that involve extensive hip flexion like stairs or curb climbing are promising applications. We propose the implementation of an invertible cam-spring that provides an additional resistance training option.

A Systematic Review of Non-Pharmacological Interventions to Improve Gait Asymmetries in Neurological Populations

Gait asymmetries are commonly observed in neurological populations and linked to decreased gait velocity, balance decrements, increased fall risk, and heightened metabolic cost. Interventions designed to improve gait asymmetries have varying methods and results. The purpose of this systematic review was to investigate non-pharmacological interventions to improve gait asymmetries in neurological populations. Keyword searches were conducted using PubMed, CINAHL, and Academic Search Complete. The search yielded 14 studies for inclusion. Gait was assessed using 3D motion capture systems (n = 7), pressure-sensitive mats (e.g., GAITRite; n = 5), and positional sensors (n = 2). The gait variables most commonly analyzed for asymmetry were step length (n = 11), stance time (n = 9), and swing time (n = 5). Interventions to improve gait asymmetries predominantly used gait training techniques via a split-belt treadmill (n = 6), followed by insoles/orthoses (n = 3). The literature suggests that a wide range of methods can be used to improve spatiotemporal asymmetries. However, future research should further examine kinematic and kinetic gait asymmetries. Additionally, researchers should explore the necessary frequency and duration of various intervention strategies to achieve the greatest improvement in gait asymmetries, and to determine the best symmetry equation for quantifying gait asymmetries.

Insole-Based Systems for Health Monitoring: Current Solutions and Research Challenges

Wearable health monitoring devices allow for measuring physiological parameters without restricting individuals' daily activities, providing information that is reflective of an individual's health and well-being. However, these systems need to be accurate, power-efficient, unobtrusive and simple to use to enable a reliable, convenient, automatic and ubiquitous means of long-term health monitoring. One such system can be embedded in an insole to obtain physiological data from the plantar aspect of the foot that can be analyzed to gain insight into an individual's health. This manuscript provides a comprehensive review of insole-based sensor systems that measure a variety of parameters useful for overall health monitoring, with a focus on insole-based PPD measurement systems developed in recent years. Existing solutions are reviewed, and several open issues are presented and discussed. The concept of a fully integrated insole-based health monitoring system and considerations for future work are described. By developing a system that is capable of measuring parameters such as PPD, gait characteristics, foot temperature and heart rate, a holistic understanding of an individual's health and well-being can be obtained without interrupting day-to-day activities. The proposed device can have a multitude of applications, such as for pathology detection, tracking medical conditions and analyzing gait characteristics.

A Retrospective, Matched Case-Control Study on the Risk Factors of Falls and Varying Severities of Fall-Related Injuries in Inpatients

OBJECTIVE

This study was to investigate the factors that influence falls and the severity of fall-related injuries in inpatients.

METHODS

We adopted a retrospective, matched case-control design and conducted this study at a teaching hospital in Taiwan. Our study period ran from January 2016 to December 2019. We searched for inpatients with official fall records and obtained data from 823 patients, and then established a control group comprising 331 matching patients based on age, sex, length of hospital stay at time of fall incident, and ward type, thereby resulting in 1154 patients in all. A cumulative logistic regression model was applied to the data collected from medical records and fall incident reports to identify the factors that influence falls and the severity of fall-related injuries in inpatients.

RESULTS

The results indicated that the use of sedatives (B = 0.49, P < 0.001), impaired consciousness (B = 0.64, P < 0.001), normal weight versus obese (B = -0.59, P = 0.008), low hemoglobin levels (B = -0.08, P = 0.003), an unsteady gait versus a normal gait (B = 1.82, P < 0.001), and history of falling within the past year (B = 0.56, P < 0.001) were correlated with the falls and the severity of fall-related injuries.

CONCLUSIONS

Our results indicate that the use of sedatives, impaired consciousness, obesity, low hemoglobin levels, poor gait function, and history of falling within the past year are all independent factors that influence falls and the severity of fall-related injuries. This study can provide medical personnel with a reference for the identification of patients with a high risk of falling and implementation of fall prevention measures.

Effects of age and knee osteoarthritis on the modular control of walking: A pilot study

Background

Older adults and individuals with knee osteoarthritis (KOA) often exhibit reduced locomotor function and altered muscle activity. Identifying age- and KOA-related changes to the modular control of gait may provide insight into the neurological mechanisms underlying reduced walking performance in these populations. The purpose of this pilot study was to determine if the modular control of walking differs between younger and older adults without KOA and adults with end-stage KOA.

Methods

Kinematic, kinetic, and electromyography data were collected from ten younger (23.5 ± 3.1 years) and ten older (63.5 ± 3.4 years) adults without KOA and ten adults with KOA (64.0 ± 4.0 years) walking at their self-selected speed. Separate non-negative matrix factorizations of 500 bootstrapped samples determined the number of modules required to reconstruct each participant’s electromyography. One-way Analysis of Variance tests assessed the effect of group on walking speed and the number of modules. Kendall rank correlations (τ_b) assessed the association between the number of modules and self-selected walking speed.

Results

The number of modules required in the younger adults (3.2 ± 0.4) was greater than in the individuals with KOA (2.3 ± 0.7; p = 0.002), though neither cohorts’ required number of modules differed significantly from the unimpaired older adults (2.7 ± 0.5; p ≥ 0.113). A significant association between module number and walking speed was observed (τ_b = 0.350, p = 0.021) and individuals with KOA walked significantly slower (0.095 ± 0.21 m/s) than younger adults (1.24 ± 0.15 m/s; p = 0.005). Individuals with KOA also exhibited altered module activation patterns and composition (which muscles are associated with each module) compared to unimpaired adults.

Conclusion

These findings suggest aging alone may not significantly alter modular control; however, the combined effects of knee osteoarthritis and aging may together impair the modular control of gait.

Approach to the Patient with Gait Disturbance

The assessment of patients presenting with disorders of gait can be a daunting task for neurologists given the broad potential localization and differential diagnosis. However, gait disorders are extremely common in outpatient neurology, and all neurologists should be comfortable with the assessment, triage, and management of patients presenting with difficulty walking. Here, we aim to present a manageable framework for neurologists to approach the assessment of patients presenting with gait dysfunction. We suggest a chief complaint-based phenomenological characterization of gait, using components of the neurological history and examination to guide testing and treatment. We present the framework to mirror the outpatient visit with the patient, highlighting (1) important features of the gait history, including the most common gait-related chief complaints and common secondary (medical) causes of gait dysfunction; (2) gait physiology and a systematic approach to the gait examination allowing appropriate characterization of gait phenomenology; (3) an algorithmic approach to ancillary testing for patients with gait dysfunction based on historical and examination features; and (4) definitive and supportive therapies for the management of patients presenting with common neurological disorders of gait.