Constraints on the kinetic, kinematic and spatiotemporal parameters of gait in Parkinson's disease

Examining virtual reality's influence on kinetic variables for obstacle crossing in Parkinson's disease

BACKGROUND

Individuals with Parkinson's disease (PD) commonly experience postural instability during walking and obstacle crossing. Previous research has explored virtual reality (VR) as a rehabilitation tool that affects gait.

RESEARCH QUESTION

How does a virtual reality environment influence the kinetic characteristics of gait during obstacle crossing in individuals with Parkinson's disease compared to real-world conditions?

METHODS

The study included 15 individuals with PD and 17 matched healthy control participants. The moment and power of lower extremity joints during walking and obstacle crossing conditions in real and VR environments were measured using three-dimensional motion analysis and force platforms.

RESULTS

The results indicate that individuals with PD demonstrated higher hip extensor moments during normal gait (NG) and obstacle crossing (OBS) tasks, accompanied by lower hip flexor moments compared to the control group. Moreover, PD individuals exhibited increased internal rotation hip joint moments during VR obstacle crossing (VR-OBS) tasks compared to NG.

CONCLUSION

Walking in a VR environment altered the kinetic status in both groups, potentially due to cautious walking to maintain stability. The greatest difference between the two groups was observed in the magnitude of joint moment in the sagittal plane, likely influenced by differences in walking speed. These findings underscore the unique joint moment patterns exhibited by PD patients during various tasks. Walking in a VR environment impacts the kinetic parameters of gait, which may have implications for designing rehabilitation programs aimed at improving gait stability in PD individuals. Clinically, these kinetic alterations highlight VR's potential as a safe modality for gait rehabilitation and fall prevention in PD.

Biomechanics Parameters of Gait Analysis to Characterize Parkinson's Disease: A Scoping Review

Parkinson's disease (PD) is characterized by a slow, short-stepping, shuffling gait pattern caused by a combination of motor control limitations due to a reduction in dopaminergic neurons. Gait disorders are indicators of global health, cognitive status, and risk of falls and increase with disease progression. Therefore, the use of quantitative information on the gait mechanisms of PD patients is a promising approach, particularly for monitoring gait disorders and potentially informing therapeutic interventions, though it is not yet a well-established tool for early diagnosis or direct assessment of disease progression. Over the years, many studies have investigated the spatiotemporal parameters that are altered in the PD gait pattern, while kinematic and kinetic gait parameters are more limited. A scoping review was performed according to the PRISMA guidelines. The Scopus and PubMed databases were searched between 1999 and 2023. A total of 29 articles were included that reported gait changes in PD patients under different gait conditions: single free walking, sequential motor task, and dual task. The main findings of our review highlighted the use of optoelectronic systems for recording kinematic parameters and force plates for measuring kinetic parameters, due to their high accuracy. Most gait analyses in PD patients have been conducted at self-selected walking speeds to capture natural movement, although studies have also examined gait under various conditions. The results of our review indicated that PD patients experience alterations in the range of motion of the hip, knee, and ankle joints, as well as a reduction in the power generated/absorbed and the extensor/flexor moments. These findings suggest that the PD gait pattern may be more effectively understood using kinematic and kinetic parameters.

Gait characteristics in idiopathic normal pressure hydrocephalus: a review on the effects of CSF tap test and shunt surgery

BACKGROUND

Idiopathic normal pressure hydrocephalus (iNPH) is a progressive disease characterized by disproportionate ventricular enlargement at brain imaging with gait disturbance and an increased risk of falling. Gait assessment is a key feature in the diagnosis of iNPH and characterization of post-surgical outcomes.

RESEARCH QUESTION

How do gait parameters change 24 h after CSF tap test (CSFTT) and after ventriculoperitoneal shunt surgery?

METHODS

The PRISMA guidelines were used to perform the systematic review. We conducted a search of the following electronic databases: PubMed, Medline, Web of Science and EBSCO. We included studies focusing on gait changes occurring 24 h after a CSFTT or after ventriculoperitoneal shunt surgery in patients with iNPH. All articles were assessed for methodological quality using an adapted version of The Standard Quality Assessment Criteria for Evaluating Primary Research Papers checklist.

RESULTS

Twenty-seven studies were included in the systematic review. Studies were highly heterogeneous due to lack of standardization of CSFTT or shunt surgery methodology, with varying amounts of CSF removed during the tap test (20-50 ml) and varying time of outcome assessment after shunt surgery. Dynamic equilibrium measurements are generally used to assess preoperative levels of cardinal symptoms and postoperative outcomes in iNPH. The most sensitive spatio-temporal parameter assessed 24 h after CSFTT was self-selected walking speed followed by stride length, which increased significantly. Cadence is hence not suitable to consider in the evaluation of effect of CSFTT and shunt surgery. Changes in balance-related gait parameters after CSFTT and shunt surgery are still a controversial area of research.

CONCLUSION

Gait assessment is a key feature in the diagnosis of iNPH and characterization of post-surgical outcomes. Dynamic equilibrium measurements are generally used to assess preoperative levels of cardinal symptoms and postoperative outcomes in iNPH, but quantitative and standardized gait analysis procedures are missing. Changes in balance-related gait parameters after CSFTT might be useful in deciding whether to perform shunt surgery in iNPH patients who hope for improvement in gait ability. The dual-task paradigm after CSFTT could improve the clinical evaluation of higher level frontal gait disturbances in patients with suspected iNPH before shunting.

Transient Increases in Neural Oscillations and Motor Deficits in a Mouse Model of Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor symptoms like tremors and bradykinesia. PD's pathology involves the aggregation of α-synuclein and loss of dopaminergic neurons, leading to altered neural oscillations in the cortico-basal ganglia-thalamic network. Despite extensive research, the relationship between the motor symptoms of PD and transient changes in brain oscillations before and after motor tasks in different brain regions remain unclear. This study aimed to investigate neural oscillations in both healthy and PD model mice using local field potential (LFP) recordings from multiple brain regions during rest and locomotion. The histological evaluation confirmed the significant dopaminergic neuron loss in the injection side in 6-OHDA lesioned mice. Behavioral tests showed motor deficits in these mice, including impaired coordination and increased forelimb asymmetry. The LFP analysis revealed increased delta, theta, alpha, beta, and gamma band activity in 6-OHDA lesioned mice during movement, with significant increases in multiple brain regions, including the primary motor cortex (M1), caudate-putamen (CPu), subthalamic nucleus (STN), substantia nigra pars compacta (SNc), and pedunculopontine nucleus (PPN). Taken together, these results show that the motor symptoms of PD are accompanied by significant transient increases in brain oscillations, especially in the gamma band. This study provides potential biomarkers for early diagnosis and therapeutic evaluation by elucidating the relationship between specific neural oscillations and motor deficits in PD.

Kinematic and Kinetic Gait Features Associated With Mild Cognitive Impairment in Parkinson's Disease

Mild cognitive impairment (MCI) and gait deficits are commonly associated with Parkinson's disease (PD). Early detection of MCI associated with Parkinson's disease (PD-MCI) and its biomarkers is critical to managing disability in PD patients, reducing caregiver burden and healthcare costs. Gait is considered a surrogate marker for cognitive decline in PD. However, gait kinematic and kinetic features in PD-MCI patients remain unknown. This study was designed to explore the difference in gait kinematics and kinetics during single-task and dual-task walking between PD patients with and without MCI. Kinematic and kinetic data of 90 PD patients were collected using 3D motion capture system. Differences in gait kinematic and kinetic gait features between groups were identified by using: first, univariate statistical analysis and then a supervised machine learning analysis. The findings of this study showed that the presence of MCI in PD patients is coupled with kinematic and kinetic deviations of gait cycle which may eventually identify two different phenotypes of the disease. Indeed, as shown by the demographical and clinical comparison between the two groups, PD-MCI patients were older and more impaired. Moreover, PD-MCI kinematic results showed that cognitive dysfunction coexists with more severe axial symptoms and an increase postural flexion. A lack of physiological distal-to-proximal shift in joint kinetics was evidenced in the PD phenotype associated with cognitive impairments.

Visual feedback improves propulsive force generation during treadmill walking in people with Parkinson disease

Persons with Parkinson's disease experience gait alterations, such as reduced step length. Gait dysfunction is a significant research priority as the current treatments targeting gait impairment are limited. This study aimed to investigate the effects of visual biofeedback on propulsive force during treadmill walking in persons with Parkinson's. Sixteen ambulatory persons with Parkinson's participated in the study. They received real-time biofeedback of anterior ground reaction force during treadmill walking at a constant speed. Peak propulsive force values were measured and normalized to body weight. Spatiotemporal parameters were also assessed, including stride length and double support percent. Persons with Parkinson's significantly increased peak propulsive force during biofeedback compared to baseline (p <.0001, Cohen's dz = 1.69). Variability in peak anterior ground reaction force decreased across repeated trials (p <.0001, dz = 1.51). While spatiotemporal parameters did not show significant changes individually, stride length and double support percent improved marginally during biofeedback trials. Persons with Parkinson's can increase propulsive force with visual biofeedback, suggesting the presence of a propulsive reserve. Though stride length did not significantly change, clinically meaningful improvements were observed. Targeting push-off force through visual biofeedback may offer a potential rehabilitation technique to enhance gait performance in Persons with Parkinson's. Future studies could explore the long-term efficacy of this intervention and investigate additional strategies to improve gait in Parkinson's disease.

Gait classification for early detection and severity rating of Parkinson's disease based on hybrid signal processing and machine learning methods

Parkinson's disease (PD) is one of the cognitive degenerative disorders of the central nervous system that affects the motor system. Gait dysfunction represents the pathology of motor symptom while gait analysis provides clinicians with subclinical information reflecting subtle differences between PD patients and healthy controls (HCs). Currently neurologists usually assess several clinical manifestations of the PD patients and rate the severity level according to some established criteria. This is highly dependent on clinician's expertise which is subjective and ineffective. In the present study we address these issues by proposing a hybrid signal processing and machine learning based gait classification system for gait anomaly detection and severity rating of PD patients. Time series of vertical ground reaction force (VGRF) data are utilized to represent discriminant gait information. First, phase space of the VGRF is reconstructed, in which the properties associated with the nonlinear gait system dynamics are preserved. Then Shannon energy is used to extract the characteristic envelope of the phase space signal. Third, Shannon energy envelope is decomposed into high and low resonance components using dual Q-factor signal decomposition derived from tunable Q-factor wavelet transform. Note that the high Q-factor component consists largely of sustained oscillatory behavior, while the low Q-factor component consists largely of transients and oscillations that are not sustained. Fourth, variational mode decomposition is employed to decompose high and low resonance components into different intrinsic modes and provide representative features. Finally features are fed to five different types of machine learning based classifiers for the anomaly detection and severity rating of PD patients based on Hohen and Yahr (HY) scale. The effectiveness of this strategy is verified using a Physionet gait database consisting of 93 idiopathic PD patients and 73 age-matched asymptomatic HCs. When evaluated with 10-fold cross-validation method for early PD detection and severity rating, the highest classification accuracy is reported to be 98.20 % and 96.69 % , respectively, by using the support vector machine classifier. Compared with other state-of-the-art methods, the results demonstrate superior performance and support the validity of the proposed method.

NeuroDiag: Software for Automated Diagnosis of Parkinson's Disease Using Handwriting

OBJECTIVE

A change in handwriting is an early sign of Parkinson's disease (PD). However, significant inter-person differences in handwriting make it difficult to identify pathological handwriting, especially in the early stages. This paper reports the testing of NeuroDiag, a software-based medical device, for the automated detection of PD using handwriting patterns. NeuroDiag is designed to direct the user to perform six drawing and writing tasks, and the recordings are then uploaded onto a server for analysis. Kinematic information and pen pressure of handwriting are extracted and used as baseline parameters. NeuroDiag was trained based on 26 PD patients in the early stage of the disease and 26 matching controls.

METHODS

Twenty-three people with PD (PPD) in their early stage of the disease, 25 age-matched healthy controls (AMC), and 7 young healthy controls were recruited for this study. Under the supervision of a consultant neurologist or their nurse, the participants used NeuroDiag. The reports were generated in real-time and tabulated by an independent observer.

RESULTS

The participants were able to use NeuroDiag without assistance. The handwriting data was successfully uploaded to the server where the report was automatically generated in real-time. There were significant differences in the writing speed between PPD and AMC (P<0.001). NeuroDiag showed 86.96% sensitivity and 76.92% specificity in differentiating PPD from those without PD.

CONCLUSION

In this work, we tested the reliability of NeuroDiag in differentiating between PPD and AMC for real-time applications. The results show that NeuroDiag has the potential to be used to assist neurologists and for telehealth applications. Clinical and Translational Impact Statement - This pre-clinical study shows the feasibility of developing a community-wide screening program for Parkinson's disease using automated handwriting analysis software, NeuroDiag.

Vertical Ground Reaction Forces in Parkinson's Disease: A Speed-Matched Comparative Analysis with Healthy Subjects

This study aimed to investigate and compare the vertical Ground Reaction Forces (vGRFs) of patients with Parkinson's Disease (PwPD) and healthy subjects (HS) when the confounding effect of walking speed was absent. Therefore, eighteen PwPD and eighteen age- and linear walking speed-matched HS were recruited. Using plantar pressure insoles, participants walked along linear and curvilinear paths at self-selected speeds. Interestingly, PwPD exhibited similar walking speed to HS during curvilinear trajectories (p = 0.48) and similar vGRF during both linear and curvilinear paths. In both groups, vGRF at initial contact and terminal stance was higher during linear walking, while vGRF at mid-stance was higher in curvilinear trajectories. Similarly, the time to peak vGRF at each phase showed no significant group differences. The vGRF timing variability was different between the two groups, particularly at terminal stance (p < 0.001). In conclusion, PwPD and HS showed similar modifications in vGRF and a similar reduction in gait speed during curvilinear paths when matched for linear walking speed. This emphasized the importance of considering walking speed when assessing gait dynamics in PwPD. This study also suggests the possibility of the variability of specific temporal measures in differentiating the gait patterns of PwPD versus those of HS, even in the early stages of the disease.

Effects of wearable visual cueing on gait pattern and stability in patients with Parkinson’s disease

The present study examined the effects of wearable visual cues, provided by a wearable laser device, on the gait pattern and stability in patients with Parkinson’s disease (PD). In total, 18 patients with a clinical diagnosis of idiopathic PD (Hoehn and Yahr stage II-III) and 18 healthy controls were included. The main outcome measures included spatiotemporal parameters, sagittal plane kinematic parameters of joints in lower limbs, and dynamic center of pressure (COP) parameters. Significant intra-group improvement in gait parameters was observed in PD patients. Compared with that at baseline, the gait pattern improved in PD patients under the cued condition, with longer stride length and higher toe clearance, as well as shortening of double stance phase, especially the stride length, double stance phase and toe clearance were not significantly different between cued condition and healthy control groups. In kinematics, the ankle peak dorsiflexion in swing phase and the hip range of motion (ROM) in gait cycle was significantly improved in PD patients with visual cues and close to healthy controls. Decreased anteroposterior (AP) position of COP improved gait stability in patients with PD under the cued condition. Multiple linear regression analysis showed that the AP position has a negative correlation with ankle peak dorsiflexion in swing phase. Pearson’s correlation coefficients showed that the minimum toe clearance (Mini TC) was positively correlated with the ankle peak dorsiflexion in swing phase. The immediate effect of wearable visual cues improved the gait pattern and stability in PD patients, suggesting that it may be effective when applied as an alternative technique in rehabilitation training for PD patients.

A spinal synergy of excitatory and inhibitory neurons coordinates ipsilateral body movements

Innate and goal-directed movements require a high-degree of trunk and appendicular muscle coordination to preserve body stability while ensuring the correct execution of the motor action. The spinal neural circuits underlying motor execution and postural stability are finely modulated by propriospinal, sensory and descending feedback, yet how distinct spinal neuron populations cooperate to control body stability and limb coordination remains unclear. Here, we identified a spinal microcircuit composed of V2 lineage-derived excitatory (V2a) and inhibitory (V2b) neurons that together coordinate ipsilateral body movements during locomotion. Inactivation of the entire V2 neuron lineage does not impair intralimb coordination but destabilizes body balance and ipsilateral limb coupling, causing mice to adopt a compensatory festinating gait and be unable to execute skilled locomotor tasks. Taken together our data suggest that during locomotion the excitatory V2a and inhibitory V2b neurons act antagonistically to control intralimb coordination, and synergistically to coordinate forelimb and hindlimb movements. Thus, we suggest a new circuit architecture, by which neurons with distinct neurotransmitter identities employ a dual-mode of operation, exerting either synergistic or opposing functions to control different facets of the same motor behavior.

A wearable motion capture suit and machine learning predict disease progression in Friedreich's ataxia

Friedreich's ataxia (FA) is caused by a variant of the Frataxin (FXN) gene, leading to its downregulation and progressively impaired cardiac and neurological function. Current gold-standard clinical scales use simplistic behavioral assessments, which require 18- to 24-month-long trials to determine if therapies are beneficial. Here we captured full-body movement kinematics from patients with wearable sensors, enabling us to define digital behavioral features based on the data from nine FA patients (six females and three males) and nine age- and sex-matched controls, who performed the 8-m walk (8-MW) test and 9-hole peg test (9 HPT). We used machine learning to combine these features to longitudinally predict the clinical scores of the FA patients, and compared these with two standard clinical assessments, Spinocerebellar Ataxia Functional Index (SCAFI) and Scale for the Assessment and Rating of Ataxia (SARA). The digital behavioral features enabled longitudinal predictions of personal SARA and SCAFI scores 9 months into the future and were 1.7 and 4 times more precise than longitudinal predictions using only SARA and SCAFI scores, respectively. Unlike the two clinical scales, the digital behavioral features accurately predicted FXN gene expression levels for each FA patient in a cross-sectional manner. Our work demonstrates how data-derived wearable biomarkers can track personal disease trajectories and indicates the potential of such biomarkers for substantially reducing the duration or size of clinical trials testing disease-modifying therapies and for enabling behavioral transcriptomics.

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

Background

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

Objective

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

Materials and methods

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

Results

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

Conclusion

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

Split-Belt Adaptation and Savings in People With Parkinson Disease

BACKGROUND AND PURPOSE

Upper extremity studies suggest that implicit adaptation is less impaired than explicit learning in persons with Parkinson disease (PD). Little work has explored implicit locomotor adaptation and savings in this population, yet implicit locomotor learning is critical for everyday function. This cross-sectional study examined adaptation and savings in individuals with PD during split-belt treadmill walking.

METHODS

Fourteen participants completed the following treadmill protocol: Baseline (6 minutes belts tied), Adaptation (10 minutes split), Washout (10 minutes tied), and Readaptation (10 minutes split). Step length and step symmetry index (SSI) were calculated to determine magnitude and rate of adaptation and savings. Rate was calculated as strides to reach SSI plateau during Adaptation and Readaptation.

RESULTS

During Early Adaptation and Early Readaptation, SSI was perturbed from Baseline ( P < 0.001 and P = 0.002, respectively). Less perturbation in Early Readaptation ( P < 0.001) demonstrated savings. In Late Adaptation and Late Readaptation, participants returned to Baseline symmetry ( P = 0.026 and P = 0.022, respectively, with adjusted level of significance = 0.007). Adaptation was also seen in reverse asymmetry observed in Early Washout ( P = 0.003 vs Baseline). Readaptation rate was faster than in Adaptation ( P = 0.015), demonstrating savings.

DISCUSSION AND CONCLUSIONS

Individuals with PD showed locomotor adaptation in an implicit sensorimotor adaptation task. They also demonstrated savings, with less perturbation and faster adaptation during the second split-belt exposure. However, performance was variable; some individuals showed minimal adaptation. Variations in learning, savings, and clinical presentation highlight the need to further explore characteristics of individuals with PD most likely to benefit from adaptation-based locomotor training.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A395 ).

A Study on Lower Limb Asymmetries in Parkinson’s Disease during Gait Assessed through Kinematic-Derived Parameters

Unilaterality of motor symptoms is a distinctive feature of Parkinson’s Disease (PD) and represents an important co-factor involved in motor deficits and limitations of functional abilities including postural instability and asymmetrical gait. In recent times, an increasing number of studies focused on the characterization of such alterations, which have been associated with increased metabolic cost and risk of falls and may severely compromise their quality of life. Although a large number of studies investigated the gait alterations in people with PD (pwPD), few focused on kinematic parameters and even less investigated interlimb asymmetry under a kinematic point of view. This retrospective study aimed to characterize such aspects in a cohort of 61 pwPD (aged 68.9 ± 9.3 years) and 47 unaffected individuals age- and sex-matched (66.0 ± 8.3 years), by means of computerized 3D gait analysis performed using an optical motion-capture system. The angular trends at hip, knee and ankle joints of pwPD during the gait cycle were extracted and compared with those of unaffected individuals on a point-by-point basis. Interlimb asymmetry was assessed using angle–angle diagrams (cyclograms); in particular, we analyzed area, orientation, trend symmetry and range offset. The results showed that pwPD are characterized by a modified gait pattern particularly at the terminal stance/early swing phase of the gait cycle. Significant alterations of interlimb coordination were detected at the ankle joint (cyclogram orientation and trend symmetry) and at the hip joint (range offset). Such findings might be useful in clinical routine to characterize asymmetry during gait and thus support physicians in the early diagnosis and in the evaluation of the disease progression.

Relevance of Sociodemographics and Clinical Tests in Single- and Dual-Task Conditions as Gait Speed Predictors of Parkinson’s Disease

This cross-sectional study aimed to identify the patient characteristics and clinical test results that predict the functional gait speed of people with Parkinson’s disease (PD). The impact of dual tasks on gait in Parkinson’s disease (PD) reveals a lack of automaticity and increased cognitive demands. We explored which characteristics explained walking speed with and without dual-task interference and if they reflected the cognitive demands of the task. The preferred gait speed, stride length, and cadence were measured in individuals with PD through five conditions: single-task (ST) and visual, verbal, auditory, and motor dual-tasks (DTs). Sociodemographic and disease characteristics and the results from clinical tests such as the Dynamic Parkinson’s Disease Gait Scale (DYPAGS), Frontal Assessment Battery (FAB), and Parkinson’s Disease Questionnaire-39 (PDQ-39), among others, were also recorded. Two models of multiple regression analysis were used to explore the predictive value of outcomes concerning speed. In Model I, clinical results were included, and in Model II, spatiotemporal variables were added to the significant predictors of Model I. Forty PD patients (aged 66.72 (7.5) years) completed the assessments. All the models generated were significant (p < 0.01). Models I and II accounted for 47% and 93% of the variance, respectively, in the single-task condition. A patient’s gender, prescribed medication (drugs), academic level, and Hoehn and Yahr (H&Y) stage, along with the FAB, DYPAGS, and PDQ-39 scores, were significant predictors of gait speed in Model I for the ST and DT conditions. In Model II, the H&Y stage and prescribed medication (drugs), along with the FAB and DYPAGS scores, remained significant predictors. This research found that sociodemographics, the patient’s stage disease, and their clinical test results contribute to their walking speed, highlighting the multifactorial nature of gait in demanding environments.

Assessment of the Kinematic Adaptations in Parkinson's Disease Using the Gait Profile Score: Influences of Trunk Posture, a Pilot Study

INTRODUCTION

Postural abnormalities are common in patients with Parkinson's disease (PD) and lead to gait abnormalities. Relationships between changes in the trunk posture of PD patients and gait profile score (GPS) and gait spatiotemporal parameters are poorly investigated. The aim of the current study was to investigate the relationships between trunk posture, GPS, and gait spatiotemporal parameters, in patients with PD.

MATERIALS AND METHODS

Twenty-three people with PD and nineteen age-matched healthy people participated in this study. A 3D gait kinematical analysis was applied to all participants using the Plug-In Gait Full BodyTM tool. Trunk and limb kinematics patterns and gait spatio-temporal parameters of patients with PD and the control group were compared. Additionally, correlations between trunk kinematics patterns, gait spatio-temporal parameters, and GPS of the PD group were tested.

RESULTS

Cadence, opposite foot off, step time, single support, double support, foot off, gait speed, trunk kinematics, and GPS showed significant differences between the two groups (p ≤ 0.05). Posture of the trunk during gait was not related to the spatio-temporal parameters and gait profile score in the PD group. The trunk flexor pattern influenced GPS domains, mainly of the ankle and the knee.

DISCUSSION AND CONCLUSIONS

Flexed posture of the trunk in patients with PD seems to influence both ankle and knee movement patterns during the gait. The GPS analysis provided direct and simplified kinematic information for the PD group. These results may have implications for understanding the importance of considering the positioning of the trunk during gait.

The effects of levodopa in the spatiotemporal gait parameters are mediated by self-selected gait speed in Parkinson's disease

In individuals with Parkinson's disease (PD), the medication induces different and inconsistent results in the spatiotemporal parameters of gait, making it difficult to understand its effects on gait. As spatiotemporal gait parameters have been reported to be affected by gait speed, it is essential to consider the gait speed when studying walking biomechanics to interpret the results better when comparing the gait pattern of different conditions. Since the medication alters the self-selected gait speed of individuals with PD, this study analysed whether the change in gait speed can explain the selective effects of l-DOPA on the spatiotemporal parameters of gait in individuals with PD. We analysed the spatiotemporal gait parameters at the self-selected speed of 22 individuals with PD under ON and OFF states of l-DOPA medication. Bayesian mediation analysis evaluated which gait variables were affected by the medication state and checked if those effects were mediated by speed changes induced by medication. The gait speed was significantly higher among ON compared with OFF medication. All the spatiotemporal parameters of the gait were mediated by speed, with proportions of mediation close to 1 (effect entirely explained by speed changes). Our results show that a change in gait speed better explains the changes in the spatiotemporal gait parameters than the ON-OFF phenomenon. As an implication for rehabilitation, our results suggest that it is possible to assess the effect of l-DOPA on improving motor symptoms related to gait disorders by measuring gait speed.

Analysis of the stance phase of the gait cycle in Parkinson's disease and its potency for Parkinson's disease discrimination

In this study, using vertical ground reaction force (VGRF) data and focusing on the stance phase of the gait cycle, the effect of Parkinson's disease (PD) on gait was investigated. The used dataset consisted of 93 PD and 72 healthy individuals. Multiple comparisons correction ANOVA test and student t-test were used for statistical analyses. Results showed that a longer stance duration with a larger VGRF peak value (p < 0.05) was observed for PD patients during the stance phase. In addition, the VGRF peak value was delayed and blunted in PD cases compared with healthy individuals. These results indicated more time and effort for PD patients for posture stabilization during the stance phase. The time delay for different locations of the foot sole to contact the ground during the stance phase indicated that PD patients might use a different strategy for maintaining their body stability compared with healthy individuals. Although the VGRF time-domain pattern during the stance phase in PD was similar to healthy conditions, its local characteristics like duration and peak value differed significantly. The classification analysis based on the VGRF time-domain extracted features during the stance phase obtained PD recognition with accuracy, sensitivity and specificity of 90.82%, 88.63% and 82.56%, respectively.

A Systematic Review of Machine Learning Based Gait characteristics in Parkinson's disease

OBJECTIVE

Parkinson's disease is a pervasive neuro disorder that affects people's quality of life throughout the world. The unsatisfactory results of clinical rating scales open the door for more research. PD treatment using current biomarkers seems a difficult task. So automatic evaluation at an early stage may enhance the quality and time-period of life.

METHODS

Grading of Recommendations Assessment, Development, and Evaluation (GRADE) and Population, intervention, comparison, and outcome (PICO) search methodology schemes are followed to search the data and eligible studies for this survey. Approximate 1500 articles were extracted using related search strings. After the stepwise mapping and elimination of studies, 94 papers are found suitable for the present review.

RESULTS

After the quality assessment of extracted studies, nine inhibitors are identified to analyze people's gait with Parkinson's disease, where four are critical. This review also differentiates the various machine learning classification techniques with their PD analysis characteristics in previous studies. The extracted research gaps are described as future perspectives. Results can help practitioners understand the PD gait as a valuable biomarker for detection, quantification, and classification.

CONCLUSION

Due to less cost and easy recording of gait, gait-based techniques are becoming popular in PD detection. By encapsulating the gait-based studies, it gives an in-depth knowledge of PD, different measures that affect gait detection and classification.

Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard: A Pilot Study

Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking using a gold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people.

DIFFERENCES IN GAIT PATTERNS BETWEEN PATIENTS WITH SWEDD AND NORMAL SUBJECTS

Whether gait assessment can distinguish patients with scans without evidence of dopaminergic deficit (SWEDD) from normal individuals remains unclear. In the present study, the gait patterns in SWEDD patients and normal individuals were investigated. 10 patients with SWEDD and 10 healthy normal subjects were recruited. A GaitRite linear walkway was used to quantitatively assess level walking. Eight spatiotemporal variables were selected for comparison between two groups. For statistical analysis, independent sample [Formula: see text]-tests were performed to compare between normal individuals and SWEDD patients. Compared with normal individuals, SWEDD patients exhibited a shorter step length ([Formula: see text]). In addition, SWEDD patients showed a wider distribution of heel-to-heel (HH) base of support compared with normal individuals ([Formula: see text]). SWEDD patients had a tendency to deteriorated gait speed ([Formula: see text]) and double-limb support cycle ([Formula: see text]). These results indicate that SWEDD patients adopted a walking strategy that utilized increased step length and width to compensate for deteriorated dynamic balance. These results indicate that quantitative gait assessment could be useful for more accurate diagnosis of SWEDD patients. In addition, understanding clinical features of SWEDD patients might contribute to efficient interventions.

A novel instrumented walker for individualized visual cue setting for gait training in patients with Parkinson's disease

Patients with Parkinson's disease suffer from gait disturbances, such as a shuffling and festinating gait, which reduces their quality of life. To circumvent this problem, external visual cues may be applied in gait training to maintain the integrity of motor function. However, conventional training methods, such as transverse lines stuck on the ground, are difficult to adjust and adapt to personalized gait ability. This study proposes a convenient instrumented wheel walker that provides gap adjustable visual cues and selectable projection modes onto the ground with and without motion relative to the user. Ten subjects with Parkinson's disease were recruited, and the efficacy of the proposed device for their gait training was assessed. We demonstrated the applicability of our device to address personalized demands in gait guidance. With a personalized setting for patients with Parkinson's disease, a significantly lengthened stride length may be achieved.

Interference of functional dual-tasks on gait in untrained people with Parkinson's disease and healthy controls: a cross-sectional study

Background

In Parkinson’s disease (PD) population, performing secondary tasks while walking further deteriorates gait and restrict mobility in functional contexts of daily life. This study (1) analyzed the interference of functional cognitive and motor secondary task on untrained people with PD and (2) compared their walking with healthy subjects.

Methods

Forty people with PD (aged 66.72 [7.5] years, Hoehn and Yahr stage I-II-III, on-medication) composed the PD group (PDG) and 43 participants (aged 66.60 [8.75] years) formed the group of healthy counterparts (HG). Gait was evaluated through spatiotemporal, kinematic and kinetic outcomes in five conditions: single task (ST) and visual, verbal, auditory and motor dual-task (DT).

Results

The velocity, stride length, and braking force performance of both groups was statistically higher in the ST condition than in verbal, auditory and motor DT (p < .05), and inferior in double support time and midstance force (p < .05). The same pattern was observed when compared the ST and visual DT condition, where participants showed a significantly higher stride length, double support time and braking force in the ST (p < .05). In addition, the PDG exhibited a significant shorter double support time and midstance force, and showed a higher braking force in the visual DT than in the verbal DT (p < .05). Similarly, the PDG showed a wider stride in the visual DT than in the motor DT condition (p < .05). PDG participants had a significantly lower performance than the HG in all the variables analyzed except for the maximum hip extension in the stance phase (p > .05). Conclusions: In untrained participants with PD, verbal and motor secondary tasks affect gait significantly, while auditory and visual tasks interfere to a lesser extent. Untrained people with PD have a poorer gait performance than their healthy counterparts, but in different grades according to the analyzed variables.

Trial registration

The data in this paper are part of a single-blind, randomized, controlled trial and correspond to the evaluations performed before a physical rehabilitation program, retrospectively registered with the number at clinicaltrial.govNCT04038866.

Mediolateral stability index as a biomarker for Parkinson's disease progression: A graph connectivity based approach

This paper presents a novel metric to serve as a bio-marker for understanding and detecting progression of Parkinson's disease (PD). Proposed metric, termed as `Mediolateral Stability (MLS) index' has been derived from processing insole gait data using graph connectivity analysis. The proposed metric focuses on variability of mediolateral pressure in foot during gait progression. Vertical Ground Reaction Force (VGRF) and stride time information derived from a wearable insole for PD subjects as well as healthy controls are processed to create a connectivity graph. The insole contains eight pressure sensitive sensors for each foot and these sensors serve as the nodes of the connectivity graph. The proposed MLS index shows significant difference (p <; 0.05) in between control and PD groups and also in between progressive stages of PD, such as mild and moderate PD groups with p <; 0.05. Proposed graph connectivity based feature can act as a bio marker to correctly classify PD, identify early onset of PD and trace changes due to disease progression and can also provide information about dynamic pressure distribution during gait.

Gait pattern analysis and clinical subgroup identification: a retrospective observational study

To identify basic gait features and abnormal gait patterns that are common to different neurological or musculoskeletal conditions, such as cerebral stroke, Parkinsonian disorders, radiculopathy, and musculoskeletal pain.In this retrospective study, temporal-spatial, kinematic, and kinetic gait parameters were analyzed in 424 patients with hemiplegia after stroke, 205 patients with Parkinsonian disorders, 216 patients with radiculopathy, 167 patients with musculoskeletal pain, and 316 normal controls (total, 1328 subjects). We assessed differences according to the condition and used a community detection algorithm to identify subgroups within each condition. Additionally, we developed a prediction model for subgroup classification according to gait speed and maximal hip extension in the stance phase.The main findings can be summarized as follows. First, there was an asymmetric decrease of the knee/ankle flexion angles in hemiplegia and a marked reduction of the hip/knee range of motion with increased moment in Parkinsonian disorders. Second, three abnormal gait patterns, including fast gait speed with adequate maximal hip extension, fast gait speed with inadequate maximal hip extension, and slow gait speed, were found throughout the conditions examined. Third, our simple prediction model based on gait speed and maximal hip extension angle was characterized by a high degree of accuracy in predicting subgroups within a condition.Our findings suggest the existence of specific gait patterns within and across conditions. Our novel subgrouping algorithm can be employed in routine clinical settings to classify abnormal gait patterns in various neurological disorders and guide the therapeutic approach and monitoring.

Comparison of predicted kinetic variables between Parkinson's disease patients and healthy age-matched control using a depth sensor-driven full-body musculoskeletal model

BACKGROUND

Abnormalities in gait kinetics in patients with Parkinson's disease (PD) who have suffer from gait impairment have been noted using a conventional inverse dynamic analysis derived by marker-based motion capture system and force plate, which are typically mounted in the laboratory floor. Despite the high accuracy of this approach in tracking markers' trajectories and acquiring ground reaction forces (GRFs), its dependence on laboratory-mounted equipment restricts its potential use in wider variety of clinical applications.

RESEARCH QUESTION

Would a full-body musculoskeletal model driven by a single depth sensor data only produce comparable gait kinetic parameters, including GRFs and lower extremity joints moments, for elderly participants, both healthy and those diagnosed with PD?

METHODS

Nine patients diagnosed with PD and 11 healthy age-matched control participants performed three over-ground gait trials. Full-body kinematic data were collected using a depth sensor and a musculoskeletal model have been constructed using AnyBody musculoskeletal modeling system to predict the three-dimensional GRFs and lower extremity joint moments. Predicted kinetic parameters for both PD and control groups were compared during the braking and propulsive phases of the gait cycle. In addition, ensemble curve analysis with 90% confidence intervals were constructed to compare between group differences across the stance phase of the gait cycle.

RESULTS

The findings of this study showed that the PD exhibited a significantly lower braking peak vertical GRF and propulsion peak horizontal GRF while no significant between-group differences were found in peak lower extremity joint moments. However, the PD showed significant alterations in lower extremity joint moments during the early and late phases of stance, which indicate a difference in ambulation strategy.

SIGNIFICANCE

The proposed method adopting full-body musculoskeletal model driven by a depth sensor data proves that it has the potential to be a portable and cost-effective gait analysis tool in the clinical setting.

A review of gait disorders in the elderly and neurological patients for robot-assisted training

Purpose: Ambulation is an important objective for people with pathological gaits. Exoskeleton robots can assist these people to complete their activities of daily living. There are exoskeletons that have been presented in literature to assist the elderly and other pathological gait users. This article presents a review of the degree of support required in the elderly and neurological gait disorders found in the human population. This will help to advance the design of robot-assisted devices based on the needs of the end users.Methods: The articles included in this review are collected from different databases including Science Direct, Springer Link, Web of Science, Medline and PubMed and with the purpose to investigate the gait parameters of elderly and neurological patients. Studies were included after considering the full texts and only those which focus on spatiotemporal, kinematic and kinetic gait parameters were selected as they are most relevant to the scope of this review. A systematic review and meta-analysis were conducted.Results: The meta-analysis report on the spatiotemporal, kinematic and kinetic gait parameters of elderly and neurological patients revealed a significant difference based on the type and level of impairment. Healthy elderly population showed deviations in the gait parameters due to age, however, significant difference is observed in the gait parameters of the neurological patients.Conclusion: A level of agreement was observed in most of the studies however the review also noticed some controversies among different studies in the same group. The review on the spatiotemporal, kinematics and kinetic gait parameters will provide a summary of the fundamental needs of the users for the future design and development of robotic assistive devices.Implications for rehabilitationThe support requirements provide the foundation for designing assistive devices.The findings will be crucial in defining the design criteria for robot assistive devices.

COMPARISON OF SPATIO-TEMPORAL GAIT VARIABLES IN PATIENTS WITH PARKINSON’S DISEASE AND SWEDD

Scans without evidence of dopaminergic deficit (SWEDD) refers to patients with a normal dopamine transporter scan among patients clinically diagnosed with Parkinson’s disease (PD). It is essential to differentiate SWEDD from PD in order to avoid costly and inappropriate treatments. We investigated differences of gait pattern in PD patients and SWEDD patients. Twelve patients with SWEDD and 8 patients with PD participated in the gait test. All participants were instructed to walk along the GaitRite linear walkway at comfortable velocity. As outcome measure, various spatio-temporal gait variables were derived from commercial GaitRite software. Independent sample [Formula: see text]-tests were evaluated to investigate statistical differences between SWEDD patients and PD patients. SWEDD patients walked more quickly with longer step length compared to PD patients ([Formula: see text]). PD patients exhibited a longer stance phase with increased double-limb support period than SWEDD patients ([Formula: see text]). Step time difference of SWEDD patients was smaller than that of PD patients ([Formula: see text]). These findings suggest quantitative gait variables could be helpful for the identification of potential SWEDD patients. Also, understanding of gait strategies of SWEDD patients might be contributed to their effective medications and treatments.

Gait dynamics in Pisa syndrome and Camptocormia: The role of stride length and hip kinematics

This is an observational cross-sectional study evaluating gait dynamics in patients with Parkinson's Disease (PD) and severe postural deformities, PD without axial deviations and healthy subjects. Ten PS individuals with Pisa syndrome (PS) and nine subjects with Camptocormia (CC) performed 3-D Gait Analysis and were evaluated with walking and balance scales. Correlations with clinical and functional scales were investigated. Spatio-temporal and kinematic data were compared to ten PD subjects without postural deformities (PP) and ten healthy matched individuals (CG). Data obtained showed decreased walking velocity, stride and step length in PP, PS and CC groups compared to controls. The correlation analysis showed that stride and step length were associated with reduced functional abilities and disease severity in PS and CC groups. Kinematic data revealed marked reduction in range of movements (ROMs) at all lower-extremity joints in PS group. While, in CC group the main differences were pronounced in hip and knee joints. PS and CC groups presented a more pronounced reduction in hip articular excursion compared to PP subjects, revealing an increased hip flexion pattern during gait cycle. Moreover, the increased hip and knee flexion pattern adversely affected functional performance during walking tests. Results obtained provide evidence that step length, along with stride length, can be proposed as simple and clear indicators of disease severity and reduced functional abilities. The reduction of ROMs at hip joint represented an important mechanism contributing to decreased walking velocity, balance impairment and reduced gait performance in PD patients with postural deformities.

Treadmill training with additional body load: Effects on the gait of people with Parkinson's disease

Aim:

The aim of this study was to assess the effects of treadmill walking training with additional body load on the gait of people with moderate Parkinson's disease.

Methods:

Nine people with Parkinson's disease (Hoehn and Yahr Scale 2–3) and gait disturbance participated in this study. This study was an A1–B–A2 single-case. Phases A1 and A2 included 6 weeks of gait training on a treadmill with a 10% increase of normal body mass. Phase B included 6 weeks of conventional physical therapy (control condition). Measurements included ground reaction forces, spatiotemporal and kinematic variables during walking on the ground at baseline and after each phase.

Findings:

A significant increase in propulsive forces, stride length, speed, and maximum hip extension during stance were observed after the training programme. No changes in joint range of motion of ankle, knee, and hip were observed.

Conclusions:

Treadmill training with additional body load was associated with an improvement in important variables for the maintenance of a functional gait, and it is a promising alternative to optimise the rehabilitation process together with conventional physical therapy. However, further studies are needed.

Vertical ground reaction force marker for Parkinson's disease

Parkinson’s disease (PD) patients regularly exhibit abnormal gait patterns. Automated differentiation of abnormal gait from normal gait can serve as a potential tool for early diagnosis as well as monitoring the effect of PD treatment. The aim of current study is to differentiate PD patients from healthy controls, on the basis of features derived from plantar vertical ground reaction force (VGRF) data during walking at normal pace. The current work presents a comprehensive study highlighting the efficacy of different machine learning classifiers towards devising an accurate prediction system. Selection of meaningful feature based on sequential forward feature selection, the swing time, stride time variability, and center of pressure features facilitated successful classification of control and PD gaits. Support Vector Machine (SVM), K-nearest neighbor (KNN), random forest, and decision trees classifiers were used to build the prediction model. We found that SVM with cubic kernel outperformed other classifiers with an accuracy of 93.6%, the sensitivity of 93.1%, and specificity of 94.1%. In comparison to other studies, utilizing same dataset, our designed prediction system improved the classification performance by approximately 10%. The results of the current study underscore the ability of the VGRF data obtained non-invasively from wearable devices, in combination with a SVM classifier trained on meticulously selected features, as a tool for diagnosis of PD and monitoring effectiveness of therapy post pathology.

Mechanical Energy Recovery during Walking in Patients with Parkinson Disease

The mechanisms of mechanical energy recovery during gait have been thoroughly investigated in healthy subjects, but never described in patients with Parkinson disease (PD). The aim of this study was to investigate whether such mechanisms are preserved in PD patients despite an altered pattern of locomotion. We consecutively enrolled 23 PD patients (mean age 64±9 years) with bilateral symptoms (H&Y ≥II) if able to walk unassisted in medication-off condition (overnight suspension of all dopaminergic drugs). Ten healthy subjects (mean age 62±3 years) walked both at their 'preferred' and 'slow' speeds, to match the whole range of PD velocities. Kinematic data were recorded by means of an optoelectronic motion analyzer. For each stride we computed spatio-temporal parameters, time-course and range of motion (ROM) of hip, knee and ankle joint angles. We also measured kinetic (Wk), potential (Wp), total (WtotCM) energy variations and the energy recovery index (ER). Along with PD progression, we found a significant correlation of WtotCM and Wp with knee ROM and in particular with knee extension in terminal stance phase. Wk and ER were instead mainly related to gait velocity. In PD subjects, the reduction of knee ROM significantly diminished both Wp and WtotCM. Rehabilitation treatments should possibly integrate passive and active mobilization of knee to prevent a reduction of gait-related energetic components.

Detrended fluctuation analysis and adaptive fractal analysis of stride time data in Parkinson's disease: stitching together short gait trials

Variability indicates motor control disturbances and is suitable to identify gait pathologies. It can be quantified by linear parameters (amplitude estimators) and more sophisticated nonlinear methods (structural information). Detrended Fluctuation Analysis (DFA) is one method to measure structural information, e.g., from stride time series. Recently, an improved method, Adaptive Fractal Analysis (AFA), has been proposed. This method has not been applied to gait data before. Fractal scaling methods (FS) require long stride-to-stride data to obtain valid results. However, in clinical studies, it is not usual to measure a large number of strides (e.g., [Formula: see text][Formula: see text] strides). Amongst others, clinical gait analysis is limited due to short walkways, thus, FS seem to be inapplicable. The purpose of the present study was to evaluate FS under clinical conditions. Stride time data of five self-paced walking trials ([Formula: see text] strides each) of subjects with PD and a healthy control group (CG) was measured. To generate longer time series, stride time sequences were stitched together. The coefficient of variation (CV), fractal scaling exponents [Formula: see text] (DFA) and [Formula: see text] (AFA) were calculated. Two surrogate tests were performed: A) the whole time series was randomly shuffled; B) the single trials were randomly shuffled separately and afterwards stitched together. CV did not discriminate between PD and CG. However, significant differences between PD and CG were found concerning [Formula: see text] and [Formula: see text]. Surrogate version B yielded a higher mean squared error and empirical quantiles than version A. Hence, we conclude that the stitching procedure creates an artificial structure resulting in an overestimation of true [Formula: see text]. The method of stitching together sections of gait seems to be appropriate in order to distinguish between PD and CG with FS. It provides an approach to integrate FS as standard in clinical gait analysis and to overcome limitations such as short walkways.

Parkinson disease and exercise

Parkinson disease (PD) is a progressive, neurodegenerative movement disorder. PD was originally attributed to neuronal loss within the substantia nigra pars compacta, and a concomitant loss of dopamine. PD is now thought to be a multisystem disorder that involves not only the dopaminergic system, but other neurotransmitter systems whose role may become more prominent as the disease progresses (189). PD is characterized by four cardinal symptoms, resting tremor, rigidity, bradykinesia, and postural instability, all of which are motor. However, PD also may include any combination of a myriad of nonmotor symptoms (195). Both motor and nonmotor symptoms may impact the ability of those with PD to participate in exercise and/or impact the effects of that exercise on those with PD. This article provides a comprehensive overview of PD, its symptoms and progression, and current treatments for PD. Among these treatments, exercise is currently at the forefront. People with PD retain the ability to participate in many forms of exercise and generally respond to exercise interventions similarly to age-matched subjects without PD. As such, exercise is currently an area receiving substantial research attention as investigators seek interventions that may modify the progression of the disease, perhaps through neuroprotective mechanisms.

Comparing adaptive algorithms to measure temporal gait parameters using lower body mounted inertial sensors

The purpose of this research was to compare different adaptive algorithms in terms of their ability to determine temporal gait parameters based on data acquired from inertial measurement units (IMUs). Eight subjects performed 25 walking trials over a force plate under five different conditions; normal, fast, slow, simulated stiff ankle and simulated stiff knee walking. Data from IMUs worn on the shanks and on the feet were used to identify temporal gait features using three different adaptive algorithms (Green, Selles & Sabatini). Each method's ability to estimate temporal events was compared to the gold standard force plate method for stance time (Greene, r= .990, Selles, r= 0.865, Sabatini, r= 0.980) and double support time (Greene, r= .837, Selles, r= .583, Sabatini, r= .745). The Greene method of estimating gait events from inertial sensor data resulted in the most accurate stance and double support times.

A comprehensive assessment of gait accelerometry signals in time, frequency and time-frequency domains

Gait accelerometry is a promising tool to assess human walking and reveal deteriorating gait characteristics in patients and can be a rich source of clinically relevant information about functional declines in older adults. Therefore, in this paper, we present a comprehensive set of signal features that may be used to extract clinically valuable information from gait accelerometry signals. To achieve our goal, we collected tri-axial gait accelerometry signals from 35 adults 65 years of age and older. Fourteen subjects were healthy controls, 10 participants were diagnosed with Parkinson's disease, and 11 participants were diagnosed with peripheral neuropathy. The data were collected while the participants walked on a treadmill at a preferred walking speed. Accelerometer signal features in time, frequency and time-frequency domains were extracted. The results of our analysis showed that some of the extracted features were able to differentiate between healthy and clinical populations. Signal features in all three domains were able to emphasize variability among different groups, and also revealed valuable information about variability of the signals between anterior-posterior, mediolateral, and vertical directions within subjects. The current results imply that the proposed signal features can be valuable tools for the analysis of gait accelerometry data and should be utilized in future studies.

The influence of dopaminergic striatal innervation on upper limb locomotor synergies

To determine the role of striatal dopaminergic innervation on upper limb synergies during walking, we measured arm kinematics in 13 subjects with Parkinson disease. Patients were recruited according to several inclusion criteria to represent the best possible in vivo model of dopaminergic denervation. Of relevance, we included only subjects with normal spatio-temporal parameters of the stride and gait speed to avoid an impairment of upper limbs locomotor synergies as a consequence of gait impairment per se. Dopaminergic innervation of the striatum was measured by FP-CIT and SPECT. All patients showed a reduction of gait-associated arms movement. No linear correlation was found between arm ROM reduction and contralateral dopaminergic putaminal innervation loss. Still, a partition analysis revealed a 80% chance of reduced arm ROM when putaminal dopamine content loss was >47%. A significant correlation was described between the asymmetry indices of the swinging of the two arms and dopaminergic striatal innervation. When arm ROM was reduced, we found a positive correlation between upper-lower limb phase shift modulation (at different gait velocities) and striatal dopaminergic innervation. These findings are preliminary evidence that dopaminergic striatal tone plays a modulatory role in upper-limb locomotor synergies and upper-lower limb coupling while walking at different velocities.

Factors related to functional independence in females with Parkinson's disease: a systematic review

Males and females may exhibit diverse expressions of Parkinson's disease (PD) as a result of biological and social differences. In general, a higher incidence of PD is found among males (RR=1.5) compared to females. However, rigidity, postural instability and levodopa-induced dyskinesia are more prevalent in females with PD. These fluctuations affect motor performance and impact functional ability. This systematic review suggests that there is minimal research literature with respect to females living with PD. Specifically, the influence of physical ability in females with PD is underemphasized, considering its contribution to functional daily living and quality of life. Three intervention and nine functional assessment studies met inclusion criteria (n=302; mean age=65.5±8.3 years, 44% female). Reports suggest that females with PD have different gait patterns compared to 'healthy' age-matched females and males with PD. Females with PD experience increased freezing of gait as compared to males with PD. Dynamic balance was reduced in females compared to males with PD. Differences in cardiorespiratory fitness compared to healthy age-matched females was inconclusive. Studies were rated to be of moderate quality (20±5.4/32) and future studies should focus on improving sex-matched recruitment, randomized group allocation, and blinding of evaluators to ensure unbiased results. Regardless, the specific impact of PD on females warrants further investigation.