Gait Kinematic Parameters in Parkinson's Disease: A Systematic Review

Clinical Assessment of Walking Capacity in People With Parkinson Disease: Are 2 Minutes Sufficient?

OBJECTIVE

Walking capacity progressively declines in people with Parkinson disease (PD), and assessment of walking is imperative for monitoring disease progression and evaluating intervention efficacy. The main aim of this study was to explore whether the 2-minute walk test (2MWT) could be substituted for the 6-minute walk test (6MWT) as a measure of walking capacity in people with PD. We also sought to investigate construct and known-groups validity of the 2MWT.

METHODS

A cross-sectional analysis based on data from the Supported Home Training in Everyday Life for Parkinson Disease trial was conducted in a hospital setting. Sixty-three people with idiopathic, mild to moderate PD (29 women; mean age = 69.2 years) were included. Spatiotemporal gait parameters during the 2MWT and the 6MWT were captured by wearable sensors. Linear regression was used to analyze the association between distances walked, whereas paired-samples t tests and repeated-measures analysis of variance were used to explore mean differences in gait parameters.

RESULTS

Distance walked over the 2MWT was very strongly associated with the 6MWT. Gait speed was higher during the shorter test, and several speed-related parameters significantly differed between the tests. There was a trend over the 6MWT, whereby participants performed better during the last 2 minutes of the test. Analyses revealed convergent, discriminant, and known-groups validity of the 2MWT.

CONCLUSION

These findings suggest that the 2MWT adequately captures walking capacity among people with mild to moderate PD and demonstrates robust convergent validity and ability to discriminate between people at different levels of disease severity.

IMPACT

The 2MWT is a sufficient and valid alternative for physical therapists who wish to assess walking capacity in people with mild to moderate PD.

Cortical Activation During Split-Belt Treadmill Walking in People With Parkinson's Disease and Healthy Controls

BackgroundPeople with Parkinson's disease (PwPD) have difficulty adapting their gait to asymmetrical conditions. Objective. We investigated cortical activity between 42 PwPD (HY 2-3) and 42 healthy controls using functional near-infrared spectroscopy during tied-belt (TB) and split-belt (SB) treadmill walking.MethodsOxygenated hemoglobin (HbO2) was measured in the prefrontal cortex, supplementary motor area (SMA), premotor cortex (PMC), and posterior parietal cortex (PPC) during 3 blocks of treadmill walking: (1) with the belts moving at the same speed (TB) and (2) when the speed of 1 side was reduced by 50% (SB; 2 blocks). The ability to adjust gait to asymmetric conditions was quantified by step length asymmetry and its variability.ResultsAdaptive gait was worse during the last 5 steps of SB versus TB in PwPD compared to controls. PwPD showed higher HbO2 in the PMC (P = .005) and PPC (P = .004) relative to controls, regardless of condition. However, an increase in HbO2 in the SMA during SB was shown relative to TB in PwPD, a change not observed in controls (group × condition interaction P = .048; pairwise post hoc P = .032). Interestingly, increased PPC activity in PwPD was associated with poorer adapted gait.ConclusionsBoth regular and adaptive gait required enhanced cortical processing in PwPD, as evidenced by the increased activation in the PMC and PPC. However, this heightened cortical activity did not correlate with a reduction in gait asymmetry, suggesting that these changes might be maladaptive. Instead, the elevated cortical activity may reflect the challenges PwPD face in adapting to asymmetrical walking conditions. Careful interpretation is warranted given the relatively small sample of mildly affected PwPD, limiting generalizability to the broader population and the measurement errors inherent to functional near-infrared spectroscopy .

Velocity ratings and perceptual qualities of electrotactile stimulation of the foot sole are impacted by direction, stimulus interval, and cutaneous saltation

Electrotactile stimulation is a çmethod of activating the tactile system by bypassing cutaneous mechanoreceptors and exciting the cutaneous afferent endings directly. This method is of interest for its potential in wearable tactile augmentation technologies. The generation of meaningful electrotactile sensation could benefit cases of peripheral neuropathy or prosthesis. There are limitations in our understanding of an electrotactile stimulations' capacity to represent tactile sensibilities, and its susceptibility to missense. The spatiotemporal parameters of an electrotactile sequence were varied. The present work extends the assessment of subjective evaluations of localization, velocity, and descriptive qualities. We applied electrotactile pulses at three sites on the foot sole, using three patterns across these sites: toward the heel or toes. We tested at three interstimulus intervals (100 ms, 160 ms, 220 ms). Faster sequences produced higher velocity ratings. Sequence direction across the foot sole impacted velocity ratings-with heel-to-toe sequences demonstrating a higher velocity rating than toe-to-heel sequences. During faster sequences with site repetition, cutaneous saltation is likely causing missense during localization. The spatiotemporal missense did not impact velocity ratings. This indicates that certain aspects of electrotactile sequence perception, such as velocity, are preserved through tactile illusions. These findings may be used to increase the resolution of stimulating grids.

Do the gait domains change in PD patients with freezing of gait during their 'interictal' period?

OBJECTIVES

Freezing of Gait (FOG) is one of the disabling symptoms in patients with Parkinson's Disease (PD). While it is difficult to early detect because of the sporadic occurrence of initial freezing events. Whether the characteristic of gait impairments in PD patients with FOG during the 'interictal' period is different from that in non-FOG patients is still unclear.

METHODS

The gait parameters were measured by wearable inertial sensors. Exploratory factor analysis was used to investigate the inherent structure of diverse univariate gait parameters, with the aim of identifying shared characteristics among the gait variables.

RESULTS

This cross-sectional study involved 68 controls and 245 PD patients (167 without FOG and 78 with FOG). The analysis yielded six distinct gait domains which were utilized to describe the impaired gait observed during the "interictal" period of FOG. Both PD-nFOG and PD-FOG groups exhibited significant impairments in the pace domain, kinematic domain, gait phase domain, and turning process domain compared to the healthy control. The gait phase domain was different in the PD-FOG group compared to the PD-nFOG group (p corrected = 0.004, Cohen's d = -0.46). And it was identified as independent risk factor for FOG (OR = 1.64, 95% CI = 1.05-2.55, p = 0.030), as well as other risk factors: gender (OR = 2.67, 95% CI = 1.19-5.99, p = 0.017), MDS-UPDRS IV score (OR = 1.23, 95% CI = 1.10-1.37, p < 0.001), and PIGD subscore (OR = 1.50, 95% CI = 1.30-1.73, p < 0.001). The model demonstrated a correct discrimination rate of 0.78 between PD-FOG and PD-nFOG, with an area under the receiver operating characteristic curve (AUC) of 0.87.

CONCLUSIONS

FOG was found to be associated with abnormal alterations in the gait phase domain during the interictal period. Models constructed using gait phase domain, PIGD subscore, gender, and severity of motor complications can better differentiate freezers from no-freezers during 'interictal' period.

Clinical and device-based predictors of improved experience of activities of daily living after a multidisciplinary inpatient treatment for people with Parkinson's disease: a cohort study

Background

The inpatient Parkinson's Disease Multimodal Complex Treatment (PD-MCT) is an important therapeutical approach to improving gait and activities of daily living (ADL) of people with PD (PwP). Wearable device-based parameters (DBP) are new options for specific gait analyses toward individualized treatments.

Objectives

We sought to identify predictors of perceived ADL benefit taking clinical scores and DBP into account. Additionally, we analyzed DBP and clinical scores before and after PD-MCT.

Design

Exploratory observational cohort study.

Methods

Clinical scores and DBP of 56 PwP (mean age: 66.3 years, median Hoehn and Yahr (H&Y) stage: 2.5) were examined at the start and the end of a 14-day inpatient PD-MCT in a German University Medical Center. Participants performed four straight walking tasks under single- and dual-task conditions for gait analyses. Additionally, clinical scores of motor and nonmotor functions and quality of life (QoL) were assessed. Using dichotomized data of change in Movement Disorders Society Unified Parkinson's Disease Rating Scale Part II (MDS-UPDRS II) as a dependent variable and clinical and DBP as independent variables, a binomial logistic regression model was implemented.

Results

Young age, high perceived ADL impairment at baseline, high dexterity skills, and a steady gait were significant predictors of ADL benefit after PD-MCT. DBP like gait speed, number of steps, step time, stance time, and double limb support time were improved after PD-MCT. In addition, motor functions (e.g., MDS-UPDRS III and IV), QoL, perceived ADL (MDS-UPDRS II), and experience of nonmotor functions (MDS-UPDRS I) improved significantly.

Conclusion

The logistic regression model identified a group of PwP who had the most probable perceived ADL benefit after PD-MCT. Additionally, gait improved toward a faster and more dynamic gait. Using wearable technology in context of PD-MCT is promising to offer more personalized therapeutical concepts.

Trial registration

German Clinical Trial Register, https://drks.de; DRKS00020948 number, 30 March 2020, retrospectively registered.

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.

Concurrent validity of instrumented insoles measuring gait and balance metrics in Parkinson's disease

Several commercially available instrumented insole systems have been examined for validity and repeatability, but very few studies have focused on validation of the Moticon OpenGo sensor insoles in measuring gait and balance parameters in a clinical population. Given the paucity of studies examining the validity of these novel technologies in PD, there were two main goals of this research: (1) assess the concurrent validity of the Moticon OpenGo sensor insoles for gait and balance assessment in people with PD using a pressure-sensitive electronic walkway (Protokinetics Zeno™ walkway) as a reference system and (2) compare the gait metrics derived from the insole and walkway systems during a walking and turning task in order to assess the output of the systems under more real-world conditions. Twelve participants (5F/7M; mean age 71.3 ± 6.8 years) with a diagnosis of Parkinson's disease and a score of 2.5 or 3 on the Modified Hoehn & Yahr score performing straight-line walking, walking with turning, and balance tasks were included in this analysis. Differences in the estimated gait metrics from each system were evaluated through Bland-Altman analysis and calculation of Pearson's correlation coefficient (r). Results showed strong agreement between the insoles and the reference walkway system during both self-selected pace and hurried pace walking tasks. Although agreement is not as strong when estimating spatial metrics, the limits of agreement (LOA) still indicate that clinically important differences can be seen. Additional investigation into the insole derived center of pressure metrics from the insole sensor systems may be necessary as well as investigation into improved algorithms for capturing gait metrics during more real-world conditions in both types of systems.

3D Kinematics Quantifies Gait Response to Levodopa earlier and to a more Comprehensive Extent than the MDS-Unified Parkinson's Disease Rating Scale in Patients with Motor Complications

BACKGROUND

Quantitative 3D movement analysis using inertial measurement units (IMUs) allows for a more detailed characterization of motor patterns than clinical assessment alone. It is essential to discriminate between gait features that are responsive or unresponsive to current therapies to better understand the underlying pathophysiological basis and identify potential therapeutic strategies.

OBJECTIVES

This study aims to characterize the responsiveness and temporal evolution of different gait subcomponents in Parkinson's disease (PD) patients in their OFF and various ON states following levodopa administration, utilizing both wearable sensors and the gold-standard MDS-UPDRS motor part III.

METHODS

Seventeen PD patients were assessed while wearing a full-body set of 15 IMUs in their OFF state and at 20-minute intervals following the administration of a supra-threshold levodopa dose. Gait was reconstructed using a biomechanical model of the human body to quantify how each feature was modulated. Comparisons with non-PD control subjects were conducted in parallel.

RESULTS

Significant motor changes were observed in both the upper and lower limbs according to the MDS-UPDRS III, 40 minutes after levodopa intake. IMU-assisted 3D kinematics detected significant motor alterations as early as 20 minutes after levodopa administration, particularly in upper limbs metrics. Although all "pace-domain" gait features showed significant improvement in the Best-ON state, most rhythmicity, asymmetry, and variability features did not.

CONCLUSION

IMUs are capable of detecting motor alterations earlier and in a more comprehensive manner than the MDS-UPDRS III. The upper limbs respond more rapidly to levodopa, possibly reflecting distinct thresholds to levodopa across striatal regions.

A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders

Step length is an important diagnostic and prognostic measure of health and disease. Wearable devices can estimate step length continuously (e.g., in clinic or real-world settings), however, the accuracy of current estimation methods is not yet optimal. We developed machine-learning models to estimate step length based on data derived from a single lower-back inertial measurement unit worn by 472 young and older adults with different neurological conditions, including Parkinson's disease and healthy controls. Studying more than 80,000 steps, the best model showed high accuracy for a single step (root mean square error, RMSE = 6.08 cm, ICC(2,1) = 0.89) and higher accuracy when averaged over ten consecutive steps (RMSE = 4.79 cm, ICC(2,1) = 0.93), successfully reaching the predefined goal of an RMSE below 5 cm (often considered the minimal-clinically-important-difference). Combining machine-learning with a single, wearable sensor generates accurate step length measures, even in patients with neurologic disease. Additional research may be needed to further reduce the errors in certain conditions.

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

Introduction

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

Methods

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

Results

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

Conclusion

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

Pathological Gait Analysis With an Open-Source Cloud-Enabled Platform Empowered by Semi-Supervised Learning-PathoOpenGait

We present PathoOpenGait, a cloud-based platform for comprehensive gait analysis. Gait assessment is crucial in neurodegenerative diseases such as Parkinson's and multiple system atrophy, yet current techniques are neither affordable nor efficient. PathoOpenGait utilizes 2D and 3D data from a binocular 3D camera for monitoring and analyzing gait parameters. Our algorithms, including a semi-supervised learning-boosted neural network model for turn time estimation and deterministic algorithms to estimate gait parameters, were rigorously validated on annotated gait records, demonstrating high precision and consistency. We further demonstrate PathoOpenGait's applicability in clinical settings by analyzing gait trials from Parkinson's patients and healthy controls. PathoOpenGait is the first open-source, cloud-based system for gait analysis, providing a user-friendly tool for continuous patient care and monitoring. It offers a cost-effective and accessible solution for both clinicians and patients, revolutionizing the field of gait assessment. PathoOpenGait is available at https://pathoopengait.cmdm.tw.

Effects of HiBalance training program on physical function of individuals with idiopathic Parkinson disease: a systematic review and meta-analysis

PURPOSE

The effectiveness of the HiBalance training program for individuals with Parkinson disease (PD) remains debated, prompting a systematic investigation. This study aims to assess whether the HiBalance training program yields favorable outcomes on physical function and self-reported function measures in PD individuals.

METHODS

A systematic search across PubMed, Cochrane Library, Ovid, Scopus, and PEDro databases identified studies exploring HiBalance training's impact on physical function in idiopathic PD. Publication date restrictions were not applied. Two independent reviewers evaluated bias risk using the Cochrane Risk of Bias tool and study quality using the PEDro scale. Effect size (standardized mean difference, SMD) and heterogeneity (Higgins I2) were determined.

RESULTS

Six studies underwent qualitative analysis, with two randomized-controlled trials and one multi-center clinical trial being included in the meta-analysis. HiBalance training exhibited a significant impact on physical function (SMD = 0.49; P = 0.0003). The Mini-BESTest score and gait velocity displayed improvements with moderate-effect sizes. However, solely gait velocity showed clinical enhancement. Yet, these benefits did not remain at the 6- and 12-month follow-ups post-intervention. Self-reported function measures showed no alteration post-HiBalance training. Publication bias was absent.

CONCLUSION

HiBalance training led to clinically significant improvements solely in gait velocity, though these gains waned over time. The findings suggest the necessity of refining the HiBalance program to sustain positive outcomes and ensure lasting enhancements. This underscores the importance of post-HiBalance training exercise programs to maintain benefits in the long term.

PROSPERO REGISTRATION NUMBER

The protocol of this review and meta-analysis was registered at PROSPERO (CRD42022325649). Available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022325649 .

Reliability of waveforms and gait metrics from multiple outdoor wearable inertial sensors collections in adults with knee osteoarthritis

Wearable sensors may allow research to move outside of controlled laboratory settings to be able to collect real-world data in clinical populations, such as older adults with osteoarthritis. However, the reliability of these sensors must be established across multiple out-of-lab data collections. Nine older adults with symptomatic knee arthritis wore wearable inertial sensors on their proximal tibias during an outdoor 6-minute walk test outside of a controlled laboratory setting as part of a pilot study. Reliability of the underlying waveforms, discrete peak outcomes, and spatiotemporal outcomes were assessed over four separate data collections, each approximately 1 week apart. Reliability at a different number of included strides was also assessed at 10, 20, 50, and 100 strides. The underlying waveforms and discrete peak outcome measures had good-to-excellent reliability for all axes, with lower reliability in frontal plane angular velocity axis. Spatiotemporal outcomes demonstrated excellent reliability. The inclusion of additional strides had little to no effect on reliability in most axes, but the confidence intervals generally became smaller across all axes. However, there was improvement in axes with lower (i.e., good) reliability. These data were collected in an out-of-lab setting, and the results are generally consistent with previous in-lab data collections, likely due to its semi-controlled nature. Additional out-of-laboratory research is required to investigate if these trends continue during truly free-living collections. This study provides support for increasing research conducted in out-of-lab data collections, as demonstrated by the good-to-excellent reliability of all axes.

Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

BACKGROUND

Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates.

METHODS

Twenty healthy older adults, 20 people with Parkinson's disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated.

RESULTS

We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms' performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms.

CONCLUSIONS

Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms' performances. Trial registration ISRCTN - 12246987.

Multiscale Entropy Algorithms to Analyze Complexity and Variability of Trunk Accelerations Time Series in Subjects with Parkinson's Disease

The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson's disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 healthy subjects (HS) were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors (τ) 1-6. Differences between swPD and HS were calculated at each τ, and the area under the receiver operating characteristics, optimal cutoff points, post-test probabilities, and diagnostic odds ratios were calculated. MSE, RCMSE, and CIs showed to differentiate swPD from HS. MSE in the anteroposterior direction at τ4 and τ5, and MSE in the ML direction at τ4 showed to characterize the gait disorders of swPD with the best trade-off between positive and negative posttest probabilities and correlated with the motor disability, pelvic kinematics, and stance phase. Using a time series of 2000 data points, a scale factor of 4 or 5 in the MSE procedure can yield the best trade-off in terms of post-test probabilities when compared to other scale factors for detecting gait variability and complexity in swPD.

Automatic Assessments of Parkinsonian Gait with Wearable Sensors for Human Assistive Systems

The rehabilitation evaluation of Parkinson's disease has always been the research focus of human assistive systems. It is a research hotspot to objectively and accurately evaluate the gait condition of Parkinson's disease patients, thereby adjusting the actuators of the human-machine system and making rehabilitation robots better adapt to the recovery process of patients. The rehabilitation evaluation of Parkinson's disease has always been the research focus of rehabilitation robots. It is a research hotspot to be able to objectively and accurately evaluate the recovery of Parkinson's disease patients, thereby adjusting the driving module of the human-machine collaboration system in real time, so that rehabilitation robots can better adapt to the recovery process of Parkinson's disease. The gait task in the Unified Parkinson's Disease Rating Scale (UPDRS) is a widely accepted standard for assessing the gait impairments of patients with Parkinson's disease (PD). However, the assessments conducted by neurologists are always subjective and inaccurate, and the results are determined by the neurologists' observation and clinical experience. Thus, in this study, we proposed a novel machine learning-based method of automatically assessing the gait task in UPDRS with wearable sensors as a more convenient and objective alternative means for PD gait assessment. In the design, twelve gait features, including three spatial-temporal features and nine kinematic features, were extracted and calculated from two shank-mounted IMUs. A novel nonlinear model is developed for calculating the score of gait task from the gait features. Twenty-five PD patients and twenty-eight healthy subjects were recruited for validating the proposed method. For comparison purpose, three traditional models, which have been used in previous studies, were also tested by the same dataset. In terms of percentages of participants, 84.9%, 73.6%, 73.6%, and 66.0% of the participants were accurately assigned into the true level with the proposed nonlinear model, the support vector machine model, the naive Bayes model, and the linear regression model, respectively, which indicates that the proposed method has a good performance on calculating the score of the UPDRS gait task and conformance with the rating done by neurologists.

Gait Characteristics Associated with Fear of Falling in Hospitalized People with Parkinson's Disease

BACKGROUND

Fear of falling (FOF) is common in Parkinson's disease (PD) and associated with distinct gait changes. Here, we aimed to answer, how quantitative gait assessment can improve our understanding of FOF-related gait in hospitalized geriatric patients with PD.

METHODS

In this cross-sectional study of 79 patients with advanced PD, FOF was assessed with the Falls Efficacy Scale International (FES-I), and spatiotemporal gait parameters were recorded with a mobile gait analysis system with inertial measurement units at each foot while normal walking. In addition, demographic parameters, disease-specific motor (MDS-revised version of the Unified Parkinson's Disease Rating Scale, Hoehn & Yahr), and non-motor (Non-motor Symptoms Questionnaire, Montreal Cognitive Assessment) scores were assessed.

RESULTS

According to the FES-I, 22.5% reported low, 28.7% moderate, and 47.5% high concerns about falling. Most concerns were reported when walking on a slippery surface, on an uneven surface, or up or down a slope. In the final regression model, previous falls, more depressive symptoms, use of walking aids, presence of freezing of gait, and lower walking speed explained 42% of the FES-I variance.

CONCLUSION

Our study suggests that FOF is closely related to gait changes in hospitalized PD patients. Therefore, FOF needs special attention in the rehabilitation of these patients, and targeting distinct gait parameters under varying walking conditions might be a promising part of a multimodal treatment program in PD patients with FOF. The effect of these targeted interventions should be investigated in future trials.

Split-Belt Treadmill Training to Improve Gait Adaptation in Parkinson's Disease

BACKGROUND

Gait deficits in people with Parkinson's disease (PD) are triggered by circumstances requiring gait adaptation. The effects of gait adaptation training on a split-belt treadmill (SBT) are unknown in PD.

OBJECTIVE

We investigated the effects of repeated SBT versus tied-belt treadmill (TBT) training on retention and automaticity of gait adaptation and its transfer to over-ground walking and turning.

METHODS

We recruited 52 individuals with PD, of whom 22 were freezers, in a multi-center randomized single-blind controlled study. Training consisted of 4 weeks of supervised treadmill training delivered three times per week. Tests were conducted pre- and post-training and at 4-weeks follow-up. Turning (primary outcome) and gait were assessed over-ground and during a gait adaptation protocol on the treadmill. All tasks were performed with and without a cognitive task.

RESULTS

We found that SBT-training improved gait adaptation with moderate to large effects sizes (P < 0.02) compared to TBT, effects that were sustained at follow-up and during dual tasking. However, better gait adaptation did not transfer to over-ground turning speed. In both SBT- and TBT-arms, over-ground walking and Movement Disorder Society-Unified Parkinson's Disease Rating Scale III (MDS-UPDRS-III scores were improved, the latter of which reached clinically meaningful effects in the SBT-group only. No impact was found on freezing of gait.

CONCLUSION

People with PD are able to learn and retain the ability to overcome asymmetric gait-speed perturbations on a treadmill remarkably well, but seem unable to generalize these skills to asymmetric gait off-treadmill. Future study is warranted into gait adaptation training to boost the transfer of complex walking skills. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Cognitive parameters can predict change of walking performance in advanced Parkinson's disease - Chances and limits of early rehabilitation

Introduction

Links between cognition and walking performance in patients with Parkinson's disease (PD), which both decline with disease progression, are well known. There is lack of knowledge regarding the predictive value of cognition for changes in walking performance after individualized therapy. The aim of this study is to identify relevant predictive cognitive and affective parameters, measurable in daily clinical routines, for change in quantitative walking performance after early geriatric rehabilitation.

Methods

Forty-seven acutely hospitalized patients with advanced PD were assessed at baseline (T1) and at the end (T2) of a 2-week early rehabilitative geriatric complex treatment (ERGCT). Global cognitive performance (Montreal Cognitive Assessment, MoCA), EF and divided attention (Trail Making Test B minus A, delta TMT), depressive symptoms, and fear of falling were assessed at T1. Change in walking performance was determined by the difference in quantitative walking parameters extracted from a sensor-based movement analysis over 20 m straight walking in single (ST, fast and normal pace) and dual task (DT, with secondary cognitive, respectively, motor task) conditions between T1 and T2. Bayesian regression (using Bayes Factor BF₁₀) and multiple linear regression models were used to determine the association of non-motor characteristics for change in walking performance.

Results

Under ST, there was moderate evidence (BF₁₀ = 7.8, respectively, BF₁₀ = 4.4) that lower performance in the ∆TMT at baseline is associated with lower reduction of step time asymmetry after treatment (R ² _adj = 0.26, p ≤ 0.008, respectively, R ² _adj = 0.18, p ≤ 0.009). Under DT walking-cognitive, there was strong evidence (BF₁₀ = 29.9, respectively, BF₁₀ = 27.9) that lower performance in the ∆TMT is associated with more reduced stride time and double limb support (R ² _adj = 0.62, p ≤ 0.002, respectively, R ² _adj = 0.51, p ≤ 0.009). There was moderate evidence (BF₁₀ = 5.1) that a higher MoCA total score was associated with increased gait speed after treatment (R ² _adj = 0.30, p ≤ 0.02).

Discussion

Our results indicate that the effect of ERGT on change in walking performance is limited for patients with deficits in EF and divided attention. However, these patients also seem to walk more cautiously after treatment in walking situations with additional cognitive demand. Therefore, future development of individualized treatment algorithms is required, which address individual needs of these vulnerable patients.

Parkinson's disease multimodal complex treatment improves gait performance: an exploratory wearable digital device-supported study

BACKGROUND

Wearable device-based parameters (DBP) objectively describe gait and balance impairment in Parkinson's disease (PD). We sought to investigate correlations between DBP of gait and balance and clinical scores, their respective changes throughout the inpatient multidisciplinary Parkinson's Disease Multimodal Complex Treatment (PD-MCT), and correlations between their changes.

METHODS

This exploratory observational study assessed 10 DBP and clinical scores at the start (T1) and end (T2) of a two-week PD-MCT of 25 PD in patients (mean age: 66.9 years, median HY stage: 2.5). Subjects performed four straight walking tasks under single- and dual-task conditions, and four balance tasks.

RESULTS

At T1, reduced gait velocity and larger sway area correlated with motor severity. Shorter strides during motor-motor dual-tasking correlated with motor complications. From T1 to T2, gait velocity improved, especially under dual-task conditions, stride length increased for motor-motor dual-tasking, and clinical scores measuring motor severity, balance, dexterity, executive functions, and motor complications changed favorably. Other gait parameters did not change significantly. Changes in motor complications, motor severity, and fear of falling correlated with changes in stride length, sway area, and measures of gait stability, respectively.

CONCLUSION

DBP of gait and balance reflect clinical scores, e.g., those of motor severity. PD-MCT significantly improves gait velocity and stride length and favorably affects additional DBP. Motor complications and fear of falling are factors that may influence the response to PD-MCT. A DBP-based assessment on admission to PD inpatient treatment could allow for more individualized therapy that can improve outcomes.

TRIAL REGISTRATION NUMBER AND DATE

DRKS00020948 number, 30-Mar-2020, retrospectively registered.

Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care

Despite recent advances in technology, clinical phenotyping of Parkinson's disease (PD) has remained relatively limited as current assessments are mainly based on empirical observation and subjective categorical judgment at the clinic. A lack of comprehensive, objective, and quantifiable clinical phenotyping data has hindered our capacity to diagnose, assess patients' conditions, discover pathogenesis, identify preclinical stages and clinical subtypes, and evaluate new therapies. Therefore, deep clinical phenotyping of PD patients is a necessary step towards understanding PD pathology and improving clinical care. In this review, we present a growing community consensus and perspective on how to clinically phenotype this disease, that is, to phenotype the entire course of disease progression by integrating capacity, performance, and perception approaches with state-of-the-art technology. We also explore the most studied aspects of PD deep clinical phenotypes, namely, bradykinesia, tremor, dyskinesia and motor fluctuation, gait impairment, speech impairment, and non-motor phenotypes.

Gait Analysis with Wearables Is a Potential Progression Marker in Parkinson's Disease

Gait disturbance is a prototypical feature of Parkinson's disease (PD), and the quantification of gait using wearable sensors is promising. This study aimed to identify gait impairment in the early and progressive stages of PD according to the Hoehn and Yahr (H-Y) scale. A total of 138 PD patients and 56 healthy controls (HCs) were included in our research. We collected gait parameters using the JiBuEn gait-analysis system. For spatiotemporal gait parameters and kinematic gait parameters, we observed significant differences in stride length (SL), gait velocity, the variability of SL, heel strike angle, and the range of motion (ROM) of the ankle, knee, and hip joints between HCs and PD patients in H-Y Ⅰ-Ⅱ. The changes worsened with the progression of PD. The differences in the asymmetry index of the SL and ROM of the hip were found between HCs and patients in H-Y Ⅳ. Additionally, these gait parameters were significantly associated with Unified Parkinson's Disease Rating Scale and Parkinson's Disease Questionnaire-39. This study demonstrated that gait impairment occurs in the early stage of PD and deteriorates with the progression of the disease. The gait parameters mentioned above may help to detect PD earlier and assess the progression of PD.

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

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

Life-space mobility, balance and self-efficacy in Parkinson's disease: a cross-sectional study

OBJECTIVE

Life-space mobility (LSM) is a mobility measure that assesses the physical and social environments through which people move during their daily lives. To characterize LSM among individuals with Parkinson's disease and explore the relationship between LSM, self-efficacy, and balance.

DESIGN

A cross-sectional study.

SETTINGS

Movement disorder clinic at a teaching hospital.

PARTICIPANTS

Eighty-eight participants with Parkinson's disease.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

The dependent variable (LSM) was assessed using the Life-Space Assessment (LSA) instrument. Balance evaluation and balance self-efficacy were assessed using the Mini Balance Evaluation Systems Test (Mini-BESTest) and the Activities-Specific Balance Confidence Scale, respectively. Other variables, such as age, disease staging (Hoehn-Yahr staging system), cognition (Montreal Cognitive Assessment), and depressive symptoms (Beck Depression Inventory-II), were also measured.

RESULTS

The mean LSA score was 65.22 (SD [SD]: 22.75) and mean age was 66.99 years (SD: 9.35 years). Among the 88 patients, 35 (39.7%) were classified as restricted SM. Age (P = 0.03), disease severity (P = 0.02), cognition (P = 0.02), and motor subtype (P = 0.006) were associated with more restricted LSM among participants. A multiple linear regression model demonstrated that LSM can be predicted by balance performance (R²  = 0.377; P < 0.001).

CONCLUSIONS

Age, disease severity, cognition, motor subtype, balance self-efficacy, and balance performance are associated with LSM. Understanding and improving balance and self-efficacy in people with Parkinson's disease could facilitate community mobility and promote functional independence and health maintenance. This article is protected by copyright. All rights reserved.

Concurrent Validity of Zeno Instrumented Walkway and Video-Based Gait Features in Adults With Parkinson's Disease

BACKGROUND

Parkinson's disease (PD) presents with motor symptoms such as bradykinesia, rigidity, and tremor that can affect gait. To monitor changes associated with disease progression or medication use, quantitative gait assessment is often performed during clinical visits. Conversely, vision-based solutions have been proposed for monitoring gait quality in non-clinical settings.

METHODS

We use three 2D human pose-estimation libraries (AlphaPose, Detectron, OpenPose) and one 3D library (ROMP) to calculate gait features from color video, and correlate them with those extracted by a Zeno instrumented walkway in older adults with PD. We calculate video-based gait features using a manual and automated heel-strike detection algorithm, and compare the correlations when the participants walk towards and away from the camera separately.

RESULTS

Based on analysis of 67 bidirectional walking bouts from 25 adults with PD, moderate to strong positive correlations were identified between the number of steps, cadence, as well as the mean and coefficient of variation of step width calculated from Zeno and video using 2D pose-estimation libraries. We noted that our automated heel-strike annotation method struggled to identify short steps.

CONCLUSION

Gait features calculated from 2D joint trajectories are more strongly correlated with the Zeno than analogous gait features calculated from ROMP. Based on our analysis, videos processed with 2D pose-estimation libraries can be used for longitudinal gait monitoring in individuals with PD. Future work will seek to improve the prediction of gait features using a comprehensive machine learning model to predict gait features directly from color video without relying on intermediate extraction of joint trajectories.

Predicting Axial Impairment in Parkinson's Disease through a Single Inertial Sensor

Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson’s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. Methods: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. Results: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). Conclusions: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.

Repeated Gait Perturbation Training in Parkinson's Disease and Healthy Older Adults: A Systematic Review and Meta-Analysis

Background: Gait impairments are common in healthy older adults (HOA) and people with Parkinson's disease (PwPD), especially when adaptations to the environment are required. Traditional rehabilitation programs do not typically address these adaptive gait demands in contrast to repeated gait perturbation training (RGPT). RGPT is a novel reactive form of gait training with potential for both short and long-term consolidation in HOA and PwPD. The aim of this systematic review with meta-analysis is to determine whether RGPT is more effective than non-RGPT gait training in improving gait and balance in HOA and PwPD in the short and longer term. Methods: This review was conducted according to the PRISMA-guidelines and pre-registered in the PROSPERO database (CRD42020183273). Included studies tested the effects of any form of repeated perturbations during gait in HOA and PwPD on gait speed, step or stride length. Studies using balance scales or sway measures as outcomes were included in a secondary analysis. Effects of randomized controlled trials (RCT) on RGPT were pooled using a meta-analysis of final measures. Results: Of the 4421 studies, eight studies were deemed eligible for review, of which six could be included in the meta-analysis, totaling 209 participants (159 PwPD and 50 HOA). The studies were all of moderate quality. The meta-analysis revealed no significant effects of RGPT over non-RGPT training on gait performance (SMD = 0.16; 95% CI = -0.18, 0.49; Z = 0.92; P = 0.36). Yet, in some individual studies, favorable effects on gait speed, step length and stride length were observed immediately after the intervention as well as after a retention period. Gait variability and asymmetry, signifying more direct outcomes of gait adaptation, also indicated favorable RGPT effects in some individual studies. Conclusion: Despite some promising results, the pooled effects of RGPT on gait and balance were not significantly greater as compared to non-RGPT gait training in PwPD and HOA. However, these findings could have been driven by low statistical power. Therefore, the present review points to the imperative to conduct sufficiently powered RCT's to verify the true effects of RGPT on gait and balance in HOA and PwPD. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php? Identifier: CRD42020183273.

Validation of quantitative gait analysis systems for Parkinson's disease for use in supervised and unsupervised environments

BACKGROUND

Gait impairments are among the most common and impactful symptoms of Parkinson's disease (PD). Recent technological advances aim to quantify these impairments using low-cost wearable systems for use in either supervised clinical consultations or long-term unsupervised monitoring of gait in ecological environments. However, very few of these wearable systems have been validated comparatively to a criterion of established validity.

OBJECTIVE

We developed two movement analysis solutions (3D full-body kinematics based on inertial sensors, and a smartphone application) in which validity was assessed versus the optoelectronic criterion in a population of PD patients.

METHODS

Nineteen subjects with PD (7 female) participated in the study (age: 62 ± 12.27 years; disease duration: 6.39 ± 3.70 years; HY: 2 ± 0.23). Each participant underwent a gait analysis whilst barefoot, at a self-selected speed, for a distance of 3 times 10 m in a straight line, assessed simultaneously with all three systems.

RESULTS

Our results show excellent agreement between either solution and the optoelectronic criterion. Both systems differentiate between PD patients and healthy controls, and between PD patients in ON or OFF medication states (normal difference distributions pooled from published research in PD patients in ON and OFF states that included an age-matched healthy control group). Fair to high waveform similarity and mean absolute errors below the mean relative orientation accuracy of the equipment were found when comparing the angular kinematics between the full-body inertial sensor-based system and the optoelectronic criterion.

CONCLUSIONS

We conclude that the presented solutions produce accurate results and can capture clinically relevant parameters using commodity wearable sensors or a simple smartphone. This validation will hopefully enable the adoption of these systems for supervised and unsupervised gait analysis in clinical practice and clinical trials.

Wearable sensors in the diagnosis and study of Parkinson's disease symptoms: a systematic review

Nowadays, there are several diseases which affect different systems of the body, producing changes in the correct functioning of the organism and the people lifestyles. One of them is Parkinson's disease (PD), which is defined as a neurodegenerative disorder provoked by the destruction of dopaminergic neurons in the brain, resulting in a set of motor and non-motor symptoms. As this disease affects principally to ancient people, several researchers have studied different treatments and therapies for stopping neurodegeneration and diminishing symptoms, to improve the quality patients' lives. The most common therapies created for PD are based on pharmacological treatment for controlling the degeneration advance and the physical ones which do not reveal the progress of patients. For this reason, this review paper opens the possibility for using wearable motion capture systems as an option for the control and study of PD. Therefore, it aims to (1) study the different wearable systems used for capture the movements of PD patients and (2) determine which of them bring better results for monitoring and assess PD people. For the analysis, it uses papers based on experiments that prove the functioning of several motion systems in different aspects as monitoring, treatment and diagnose of the disease. As a result, it works with 30 papers which describe the factors mentioned before. Additionally, the paper uses journals and literature review about the pathology, its characteristics and the function of wearable sensors for the correct understanding of the topic.

Wearables als unterstützendes Tool für den Paradigmenwechsel in der Versorgung von Parkinson Patienten

Tragbare Sensoren – „Wearables“ – eignen sich, Funktionsstörungen bei Parkinson Patienten zu erheben und werden zur Prävention, Prädiktion, Diagnostik und Therapieunterstützung genutzt. In der Forschung erhöhen sie die Reliabilität der erhobenen Daten und stellen bessere Studien-Endpunkte dar, als die herkömmlichen, subjektiven und wenig quantitativen Rating- und Selbstbeurteilungsskalen. Untersucht werden motorische Symptome wie Tremor, Bradykinese und Gangstörungen und auch nicht motorische Symptome. In der Home-Monitoringanwendung kann der Ist-Zustand des Patienten im realen Leben untersucht werden, die Therapie überwacht, die Adhärenz verbessert und die Compliance überprüft werden. Zusätzlich können Wearables interventionell zur Verbesserung von Symptomen eingesetzt werden wie z. B. Cueing, Gamification oder Coaching. Der Transfer von Laborbedingungen in den häuslichen Alltag ist eine medizinisch-technische Herausforderung. Optimierte Versorgungsmodelle müssen entwickelt werden und der tatsächliche Nutzen für den individuellen Patienten in weiteren Studien belegt werden.

Simple Smartphone-Based Assessment of Gait Characteristics in Parkinson Disease: Validation Study

Background

Parkinson disease (PD) is a common movement disorder. Patients with PD have multiple gait impairments that result in an increased risk of falls and diminished quality of life. Therefore, gait measurement is important for the management of PD.

Objective

We previously developed a smartphone-based dual-task gait assessment that was validated in healthy adults. The aim of this study was to test the validity of this gait assessment in people with PD, and to examine the association between app-derived gait metrics and the clinical and functional characteristics of PD.

Methods

Fifty-two participants with clinically diagnosed PD completed assessments of walking, Movement Disorder Society Unified Parkinson Disease Rating Scale III (UPDRS III), Montreal Cognitive Assessment (MoCA), Hamilton Anxiety (HAM-A), and Hamilton Depression (HAM-D) rating scale tests. Participants followed multimedia instructions provided by the app to complete two 20-meter trials each of walking normally (single task) and walking while performing a serial subtraction dual task (dual task). Gait data were simultaneously collected with the app and gold-standard wearable motion sensors. Stride times and stride time variability were derived from the acceleration and angular velocity signal acquired from the internal motion sensor of the phone and from the wearable sensor system.

Results

High correlations were observed between the stride time and stride time variability derived from the app and from the gold-standard system (r=0.98-0.99, P<.001), revealing excellent validity of the app-based gait assessment in PD. Compared with those from the single-task condition, the stride time (F_1,103=14.1, P<.001) and stride time variability (F_1,103=6.8, P=.008) in the dual-task condition were significantly greater. Participants who walked with greater stride time variability exhibited a greater UPDRS III total score (single task: β=.39, P<.001; dual task: β=.37, P=.01), HAM-A (single-task: β=.49, P=.007; dual-task: β=.48, P=.009), and HAM-D (single task: β=.44, P=.01; dual task: β=.49, P=.009). Moreover, those with greater dual-task stride time variability (β=.48, P=.001) or dual-task cost of stride time variability (β=.44, P=.004) exhibited lower MoCA scores.

Conclusions

A smartphone-based gait assessment can be used to provide meaningful metrics of single- and dual-task gait that are associated with disease severity and functional outcomes in individuals with PD.

Risk of Falling in a Timed Up and Go Test Using an UWB Radar and an Instrumented Insole

Previously, studies reported that falls analysis is possible in the elderly, when using wearable sensors. However, these devices cannot be worn daily, as they need to be removed and recharged from time-to-time due to their energy consumption, data transfer, attachment to the body, etc. This study proposes to introduce a radar sensor, an unobtrusive technology, for risk of falling analysis and combine its performance with an instrumented insole. We evaluated our methods on datasets acquired during a Timed Up and Go (TUG) test where a stride length (SL) was computed by the insole using three approaches. Only the SL from the third approach was not statistically significant (p = 0.2083 > 0.05) compared to the one provided by the radar, revealing the importance of a sensor location on human body. While reducing the number of force sensors (FSR), the risk scores using an insole containing three FSRs and y-axis of acceleration were not significantly different (p > 0.05) compared to the combination of a single radar and two FSRs. We concluded that contactless TUG testing is feasible, and by supplementing the instrumented insole to the radar, more precise information could be available for the professionals to make accurate decision.