Real-life gait assessment in degenerative cerebellar ataxia

Neurology ◽  
2020 ◽  
Vol 95 (9) ◽  
pp. e1199-e1210
Author(s):  
Winfried Ilg ◽  
Jens Seemann ◽  
Martin Giese ◽  
Andreas Traschütz ◽  
Ludger Schöls ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Wearable Sensors ◽  
Real Life ◽  
High Sensitivity ◽  
Gait Variability ◽  
Clinical Severity ◽  
Effect Sizes ◽  
Cerebellar Disease ◽  
Ataxic Gait ◽  
Disease Modifying Drugs

ObjectivesWith disease-modifying drugs on the horizon for degenerative ataxias, ecologically valid motor biomarkers are highly warranted. In this observational study, we aimed to unravel and validate markers of ataxic gait in real life by using wearable sensors.MethodsWe assessed gait characteristics of 43 patients with degenerative cerebellar disease (Scale for the Assessment and Rating of Ataxia [SARA] 9.4 ± 3.9) compared with 35 controls by 3 body-worn inertial sensors in 3 conditions: (1) laboratory-based walking; (2) supervised free walking; (3) real-life walking during everyday living (subgroup n = 21). Movement analysis focused on measures of spatiotemporal step variability and movement smoothness.ResultsA set of gait variability measures was identified that allowed us to consistently identify ataxic gait changes in all 3 conditions. Lateral step deviation and a compound measure of spatial step variability categorized patients vs controls with a discrimination accuracy of 0.86 in real life. Both were highly correlated with clinical ataxia severity (effect size ρ = 0.76). These measures allowed detecting group differences even for patients who differed only 1 point in the clinical SARAposture&gait subscore, with highest effect sizes for real-life walking (d = 0.67).ConclusionsWe identified measures of ataxic gait that allowed us not only to capture the gait variability inherent in ataxic gait in real life, but also to demonstrate high sensitivity to small differences in disease severity, with the highest effect sizes in real-life walking. They thus represent promising candidates for motor markers for natural history and treatment trials in ecologically valid contexts.Classification of evidenceThis study provides Class I evidence that a set of gait variability measures, even if accessed in real life, correlated with the clinical severity of ataxia in patients with degenerative cerebellar disease.

scholarly journals Towards ecologically valid biomarkers: real-life gait assessment in cerebellar ataxia

2019 ◽  
Author(s):  
Winfried Ilg ◽  
Jens Seemann ◽  
Martin Giese ◽  
Andreas Traschütz ◽  
Ludger Schöls ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Wearable Sensors ◽  
Real Life ◽  
Movement Analysis ◽  
High Sensitivity ◽  
Step Length ◽  
Gait Variability ◽  
Effect Sizes ◽  
Ataxic Gait ◽  
Disease Modifying Drugs

AbstractBACKGROUNDWith disease-modifying drugs on the horizon for degenerative ataxias, motor biomarkers are highly warranted. While ataxic gait and its treatment-induced improvements can be captured in laboratory-based assessments, quantitative markers of ataxic gait in real life will help to determine ecologically meaningful improvements.OBJECTIVESTo unravel and validate markers of ataxic gait in real life by using wearable sensors.METHODSWe assessed gait characteristics of 43 patients with degenerative cerebellar disease (SARA:9.4±3.9) compared to 35 controls by 3 body-worn inertial sensors in three conditions: (1) laboratory-based walking; (2) supervised free walking; (3) real-life walking during everyday living (subgroup n=21). Movement analysis focussed on measures of movement smoothness and spatio-temporal step variability.RESULTSA set of gait variability measures was identified which allowed to consistently identify ataxic gait changes in all three conditions. Lateral step deviation and a compound measure of step length categorized patients against controls in real life with a discrimination accuracy of 0.86. Both were highly correlated with clinical ataxia severity (effect size ρ=0.76). These measures allowed detecting group differences even for patients who differed only 1 point in the SARAp&g subscore, with highest effect sizes for real-life walking (d=0.67).CONCLUSIONSWe identified measures of ataxic gait that allowed not only to capture the gait variability inherent in ataxic gait in real life, but also demonstrate high sensitivity to small differences in disease severity - with highest effect sizes in real-life walking. They thus represent promising candidates for quantitative motor markers for natural history and treatment trials in ecologically valid contexts.

scholarly journals Smartphone-Based Monitoring of Parkinson Disease: Quasi-Experimental Study to Quantify Hand Tremor Severity and Medication Effectiveness (Preprint)

2020 ◽  
Author(s):  
Elina Kuosmanen ◽  
Florian Wolling ◽  
Julio Vega ◽  
Valerii Kan ◽  
Yuuki Nishiyama ◽  
...  
Keyword(s):  
Parkinson Disease ◽  
Rating Scale ◽  
Inertial Sensors ◽  
Negative Impact ◽  
Wearable Sensors ◽  
Real Life ◽  
Rank Correlation ◽  
Accelerometer Data ◽  
Hand Tremor ◽  
Significant Difference

BACKGROUND Hand tremor typically has a negative impact on a person’s ability to complete many common daily activities. Previous research has investigated how to quantify hand tremor with smartphones and wearable sensors, mainly under controlled data collection conditions. Solutions for daily real-life settings remain largely underexplored. OBJECTIVE Our objective was to monitor and assess hand tremor severity in patients with Parkinson disease (PD), and to better understand the effects of PD medications in a naturalistic environment. METHODS Using the Welch method, we generated periodograms of accelerometer data and computed signal features to compare patients with varying degrees of PD symptoms. RESULTS We introduced and empirically evaluated the tremor intensity parameter (TIP), an accelerometer-based metric to quantify hand tremor severity in PD using smartphones. There was a statistically significant correlation between the TIP and self-assessed Unified Parkinson Disease Rating Scale (UPDRS) II tremor scores (Kendall rank correlation test: z=30.521, <i>P</i>&lt;.001, τ=0.5367379; n=11). An analysis of the “before” and “after” medication intake conditions identified a significant difference in accelerometer signal characteristics among participants with different levels of rigidity and bradykinesia (Wilcoxon rank sum test, <i>P</i>&lt;.05). CONCLUSIONS Our work demonstrates the potential use of smartphone inertial sensors as a systematic symptom severity assessment mechanism to monitor PD symptoms and to assess medication effectiveness remotely. Our smartphone-based monitoring app may also be relevant for other conditions where hand tremor is a prevalent symptom.

scholarly journals Smartphone-Based Monitoring of Parkinson Disease: Quasi-Experimental Study to Quantify Hand Tremor Severity and Medication Effectiveness

10.2196/21543 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e21543
Author(s):  
Elina Kuosmanen ◽  
Florian Wolling ◽  
Julio Vega ◽  
Valerii Kan ◽  
Yuuki Nishiyama ◽  
...  
Keyword(s):  
Parkinson Disease ◽  
Rating Scale ◽  
Inertial Sensors ◽  
Negative Impact ◽  
Wearable Sensors ◽  
Real Life ◽  
Rank Correlation ◽  
Accelerometer Data ◽  
Hand Tremor ◽  
Significant Difference

Background Hand tremor typically has a negative impact on a person’s ability to complete many common daily activities. Previous research has investigated how to quantify hand tremor with smartphones and wearable sensors, mainly under controlled data collection conditions. Solutions for daily real-life settings remain largely underexplored. Objective Our objective was to monitor and assess hand tremor severity in patients with Parkinson disease (PD), and to better understand the effects of PD medications in a naturalistic environment. Methods Using the Welch method, we generated periodograms of accelerometer data and computed signal features to compare patients with varying degrees of PD symptoms. Results We introduced and empirically evaluated the tremor intensity parameter (TIP), an accelerometer-based metric to quantify hand tremor severity in PD using smartphones. There was a statistically significant correlation between the TIP and self-assessed Unified Parkinson Disease Rating Scale (UPDRS) II tremor scores (Kendall rank correlation test: z=30.521, P<.001, τ=0.5367379; n=11). An analysis of the “before” and “after” medication intake conditions identified a significant difference in accelerometer signal characteristics among participants with different levels of rigidity and bradykinesia (Wilcoxon rank sum test, P<.05). Conclusions Our work demonstrates the potential use of smartphone inertial sensors as a systematic symptom severity assessment mechanism to monitor PD symptoms and to assess medication effectiveness remotely. Our smartphone-based monitoring app may also be relevant for other conditions where hand tremor is a prevalent symptom.

scholarly journals Changes in Overall Participation Profile of Youth with Physical Disabilities Following the PREP Intervention

2020 ◽  
Vol 17 (11) ◽  
pp. 3990
Author(s):  
Colin Hoehne ◽  
Brittany Baranski ◽  
Louiza Benmohammed ◽  
Liam Bienstock ◽  
Nathan Menezes ◽  
...  
Keyword(s):  
Life Experiences ◽  
Physical Disabilities ◽  
Real Life ◽  
Children And Youth ◽  
Effect Sizes ◽  
Special School ◽  
Access To Information ◽  
Environmental Barriers ◽  
Potential Impact ◽  
School Roles

The Pathways and Resources for Engagement and Participation (PREP), an environmental-based intervention, is effective in improving the participation of youth with disabilities in specific targeted activities; however, its potential impact on overall participation beyond these activities is unknown. This study examined the differences in participation levels and environmental barriers and supports following the 12-week PREP intervention. Existing data on participation patterns and environmental barriers and supports, measured by the Participation and Environment Measure for Children and Youth, pre-and post-PREP intervention, were statistically analyzed across 20 youth aged 12 to 18 (mean = 14.4, standard deviation (SD) = 1.82) with physical disabilities in three settings: home, school and community. Effect sizes were calculated using Cohen’s d. Following PREP, youth participated significantly less often at home (d = 2.21; 95% Confidence Interval (CI) [1.79, 2.96]), more often (d = 0.57; 95% CI [−0.79, −0.14]) and in more diverse activities (d = 0.51; 95% CI [−1.99, −0.51]) in the community. At school, significantly greater participation was observed in special school roles (t = −2.46. p = 0.024). Involvement and desire for change remained relatively stable across all settings. A substantial increase in community environmental supports was observed (d = 0.67), with significantly more parents reporting availability of, and access to information as a support (χ2 = 4.28, p = 0.038). Findings lend further support to the effectiveness of environmental-based interventions, involving real-life experiences.

scholarly journals Independent and sensitive gait parameters for objective evaluation in knee and hip osteoarthritis using wearable sensors

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ramon J. Boekesteijn ◽  
José M. H. Smolders ◽  
Vincent J. J. F. Busch ◽  
Alexander C. H. Geurts ◽  
Katrijn Smulders
Keyword(s):  
Dual Task ◽  
Wearable Sensors ◽  
Body Motion ◽  
Effect Sizes ◽  
Upper Body ◽  
Healthy Controls ◽  
Gait Parameters ◽  
Dual Task Cost ◽  
Dual Task Performance ◽  
Spatiotemporal Parameters

Abstract Background Although it is well-established that osteoarthritis (OA) impairs daily-life gait, objective gait assessments are not part of routine clinical evaluation. Wearable inertial sensors provide an easily accessible and fast way to routinely evaluate gait quality in clinical settings. However, during these assessments, more complex and meaningful aspects of daily-life gait, including turning, dual-task performance, and upper body motion, are often overlooked. The aim of this study was therefore to investigate turning, dual-task performance, and upper body motion in individuals with knee or hip OA in addition to more commonly assessed spatiotemporal gait parameters using wearable sensors. Methods Gait was compared between individuals with unilateral knee (n = 25) or hip OA (n = 26) scheduled for joint replacement, and healthy controls (n = 27). For 2 min, participants walked back and forth along a 6-m trajectory making 180° turns, with and without a secondary cognitive task. Gait parameters were collected using 4 inertial measurement units on the feet and trunk. To test if dual-task gait, turning, and upper body motion had added value above spatiotemporal parameters, a factor analysis was conducted. Effect sizes were computed as standardized mean difference between OA groups and healthy controls to identify parameters from these gait domains that were sensitive to knee or hip OA. Results Four independent domains of gait were obtained: speed-spatial, speed-temporal, dual-task cost, and upper body motion. Turning parameters constituted a gait domain together with cadence. From the domains that were obtained, stride length (speed-spatial) and cadence (speed-temporal) had the strongest effect sizes for both knee and hip OA. Upper body motion (lumbar sagittal range of motion), showed a strong effect size when comparing hip OA with healthy controls. Parameters reflecting dual-task cost were not sensitive to knee or hip OA. Conclusions Besides more commonly reported spatiotemporal parameters, only upper body motion provided non-redundant and sensitive parameters representing gait adaptations in individuals with hip OA. Turning parameters were sensitive to knee and hip OA, but were not independent from speed-related gait parameters. Dual-task parameters had limited additional value for evaluating gait in knee and hip OA, although dual-task cost constituted a separate gait domain. Future steps should include testing responsiveness of these gait domains to interventions aiming to improve mobility.

scholarly journals Gait Variability and Complexity during Single and Dual-Task Walking on Different Surfaces in Outdoor Environment

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4792
Author(s):  
Denisa Nohelova ◽  
Lucia Bizovska ◽  
Nicolas Vuillerme ◽  
Zdenek Svoboda
Keyword(s):  
Dual Task ◽  
Inertial Sensor ◽  
Cognitive Task ◽  
Real Life ◽  
Gait Pattern ◽  
Gait Variability ◽  
Outdoor Environment ◽  
Manual Task ◽  
Lower Trunk ◽  
Trunk Acceleration

Nowadays, gait assessment in the real life environment is gaining more attention. Therefore, it is desirable to know how some factors, such as surfaces (natural, artificial) or dual-tasking, influence real life gait pattern. The aim of this study was to assess gait variability and gait complexity during single and dual-task walking on different surfaces in an outdoor environment. Twenty-nine healthy young adults aged 23.31 ± 2.26 years (18 females, 11 males) walked at their preferred walking speed on three different surfaces (asphalt, cobbles, grass) in single-task and in two dual-task conditions (manual task—carrying a cup filled with water, cognitive task—subtracting the number 7). A triaxial inertial sensor attached to the lower trunk was used to record trunk acceleration during gait. From 15 strides, sample entropy (SampEn) as an indicator of gait complexity and root mean square (RMS) as an indicator of gait variability were computed. The findings demonstrate that in an outdoor environment, the surfaces significantly impacted only gait variability, not complexity, and that the tasks affected both gait variability and complexity in young healthy adults.

SonicFace

2021 ◽  
Vol 5 (4) ◽  
pp. 1-33
Author(s):  
Yang Gao ◽  
Yincheng Jin ◽  
Seokmin Choi ◽  
Jiyang Li ◽  
Junjie Pan ◽  
...  
Keyword(s):  
Facial Expressions ◽  
Microphone Array ◽  
Wearable Sensors ◽  
Real Life ◽  
Privacy Concerns ◽  
Fine Grained ◽  
Recognition Of Facial Expressions ◽  
Ubiquitous Sensing ◽  
Contact Free

Accurate recognition of facial expressions and emotional gestures is promising to understand the audience's feedback and engagement on the entertainment content. Existing methods are primarily based on various cameras or wearable sensors, which either raise privacy concerns or demand extra devices. To this aim, we propose a novel ubiquitous sensing system based on the commodity microphone array --- SonicFace, which provides an accessible, unobtrusive, contact-free, and privacy-preserving solution to monitor the user's emotional expressions continuously without playing hearable sound. SonicFace utilizes a pair of speaker and microphone array to recognize various fine-grained facial expressions and emotional hand gestures by emitted ultrasound and received echoes. Based on a set of experimental evaluations, the accuracy of recognizing 6 common facial expressions and 4 emotional gestures can reach around 80%. Besides, the extensive system evaluations with distinct configurations and an extended real-life case study have demonstrated the robustness and generalizability of the proposed SonicFace system.

scholarly journals Wearable Sensors Integrated with Virtual Reality: A Self-Guided Healthcare System Measuring Shoulder Joint Mobility for Frozen Shoulder

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Jianjun Cui ◽  
Shih-Ching Yeh ◽  
Si-Huei Lee
Keyword(s):  
Virtual Reality ◽  
Shoulder Joint ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Frozen Shoulder ◽  
Joint Mobility ◽  
Joint Movement ◽  
Rehabilitation Process ◽  
Shoulder Condition ◽  
At Home

Frozen shoulder is a common clinical shoulder condition. Measuring the degree of shoulder joint movement is crucial to the rehabilitation process. Such measurements can be used to evaluate the severity of patients’ condition, establish rehabilitation goals and appropriate activity difficulty levels, and understand the effects of rehabilitation. Currently, measurements of the shoulder joint movement degree are typically conducted by therapists using a protractor. However, along with the growth of telerehabilitation, measuring the shoulder joint mobility on patients’ own at home will be needed. In this study, wireless inertial sensors were combined with the virtual reality interactive technology to provide an innovative shoulder joint mobility self-measurement system that can enable patients to measure their performance of four shoulder joint movements on their own at home. Pilot clinical trials were conducted with 25 patients to confirm the feasibility of the system. In addition, the results of correlation and differential analyses compared with the results of traditional measurement methods exhibited a high correlation, verifying the accuracy of the proposed system. Moreover, according to interviews with patients, they are confident in their ability to measure shoulder joint mobility themselves.

scholarly journals Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4132 ◽  
Author(s):  
Ku Ku Abd. Rahim ◽  
I. Elamvazuthi ◽  
Lila Izhar ◽  
Genci Capi
Keyword(s):  
Activity Recognition ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Ensemble Methods ◽  
Daily Activities ◽  
Support Vector ◽  
Random Subspace ◽  
Ensemble Classifiers ◽  
Accuracy Rate

Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace. These ensemble classifiers employed Support vector machine (SVM) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is evaluated with the holdout and 10-fold cross-validation evaluation methods. The performance of each human daily activity was measured in terms of precision, recall, F-measure, and receiver operating characteristic (ROC) curve. In addition, the performance is also measured based on the comparison of overall accuracy rate of classification between different ensemble classifiers and base learners. It was observed that overall, SVM produced better accuracy rate with 99.22% compared to RF with 97.91% based on a random subspace ensemble classifier.

The Effect of Lesion Characteristic on Remineralization and Model Sensitivity

1992 ◽  
Vol 71 (3_suppl) ◽  
pp. 811-813 ◽  
Author(s):  
F. Schäfer ◽  
S.J. Raven ◽  
T.A. Parr
Keyword(s):  
Real Life ◽  
High Sensitivity ◽  
Lesion Size ◽  
Experimental Conditions ◽  
Treatment Groups ◽  
Human Enamel ◽  
Enamel Sample ◽  
Two Factors ◽  
Treatment Bias

A major criterion for assessing the value of any experimental model in scientific research is the degree of correspondence between its results and data from the real-life process it is designed to model. Intra-oral models aimed at predicting the anti-caries efficacy of toothpastes or other topical treatments should therefore be calibrated against treatments proven to be effective in a caries clinical trial. For this to be achieved, it is necessary that a model with high sensitivity be designed, while at the same time retaining relevance to the process to be modeled. This means that the effects of the various experimental conditions and parameters of the model on its performance must be understood. The purpose of this paper was to assess the influence of two specific factors on the performance of an in situ enamel remineralization model, which is based on human enamel slabs attached to partial dentures. The two factors are initial lesion severity and origin of enamel sample. The results indicated that initial lesion size affected whether net remineralization or net demineralization occurred during in situ treatment. Samples with an initial range of from 1500 to 2500 (ΔZ) tended more toward demineralization than did samples with ΔZ > 3500. This means that treatment groups must be well-balanced with respect to initial lesion size. Differences in initial demineralization severity between different tooth locations must also be considered so that systematic treatment bias can be avoided. The solution used in the model discussed here is based on a balanced experimental design, which allows this effect to be taken into account in the data analysis.

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