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.

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 Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 82 ◽  
Author(s):  
Udeni Jayasinghe ◽  
William S. Harwin ◽  
Faustina Hwang
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Wearable Sensors ◽  
Correlation Coefficients ◽  
Movement Analysis ◽  
Multiple Sensors ◽  
Sensor Orientation ◽  
Multiple Devices ◽  
Good Potential ◽  
Sensor Errors

Inertial sensors are a useful instrument for long term monitoring in healthcare. In many cases, inertial sensor devices can be worn as an accessory or integrated into smart textiles. In some situations, it may be beneficial to have data from multiple inertial sensors, rather than relying on a single worn sensor, since this may increase the accuracy of the analysis and better tolerate sensor errors. Integrating multiple sensors into clothing improves the feasibility and practicality of wearing multiple devices every day, in approximately the same location, with less likelihood of incorrect sensor orientation. To facilitate this, the current work investigates the consequences of attaching lightweight sensors to loose clothes. The intention of this paper is to discuss how data from these clothing sensors compare with similarly placed body worn sensors, with additional consideration of the resulting effects on activity recognition. This study compares the similarity between the two signals (body worn and clothing), collected from three different clothing types (slacks, pencil skirt and loose frock), across multiple daily activities (walking, running, sitting, and riding a bus) by calculating correlation coefficients for each sensor pair. Even though the two data streams are clearly different from each other, the results indicate that there is good potential of achieving high classification accuracy when using inertial sensors in clothing.

scholarly journals The Use of Wearable Sensors in Human Movement Analysis in Non-Swimming Aquatic Activities: A Systematic Review

2019 ◽  
Vol 16 (24) ◽  
pp. 5067
Author(s):  
Daniel A. Marinho ◽  
Henrique P. Neiva ◽  
Jorge E. Morais
Keyword(s):  
Systematic Review ◽  
Aquatic Environment ◽  
Web Of Science ◽  
Inertial Sensors ◽  
State Of The Art ◽  
Wearable Sensors ◽  
Movement Analysis ◽  
Human Movement ◽  
Dry Land ◽  
Swimming Kinematics

The use of smart technology, specifically inertial sensors (accelerometers, gyroscopes, and magnetometers), to analyze swimming kinematics is being reported in the literature. However, little is known about the usage/application of such sensors in other human aquatic exercises. As the sensors are getting smaller, less expensive, and simple to deal with (regarding data acquisition), one might consider that its application to a broader range of exercises should be a reality. The aim of this systematic review was to update the state of the art about the framework related to the use of sensors assessing human movement in an aquatic environment, besides swimming. The following databases were used: IEEE Xplore, Pubmed, Science Direct, Scopus, and Web of Science. Five articles published in indexed journals, aiming to assess human exercises/movements in the aquatic environment were reviewed. The data from the five articles was categorized and summarized based on the aim, purpose, participants, sensor’s specifications, body area and variables analyzed, and data analysis and statistics. The analyzed studies aimed to compare the movement/exercise kinematics between environments (i.e., dry land versus aquatic), and in some cases compared healthy to pathological participants. The use of sensors in a rehabilitation/hydrotherapy perspective may provide major advantages for therapists.

scholarly journals Crossing Virtual Doors: A New Method to Study Gait Impairments and Freezing of Gait in Parkinson’s Disease

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Luis I. Gómez-Jordana ◽  
James Stafford ◽  
C. (Lieke) E. Peper ◽  
Cathy M. Craig
Keyword(s):  
Real World ◽  
Ecological Validity ◽  
Freezing Of Gait ◽  
Real Life ◽  
Step Length ◽  
Gait Variability ◽  
Healthy Controls ◽  
Real World Setting ◽  
History Of ◽  
Gait Impairments

Studying freezing of gait (FOG) in the lab has proven problematic. This has primarily been due to the difficulty in designing experimental setups that maintain high levels of ecological validity whilst also permitting sufficient levels of experimental control. To help overcome these challenges, we have developed a virtual reality (VR) environment with virtual doorways, a situation known to illicit FOG in real life. To examine the validity of this VR environment, an experiment was conducted, and the results were compared to a previous “real-world” experiment. A group of healthy controls (N = 10) and a group of idiopathic Parkinson disease (PD) patients without any FOG episodes (N = 6) and with a history of freezing (PD-f, N = 4) walked under three different virtual conditions (no door, narrow doorway (100% of shoulder width) and standard doorway (125% of shoulder width)). The results were similar to those obtained in the real-world setting. Virtual doorways reduced step length and velocity while increasing general gait variability. The PD-f group always walked slower, with a smaller step length, and showed the largest increases in gait variability. The narrow doorway induced FOG in 66% of the trials, while the standard doorway caused FOG in 29% of the trials. Our results closely mirrored those obtained with real doors. In short, this methodology provides a safe, personalized yet adequately controlled means to examine FOG in Parkinson’s patients, along with possible interventions.

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 Step Length Estimation with Wearable Sensors Using a Switched-Gain Nonlinear Observer

2021 ◽  
Author(s):  
Ali Nouriani ◽  
Robert A McGovern ◽  
Rajesh Rajamani
Keyword(s):  
Inertial Sensors ◽  
Wearable Sensors ◽  
Infrared Camera ◽  
Step Length ◽  
The Body ◽  
Estimation Problem ◽  
Nonlinear Observer ◽  
Length Estimation ◽  
Minimum Number ◽  
Sensor Configuration

This paper focuses on step length estimation using inertial measurement units. Accurate step length estimation has a number of useful health applications, including its use in characterizing the postural instability of Parkinson’s disease patients. Three different sensor configurations are studied using sensors on the shank and/or thigh of a human subject. The estimation problem has several challenges due to unknown measurement bias, misalignment of the sensors on the body and the desire to use a minimum number of sensors. A nonlinear estimation problem is formulated that aims to estimate shank angle, thigh angle, bias parameters of the inertial sensors and step lengths. A nonlinear observer is designed using Lyapunov analysis and requires solving an LMI to find a stabilizing observer gain. It turns out that global stability over the entire operating region can only be obtained by using switched gains, one gain for each piecewise monotonic region of the nonlinear output function. Experimental results are presented on the performance of the nonlinear observer and compared with gold standard reference measurements from an infrared camera capture system. An innovative technique that utilizes three sensors is shown to provide a step length accuracy nearly equal to that of the four-sensor configuration.

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 Continuous home monitoring of Parkinson’s disease using inertial sensors: A systematic review

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246528
Author(s):  
Marco Sica ◽  
Salvatore Tedesco ◽  
Colum Crowe ◽  
Lorna Kenny ◽  
Kevin Moore ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Home Monitoring ◽  
Relevant Information ◽  
Step Length ◽  
Living Environment ◽  
Free Living ◽  
Gait Impairments

Parkinson’s disease (PD) is a progressive neurological disorder of the central nervous system that deteriorates motor functions, while it is also accompanied by a large diversity of non-motor symptoms such as cognitive impairment and mood changes, hallucinations, and sleep disturbance. Parkinsonism is evaluated during clinical examinations and appropriate medical treatments are directed towards alleviating symptoms. Tri-axial accelerometers, gyroscopes, and magnetometers could be adopted to support clinicians in the decision-making process by objectively quantifying the patient’s condition. In this context, at-home data collections aim to capture motor function during daily living and unobstructedly assess the patients’ status and the disease’s symptoms for prolonged time periods. This review aims to collate existing literature on PD monitoring using inertial sensors while it focuses on papers with at least one free-living data capture unsupervised either directly or via videotapes. Twenty-four papers were selected at the end of the process: fourteen investigated gait impairments, eight of which focused on walking, three on turning, two on falls, and one on physical activity; ten articles on the other hand examined symptoms, including bradykinesia, tremor, dyskinesia, and motor state fluctuations in the on/off phenomenon. In summary, inertial sensors are capable of gathering data over a long period of time and have the potential to facilitate the monitoring of people with Parkinson’s, providing relevant information about their motor status. Concerning gait impairments, kinematic parameters (such as duration of gait cycle, step length, and velocity) were typically used to discern PD from healthy subjects, whereas for symptoms’ assessment, researchers were capable of achieving accuracies of over 90% in a free-living environment. Further investigations should be focused on the development of ad-hoc hardware and software capable of providing real-time feedback to clinicians and patients. In addition, features such as the wearability of the system and user comfort, set-up process, and instructions for use, need to be strongly considered in the development of wearable sensors for PD monitoring.

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.

Sign in / Sign up

Export Citation Format

Share Document