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Wearable Sensors for Movement Analysis

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 50122

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Special Issue Editors

Prof. Dr. Jeffrey M. Hausdorff

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Guest Editor

Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 62431, Israel
Interests: aging; gait; falls; freezing of gait; wearables; Parkinson’s disease; fractal physiology

Prof. Dr. Jaap van Dieën

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Guest Editor

Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Institute for Brain and Behavior Amsterdam & Amsterdam Movement Sciences, 1081 BT Amsterdam, The Netherlands
Interests: neuromechanics; posture; gait; movement disorders; musculoskeletal disorders

Dr. Martina Mancini

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Guest Editor

Department of Neurology, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239-3098, USA
Interests: balance; turning; home monitoring; Parkinson's disease; closed-loop systems for gait rehabilitation; mobile imaging

Special Issue Information

Dear Colleagues,

In 1964, H Gage reported on the use of an accelerometer to quantify the “smoothness” of walking. Thirteen years later, GL Smidt and colleagues took that work a step further and linked a 3-D accelerometer and footswitches to a PDP-12 to quantitatively characterize gait over a 7-meter pathway in patients with tibial-femoral knee implants. We have come a long way since those early studies. The PDP-12 is now “ancient history”. Today, with smartphones and cloud computing, wearables are used to evaluate movement, gait, balance, and mobility, in conventional research settings, as well as in our streets, in our homes, and even on ski slopes over extended periods of time, changing the landscape of wearables and opening up new opportunities for research and clinical applications. Other types of sensors, such as thermal and electrophysiological sensors, have been miniaturized and made wearable to integrate additional information on human movement. Whether they are used to provide new insights into physiology and patho-physiology; to assess the impact of a new drug, therapy or training; or to generate real-time feedback and interventions, wearables are becoming an increasingly ubiquitous tool for the study and treatment of movement and mobility.

As the journal Sensors celebrates its 20^th year, and as we mark what may be the sixth decade of wearables for the study of movement, we invite you to take part in this celebration by submitting manuscripts for a Special Issue devoted to ‘’Wearables for Movement Analysis’’. Papers from a wide variety of perspectives that will help to advance the field are invited. For more information or questions about the fit of a potential manuscript, please e-mail us.

Prof. Jeffrey M. Hausdorff
Prof. Dr. Jaap van Dieen
Dr. Martina Mancini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

accelerometers
inertial measurement units
gait
balance
mobility
postural transitions
aging
falls
validation
EMG
ECG
fNIRS
EEG

Published Papers (12 papers)

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Assessment of the Shank-to-Vertical Angle While Changing Heel Heights Using a Single Inertial Measurement Unit in Individuals with Incomplete Spinal Cord Injury Wearing an Ankle-Foot-Orthosis

by Lysanne A. F. de Jong, Yvette L. Kerkum, Tom de Groot, Marije Vos-van der Hulst, Ilse J. W. van Nes and Noel L. W. Keijsers

Sensors 2021, 21(3), 985; https://doi.org/10.3390/s21030985 - 2 Feb 2021

Cited by 2 | Viewed by 3332

Abstract

Previous research showed that an Inertial Measurement Unit (IMU) on the anterior side of the shank can accurately measure the Shank-to-Vertical Angle (SVA), which is a clinically-used parameter to guide tuning of ankle-foot orthoses (AFOs). However, in this context it is specifically important that differences in the SVA are detected during the tuning process, i.e., when adjusting heel height. This study investigated the validity of the SVA as measured by an IMU and its responsiveness to changes in AFO-footwear combination (AFO-FC) heel height in persons with incomplete spinal cord injury (iSCI). Additionally, the effect of heel height on knee flexion-extension angle and internal moment was evaluated. Twelve persons with an iSCI walked with their own AFO-FC in three different conditions: (1) without a heel wedge (refHH), (2) with 5 mm heel wedge (lowHH) and (3) with 10 mm heel wedge (highHH). Walking was recorded by a single IMU on the anterior side of the shank and a 3D gait analysis (3DGA) simultaneously. To estimate validity, a paired t-test and intraclass correlation coefficient (ICC) between the SVA_IMU and SVA_3DGA were calculated for the refHH. A repeated measures ANOVA was performed to evaluate the differences between the heel heights. A good validity with a mean difference smaller than 1 and an ICC above 0.9 was found for the SVA during midstance phase and at midstance. Significant differences between the heel heights were found for changes in SVA_IMU (p = 0.036) and knee moment (p = 0.020) during the midstance phase and in SVA_IMU (p = 0.042) and SVA_3DGA (p = 0.006) at midstance. Post-hoc analysis revealed a significant difference between the ref and high heel height condition for the SVA_IMU (p = 0.005) and knee moment (p = 0.006) during the midstance phase and for the SVA_IMU (p = 0.010) and SVA_3DGA (p = 0.006) at the instant of midstance. The SVA measured with an IMU is valid and responsive to changing heel heights and equivalent to the gold standard 3DGA. The knee joint angle and knee joint moment showed concomitant changes compared to SVA as a result of changing heel height. Full article

(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)

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15 pages, 702 KiB

Open AccessArticle

The Impact of Environment on Gait Assessment: Considerations from Real-World Gait Analysis in Dementia Subtypes

by Ríona Mc Ardle, Silvia Del Din, Paul Donaghy, Brook Galna, Alan J Thomas and Lynn Rochester

Sensors 2021, 21(3), 813; https://doi.org/10.3390/s21030813 - 26 Jan 2021

Cited by 30 | Viewed by 4325

Abstract

Laboratory-based gait assessments are indicative of clinical outcomes (e.g., disease identification). Real-world gait may be more sensitive to clinical outcomes, as impairments may be exaggerated in complex environments. This study aims to investigate how different environments (e.g., lab, real world) impact gait. Different walking bout lengths in the real world will be considered proxy measures of context. Data collected in different dementia disease subtypes will be analysed as disease-specific gait impairments are reported between these groups. Thirty-two people with cognitive impairment due to Alzheimer’s disease (AD), 28 due to dementia with Lewy bodies (DLB) and 25 controls were recruited. Participants wore a tri-axial accelerometer for six 10 m walks in lab settings, and continuously for seven days in the real world. Fourteen gait characteristics across five domains were measured (i.e., pace, variability, rhythm, asymmetry, postural control). In the lab, the DLB group showed greater step length variability (p = 0.008) compared to AD. Both subtypes demonstrated significant gait impairments (p < 0.01) compared to controls. In the real world, only very short walking bouts (<10 s) demonstrated different gait impairments between subtypes. The context where walking occurs impacts signatures of gait impairment in dementia subtypes. To develop real-world gait assessment as a clinical tool, algorithms and metrics must accommodate for changes in context. Full article

(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)

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13 pages, 1293 KiB

Open AccessArticle

Hearing Loss Is Associated with Increased Variability in Double Support Period in the Elderly

by Betsy Szeto, Damiano Zanotto, Erin M. Lopez, John A. Stafford, John S. Nemer, Adam R. Chambers, Sunil K. Agrawal and Anil K. Lalwani

Sensors 2021, 21(1), 278; https://doi.org/10.3390/s21010278 - 4 Jan 2021

Cited by 7 | Viewed by 2348

Abstract

Hearing loss is a disabling condition that increases with age and has been linked to difficulties in walking and increased risk of falls. The purpose of this study is to investigate changes in gait parameters associated with hearing loss in a group of older adults aged 60 or greater. Custom-engineered footwear was used to collect spatiotemporal gait data in an outpatient clinical setting. Multivariable linear regression was used to determine the relationship between spatiotemporal gait parameters and high and low frequency hearing thresholds of the poorer hearing ear, the left ear, and the right ear, respectively, adjusting for age, sex, race/ethnicity, and the Dizziness Handicap Inventory–Screening version score. Worsening high and low frequency hearing thresholds were associated with increased variability in double support period. Effects persisted after adjusting for the effects of age and perceived vestibular disability and were greater for increases in hearing thresholds for the right ear compared to the left ear. These findings illustrate the importance of auditory feedback for balance and coordination and may suggest a right ear advantage for the influence of auditory feedback on gait. Full article

(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)

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18 pages, 1771 KiB

Open AccessArticle

Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders

by Rana Zia Ur Rehman, Yuhan Zhou, Silvia Del Din, Lisa Alcock, Clint Hansen, Yu Guan, Tibor Hortobágyi, Walter Maetzler, Lynn Rochester and Claudine J. C. Lamoth

Sensors 2020, 20(23), 6992; https://doi.org/10.3390/s20236992 - 7 Dec 2020

Cited by 23 | Viewed by 4611

Abstract

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43–99% sensitivity and 48–98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making. Full article

(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)

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16 pages, 2360 KiB

Open AccessArticle

An Objective Methodology for the Selection of a Device for Continuous Mobility Assessment

by Tecla Bonci, Alison Keogh, Silvia Del Din, Kirsty Scott, Claudia Mazzà and on behalf of the Mobilise-D consortium

Sensors 2020, 20(22), 6509; https://doi.org/10.3390/s20226509 - 14 Nov 2020

Cited by 10 | Viewed by 4230

Abstract

Continuous monitoring by wearable technology is ideal for quantifying mobility outcomes in “real-world” conditions. Concurrent factors such as validity, usability, and acceptability of such technology need to be accounted for when choosing a monitoring device. This study proposes a bespoke methodology focused on defining a decision matrix to allow for effective decision making. A weighting system based on responses (n = 69) from a purpose-built questionnaire circulated within the IMI Mobilise-D consortium and its external collaborators was established, accounting for respondents’ background and level of expertise in using wearables in clinical practice. Four domains (concurrent validity, CV; human factors, HF; wearability and usability, WU; and data capture process, CP), associated evaluation criteria, and scores were established through literature research and group discussions. While the CV was perceived as the most relevant domain (37%), the others were also considered highly relevant (WU: 30%, HF: 17%, CP: 16%). Respondents (~90%) preferred a hidden fixation and identified the lower back as an ideal sensor location for mobility outcomes. Overall, this study provides a novel, holistic, objective, as well as a standardized approach accounting for complementary aspects that should be considered by professionals and researchers when selecting a solution for continuous mobility monitoring. Full article

(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)

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13 pages, 977 KiB

Open AccessArticle

Dual-Task Gait Stability after Concussion and Subsequent Injury: An Exploratory Investigation

by David R. Howell, Scott Bonnette, Jed A. Diekfuss, Dustin R. Grooms, Gregory D. Myer, Julie C. Wilson and William P. Meehan III

Sensors 2020, 20(21), 6297; https://doi.org/10.3390/s20216297 - 5 Nov 2020

Cited by 14 | Viewed by 3458

Abstract

Persistent gait alterations can occur after concussion and may underlie future musculoskeletal injury risk. We compared dual-task gait stability measures among adolescents who did/did not sustain a subsequent injury post-concussion, and uninjured controls. Forty-seven athletes completed a dual-task gait evaluation. One year later, they reported sport-related injuries and sport participation volumes. There were three groups: concussion participants who sustained a sport-related injury (n = 8; age =15.4 ± 3.5 years; 63% female), concussion participants who did not sustain a sport-related injury (n = 24; 14.0 ± 2.6 years; 46% female), and controls (n = 15; 14.2 ± 1.9 years; 53% female). Using cross-recurrence quantification, we quantified dual-task gait stability using diagonal line length, trapping time, percent determinism, and laminarity. The three groups reported similar levels of sports participation (11.8 ± 5.8 vs. 8.6 ± 4.4 vs. 10.9 ± 4.3 hours/week; p = 0.37). The concussion/subsequent injury group walked slower (0.76 ± 0.14 vs. 0.65 ± 0.13 m/s; p = 0.008) and demonstrated higher diagonal line length (0.67 ± 0.08 vs. 0.58 ± 0.05; p = 0.02) and trapping time (5.3 ± 1.5 vs. 3.8 ± 0.6; p = 0.006) than uninjured controls. Dual-task diagonal line length (hazard ratio =1.95, 95% CI = 1.05–3.60), trapping time (hazard ratio = 1.66, 95% CI = 1.09–2.52), and walking speed (hazard ratio = 0.01, 95% CI = 0.00–0.51) were associated with subsequent injury. Dual-task gait stability measures can identify altered movement that persists despite clinical concussion recovery and is associated with future injury risk. Full article

(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)

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16 pages, 2126 KiB

Open AccessArticle

Effect of Bout Length on Gait Measures in People with and without Parkinson’s Disease during Daily Life

by Vrutangkumar V. Shah, James McNames, Graham Harker, Martina Mancini, Patricia Carlson-Kuhta, John G. Nutt, Mahmoud El-Gohary, Carolin Curtze and Fay B. Horak

Sensors 2020, 20(20), 5769; https://doi.org/10.3390/s20205769 - 12 Oct 2020

Cited by 19 | Viewed by 3110

Abstract

Although the use of wearable technology to characterize gait disorders in daily life is increasing, there is no consensus on which specific gait bout length should be used to characterize gait. Clinical trialists using daily life gait quality as study outcomes need to understand how gait bout length affects the sensitivity and specificity of measures to discriminate pathological gait as well as the reliability of gait measures across gait bout lengths. We investigated whether Parkinson’s disease (PD) affects how gait characteristics change as bout length changes, and how gait bout length affects the reliability and discriminative ability of gait measures to identify gait impairments in people with PD compared to neurotypical Old Adults (OA). We recruited 29 people with PD and 20 neurotypical OA of similar age for this study. Subjects wore 3 inertial sensors, one on each foot and one over the lumbar spine all day, for 7 days. To investigate which gait bout lengths should be included to extract gait measures, we determined the range of gait bout lengths available across all subjects. To investigate if the effect of bout length on each gait measure is similar or not between subjects with PD and OA, we used a growth curve analysis. For reliability and discriminative ability of each gait measure as a function of gait bout length, we used the intraclass correlation coefficient (ICC) and area under the curve (AUC), respectively. Ninety percent of subjects walked with a bout length of less than 53 strides during the week, and the majority (>50%) of gait bouts consisted of less than 12 strides. Although bout length affected all gait measures, the effects depended on the specific measure and sometimes differed for PD versus OA. Specifically, people with PD did not increase/decrease cadence and swing duration with bout length in the same way as OA. ICC and AUC characteristics tended to be larger for shorter than longer gait bouts. Our findings suggest that PD interferes with the scaling of cadence and swing duration with gait bout length. Whereas control subjects gradually increased cadence and decreased swing duration as bout length increased, participants with PD started with higher than normal cadence and shorter than normal stride duration for the smallest bouts, and cadence and stride duration changed little as bout length increased, so differences between PD and OA disappeared for the longer bout lengths. Gait measures extracted from shorter bouts are more common, more reliable, and more discriminative, suggesting that shorter gait bouts should be used to extract potential digital biomarkers for people with PD. Full article

(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)

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16 pages, 1784 KiB

Open AccessArticle

Using Wearable Sensors and Machine Learning to Automatically Detect Freezing of Gait during a FOG-Provoking Test

by Tal Reches, Moria Dagan, Talia Herman, Eran Gazit, Natalia A. Gouskova, Nir Giladi, Brad Manor and Jeffrey M. Hausdorff

Sensors 2020, 20(16), 4474; https://doi.org/10.3390/s20164474 - 10 Aug 2020

Cited by 28 | Viewed by 4998

Abstract

Freezing of gait (FOG) is a debilitating motor phenomenon that is common among individuals with advanced Parkinson’s disease. Objective and sensitive measures are needed to better quantify FOG. The present work addresses this need by leveraging wearable devices and machine-learning methods to develop and evaluate automated detection of FOG and quantification of its severity. Seventy-one subjects with FOG completed a FOG-provoking test while wearing three wearable sensors (lower back and each ankle). Subjects were videotaped before (OFF state) and after (ON state) they took their antiparkinsonian medications. Annotations of the videos provided the “ground-truth” for FOG detection. A leave-one-patient-out validation process with a training set of 57 subjects resulted in 84.1% sensitivity, 83.4% specificity, and 85.0% accuracy for FOG detection. Similar results were seen in an independent test set (data from 14 other subjects). Two derived outcomes, percent time frozen and number of FOG episodes, were associated with self-report of FOG. Both derived-metrics were higher in the OFF state than in the ON state and in the most challenging level of the FOG-provoking test, compared to the least challenging level. These results suggest that this automated machine-learning approach can objectively assess FOG and that its outcomes are responsive to therapeutic interventions. Full article

(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)

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14 pages, 4510 KiB

Open AccessArticle

Validation of an IMU Suit for Military-Based Tasks

by Matthew P. Mavor, Gwyneth B. Ross, Allison L. Clouthier, Thomas Karakolis and Ryan B. Graham

Sensors 2020, 20(15), 4280; https://doi.org/10.3390/s20154280 - 31 Jul 2020

Cited by 44 | Viewed by 8564

Abstract

Investigating the effects of load carriage on military soldiers using optical motion capture is challenging. However, inertial measurement units (IMUs) provide a promising alternative. Our purpose was to compare optical motion capture with an Xsens IMU system in terms of movement reconstruction using principal component analysis (PCA) using correlation coefficients and joint kinematics using root mean squared error (RMSE). Eighteen civilians performed military-type movements while their motion was recorded using both optical and IMU-based systems. Tasks included walking, running, and transitioning between running, kneeling, and prone positions. PCA was applied to both the optical and virtual IMU markers, and the correlations between the principal component (PC) scores were assessed. Full-body joint angles were calculated and compared using RMSE between optical markers, IMU data, and virtual markers generated from IMU data with and without coordinate system alignment. There was good agreement in movement reconstruction using PCA; the average correlation coefficient was 0.81 ± 0.14. RMSE values between the optical markers and IMU data for flexion-extension were less than 9°, and 15° for the lower and upper limbs, respectively, across all tasks. The underlying biomechanical model and associated coordinate systems appear to influence RMSE values the most. The IMU system appears appropriate for capturing and reconstructing full-body motion variability for military-based movements. Full article

(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)

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15 pages, 4541 KiB

Open AccessArticle

A Subject-Specific Approach to Detect Fatigue-Related Changes in Spine Motion Using Wearable Sensors

by Victor C.H. Chan, Shawn M. Beaudette, Kenneth B. Smale, Kristen H.E. Beange and Ryan B. Graham

Sensors 2020, 20(9), 2646; https://doi.org/10.3390/s20092646 - 6 May 2020

Cited by 8 | Viewed by 2970

Abstract

An objective method to detect muscle fatigue-related kinematic changes may reduce workplace injuries. However, heterogeneous responses to muscle fatigue suggest that subject-specific analyses are necessary. The objectives of this study were to: (1) determine if wearable inertial measurement units (IMUs) could be used in conjunction with a spine motion composite index (SMCI) to quantify subject-specific changes in spine kinematics during a repetitive spine flexion-extension (FE) task; and (2) determine if the SMCI was correlated with measures of global trunk muscle fatigue. Spine kinematics were measured using wearable IMUs in 10 healthy adults during a baseline set followed by 10 sets of 50 spine FE repetitions. After each set, two fatigue measures were collected: perceived level of fatigue using a visual analogue scale (VAS), and maximal lift strength. SMCIs incorporating 10 kinematic variables from 2 IMUs (pelvis and T8 vertebrae) were calculated and used to quantify subject-specific changes in movement. A main effect of set was observed (F (1.7, 15.32) = 10.42, p = 0.002), where the SMCI became significantly greater than set 1 starting at set 4. Significant correlations were observed between the SMCI and both fatigue VAS and maximal lift strength at the individual and study level. These findings support the use of wearable IMUs to detect subject-specific changes in spine motion associated with muscle fatigue. Full article

(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)

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12 pages, 2555 KiB

Open AccessLetter

Identifying Fatigue Indicators Using Gait Variability Measures: A Longitudinal Study on Elderly Brisk Walking

by Guoxin Zhang, Ivy Kwan-Kei Wong, Tony Lin-Wei Chen, Tommy Tung-Ho Hong, Duo Wai-Chi Wong, Yinghu Peng, Fei Yan, Yan Wang, Qitao Tan and Ming Zhang

Sensors 2020, 20(23), 6983; https://doi.org/10.3390/s20236983 - 7 Dec 2020

Cited by 8 | Viewed by 2504

Abstract

Real-time detection of fatigue in the elderly during physical exercises can help identify the stability and thus falling risks which are commonly achieved by the investigation of kinematic parameters. In this study, we aimed to identify the change in gait variability parameters from inertial measurement units (IMU) during a course of 60 min brisk walking which could lay the foundation for the development of fatigue-detecting wearable sensors. Eighteen elderly people were invited to participate in the brisk walking trials for 60 min with a single IMU attached to the posterior heel region of the dominant side. Nine sets of signals, including the accelerations, angular velocities, and rotation angles of the heel in three anatomical axes, were measured and extracted at the three walking times (baseline, 30th min, and 60th min) of the trial for analysis. Sixteen of eighteen participants reported fatigue after walking, and there were significant differences in the median acceleration (p = 0.001), variability of angular velocity (p = 0.025), and range of angle rotation (p = 0.0011), in the medial–lateral direction. In addition, there were also significant differences in the heel pronation angle (p = 0.005) and variability and energy consumption of the angles in the anterior–posterior axis (p = 0.028, p = 0.028), medial–lateral axis (p = 0.014, p = 0.014), and vertical axis (p = 0.002, p < 0.001). Our study demonstrated that a single IMU on the posterior heel of the dominant side can address the variability of kinematics parameters for elderly performing prolonged brisk walking and could serve as an indicator for walking instability, and thus fatigue. Full article

(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)

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12 pages, 2184 KiB

Open AccessLetter

Quantification of Arm Swing during Walking in Healthy Adults and Parkinson’s Disease Patients: Wearable Sensor-Based Algorithm Development and Validation

by Elke Warmerdam, Robbin Romijnders, Julius Welzel, Clint Hansen, Gerhard Schmidt and Walter Maetzler

Sensors 2020, 20(20), 5963; https://doi.org/10.3390/s20205963 - 21 Oct 2020

Cited by 18 | Viewed by 4647

Abstract

Neurological pathologies can alter the swinging movement of the arms during walking. The quantification of arm swings has therefore a high clinical relevance. This study developed and validated a wearable sensor-based arm swing algorithm for healthy adults and patients with Parkinson’s disease (PwP). Arm swings of 15 healthy adults and 13 PwP were evaluated (i) with wearable sensors on each wrist while walking on a treadmill, and (ii) with reflective markers for optical motion capture fixed on top of the respective sensor for validation purposes. The gyroscope data from the wearable sensors were used to calculate several arm swing parameters, including amplitude and peak angular velocity. Arm swing amplitude and peak angular velocity were extracted with systematic errors ranging from 0.1 to 0.5° and from −0.3 to 0.3°/s, respectively. These extracted parameters were significantly different between healthy adults and PwP as expected based on the literature. An accurate algorithm was developed that can be used in both clinical and daily-living situations. This algorithm provides the basis for the use of wearable sensor-extracted arm swing parameters in healthy adults and patients with movement disorders such as Parkinson’s disease. Full article

(This article belongs to the Special Issue Wearable Sensors for Movement Analysis)

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