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Wearable Sensors for Assessment of Gait in Older Adults

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 17031

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Special Issue Editors

Dr. Barry Greene

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Guest Editor

Kinesis Health Technologies Ltd，Belfield Office Park, Clonskeagh, Dublin D04 V2N9, Ireland
Interests: fall risk assessment; gait assessment; digital biomarkers; machine learning; biomedical signal processing

Dr. Rahul Soangra

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Guest Editor

Fowler School of Engineering, Chapman University, Orange, CA 92866, USA
Interests: biomechanics; gait; postural control; wearable sensors; falls
Special Issues, Collections and Topics in MDPI journals

Dr. Alan Bourke

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Guest Editor

pRED Informatics, Digital Biomarkers, Roche Pharmaceutical Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel CH 4070, Switzerland
Interests: inertial sensors; human activity recognition; fall-detection; gait-analysis; human physiological measurement and human biomechanics

Special Issue Information

Dear Colleagues,

Assessment of gait and physical function have been traditionally carried out in dedicated laboratories using specialized motion capture equipment. Recent developments in wearable sensors and algorithms have made it feasible and cost-effective to measure gait and motor function over a prolonged period in a home, clinical or community setting. Wearable sensors have enormous potential in healthcare management as a means of facilitating ambulatory monitoring of physical function, over a prolonged period of time, particularly in older adults who are the biggest drivers of healthcare utilization.

Ongoing monitoring of gait may facilitate assessment of disease progression and the development of novel digital biomarkers for neurological disorders, as well as early detection of deviations from normal function in a non-clinical environment. Detection of gait abnormalities or deteriorations in physical and function could identify the presence of diseases and pathologies associated with increased risk of falling or reduced or physical cognitive function, facilitating timely intervention to prevent falls, reverse frailty, and manage disease progression.

This Special Issue aims to present a collection of recent advances in wearable sensor-based gait assessment in older adults, in particular assessment of gait, fall risk, physical and cognitive function, disease progression and the development of novel digital biomarkers.

Dr. Barry Greene
Dr. Rahul Soangra
Dr. Alan Bourke
Guest Editors

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Keywords

gait
falls
inertial sensors
wearable sensors
digital biomarkers
neurological disease

Published Papers (5 papers)

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Research

19 pages, 12473 KiB

Open AccessArticle

Effects of Load Carriage on Postural Control and Spatiotemporal Gait Parameters during Level and Uphill Walking

by Asimina Mexi, Ioannis Kafetzakis, Maria Korontzi, Dimitris Karagiannakis, Perikles Kalatzis and Dimitris Mandalidis

Sensors 2023, 23(2), 609; https://doi.org/10.3390/s23020609 - 5 Jan 2023

Cited by 1 | Viewed by 1746

Abstract

Load carriage and uphill walking are conditions that either individually or in combination can compromise postural control and gait eliciting several musculoskeletal low back and lower limb injuries. The objectives of this study were to investigate postural control responses and spatiotemporal parameters of gait during level and uphill unloaded (UL), back-loaded (BL), and front-loaded (FL) walking. Postural control was assessed in 30 asymptomatic individuals by simultaneously recording (i) EMG activity of neck, thoracic and lumbar erector spinae, and rectus abdominis, (ii) projected 95% ellipse area as well as the anteroposterior and mediolateral trunk displacement, and (iii) spatiotemporal gait parameters (stride/step length and cadence). Measurements were performed during level (0%) and uphill (5, 10, and 15%) walking at a speed of 5 km h⁻¹ without and with a suspended front pack or a backpack weighing 15% of each participant’s body weight. The results of our study showed that postural control, as indicated by increased erector spinae EMG activity and changes in spatiotemporal parameters of gait that manifested with decreased stride/step length and increased cadence, is compromised particularly during level and uphill FL walking as opposed to BL or UL walking, potentially increasing the risk of musculoskeletal and fall-related injuries. Full article

(This article belongs to the Special Issue Wearable Sensors for Assessment of Gait in Older Adults)

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11 pages, 1187 KiB

Open AccessArticle

Validity and Reliability of the Insole3 Instrumented Shoe Insole for Ground Reaction Force Measurement during Walking and Running

by Leora A. Cramer, Markus A. Wimmer, Philip Malloy, Joan A. O’Keefe, Christopher B. Knowlton and Christopher Ferrigno

Sensors 2022, 22(6), 2203; https://doi.org/10.3390/s22062203 - 11 Mar 2022

Cited by 11 | Viewed by 3295

Abstract

Pressure-detecting insoles such as the Insole3 have potential as a portable alternative for assessing vertical ground reaction force (vGRF) outside of specialized laboratories. This study evaluated whether the Insole3 is a valid and reliable alternative to force plates for measuring vGRF. Eleven healthy participants walked overground at slow and moderately paced speeds and ran at a moderate pace while collecting vGRF simultaneously from a force plate (3000 Hz) and Insole3 (100 Hz). Intraclass correlation coefficients (ICC) demonstrated excellent vGRF agreement between systems during both walking speeds for Peak 1, Peak 2, the valley between peaks, and the vGRF impulse (ICC > 0.941). There was excellent agreement during running for the single vGRF peak (ICC = 0.942) and impulse (ICC = 0.940). The insoles slightly underestimated vGRF peaks (−3.7% to 0.9% bias) and valleys (−2.2% to −1.8% bias), and slightly overestimated impulses (4.2% to 5.6% bias). Reliability between visits for all three activities was excellent (ICC > 0.970). The Insole3 is a valid and reliable alternative to traditional force plates for assessing vGRF during walking and running in healthy adults. The excellent ICC values during slow walking suggests that the Insole3 may be particularly suitable for older adults in clinical and home settings. Full article

(This article belongs to the Special Issue Wearable Sensors for Assessment of Gait in Older Adults)

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15 pages, 5093 KiB

Open AccessArticle

Visualization-Driven Time-Series Extraction from Wearable Systems Can Facilitate Differentiation of Passive ADL Characteristics among Stroke and Healthy Older Adults

by Joby John and Rahul Soangra

Sensors 2022, 22(2), 598; https://doi.org/10.3390/s22020598 - 13 Jan 2022

Cited by 2 | Viewed by 2171

Abstract

Wearable technologies allow the measurement of unhindered activities of daily living (ADL) among patients who had a stroke in their natural settings. However, methods to extract meaningful information from large multi-day datasets are limited. This study investigated new visualization-driven time-series extraction methods for distinguishing activities from stroke and healthy adults. Fourteen stroke and fourteen healthy adults wore a wearable sensor at the L5/S1 position for three consecutive days and collected accelerometer data passively in the participant’s naturalistic environment. Data from visualization facilitated selecting information-rich time series, which resulted in classification accuracy of 97.3% using recurrent neural networks (RNNs). Individuals with stroke showed a negative correlation between their body mass index (BMI) and higher-acceleration fraction produced during ADL. We also found individuals with stroke made lower activity amplitudes than healthy counterparts in all three activity bands (low, medium, and high). Our findings show that visualization-driven time series can accurately classify movements among stroke and healthy groups using a deep recurrent neural network. This novel visualization-based time-series extraction from naturalistic data provides a physical basis for analyzing passive ADL monitoring data from real-world environments. This time-series extraction method using unit sphere projections of acceleration can be used by a slew of analysis algorithms to remotely track progress among stroke survivors in their rehabilitation program and their ADL abilities. Full article

(This article belongs to the Special Issue Wearable Sensors for Assessment of Gait in Older Adults)

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19 pages, 2481 KiB

Open AccessArticle

An Automatic Gait Analysis Pipeline for Wearable Sensors: A Pilot Study in Parkinson’s Disease

by Luis R. Peraza, Kirsi M. Kinnunen, Roisin McNaney, Ian J. Craddock, Alan L. Whone, Catherine Morgan, Richard Joules and Robin Wolz

Sensors 2021, 21(24), 8286; https://doi.org/10.3390/s21248286 - 11 Dec 2021

Cited by 10 | Viewed by 4147

Abstract

The use of wearable sensors allows continuous recordings of physical activity from participants in free-living or at-home clinical studies. The large amount of data collected demands automatic analysis pipelines to extract gait parameters that can be used as clinical endpoints. We introduce a deep learning-based automatic pipeline for wearables that processes tri-axial accelerometry data and extracts gait events—bout segmentation, initial contact (IC), and final contact (FC)—from a single sensor located at either the lower back (near L5), shin or wrist. The gait events detected are posteriorly used for gait parameter estimation, such as step time, length, and symmetry. We report results from a leave-one-subject-out (LOSO) validation on a pilot study dataset of five participants clinically diagnosed with Parkinson’s disease (PD) and six healthy controls (HC). Participants wore sensors at three body locations and walked on a pressure-sensing walkway to obtain reference gait data. Mean absolute errors (MAE) for the IC events ranged from 22.82 to 33.09 milliseconds (msecs) for the lower back sensor while for the shin and wrist sensors, MAE ranges were 28.56–64.66 and 40.19–72.50 msecs, respectively. For the FC-event detection, MAE ranges were 29.06–48.42, 40.19–72.70 and 36.06–60.18 msecs for the lumbar, wrist and shin sensors, respectively. Intraclass correlation coefficients, ICC(2,k), between the estimated parameters and the reference data resulted in good-to-excellent agreement (ICC ≥ 0.84) for the lumbar and shin sensors, excluding the double support time (ICC = 0.37 lumbar and 0.38 shin) and swing time (ICC = 0.55 lumbar and 0.59 shin). The wrist sensor also showed good agreements, but the ICCs were lower overall than for the other two sensors. Our proposed analysis pipeline has the potential to extract up to 100 gait-related parameters, and we expect our contribution will further support developments in the fields of wearable sensors, digital health, and remote monitoring in clinical trials. Full article

(This article belongs to the Special Issue Wearable Sensors for Assessment of Gait in Older Adults)

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12 pages, 2023 KiB

Open AccessArticle

Unsupervised Assessment of Balance and Falls Risk Using a Smartphone and Machine Learning

by Barry R. Greene, Killian McManus, Lilian Genaro Motti Ader and Brian Caulfield

Sensors 2021, 21(14), 4770; https://doi.org/10.3390/s21144770 - 13 Jul 2021

Cited by 10 | Viewed by 4587

Abstract

Assessment of health and physical function using smartphones (mHealth) has enormous potential due to the ubiquity of smartphones and their potential to provide low cost, scalable access to care as well as frequent, objective measurements, outside of clinical environments. Validation of the algorithms and outcome measures used by mHealth apps is of paramount importance, as poorly validated apps have been found to be harmful to patients. Falls are a complex, common and costly problem in the older adult population. Deficits in balance and postural control are strongly associated with falls risk. Assessment of balance and falls risk using a validated smartphone app may lessen the need for clinical assessments which can be expensive, requiring non-portable equipment and specialist expertise. This study reports results for the real-world deployment of a smartphone app for self-directed, unsupervised assessment of balance and falls risk. The app relies on a previously validated algorithm for assessment of balance and falls risk; the outcome measures employed were trained prior to deployment on an independent data set. Results for a sample of 594 smartphone assessments from 147 unique phones show a strong association between self-reported falls history and the falls risk and balance impairment scores produced by the app, suggesting they may be clinically useful outcome measures. In addition, analysis of the quantitative balance features produced seems to suggest that unsupervised, self-directed assessment of balance in the home is feasible. Full article

(This article belongs to the Special Issue Wearable Sensors for Assessment of Gait in Older Adults)

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