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Gait Analysis Based on Sensing Technology in Populations at Risk of Falls

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

Deadline for manuscript submissions: closed (10 August 2024) | Viewed by 4134

Special Issue Editors

Dr. Sheree Hurn

E-Mail Website
Guest Editor
Faculty of Health, School of Clinical Sciences, Queensland University of Technology (QUT), Kelvin Grove 4059, Australia
Interests: lower limb musculoskeletal disorders; rehabilitation; gait analysis; footwear
Dr. Anna Hatton

E-Mail Website
Guest Editor
School of Health and Rehabilitation Sciences, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
Interests: footwear devices; postural control; falls prevention; cutaneous sensation; feet; gait analysis; neurological diseases; ageing
Dr. Wolbert Van den Hoorn

E-Mail Website
Guest Editor
Faculty of Health, School of Exercise and Nutrition Sciences, Queensland University of Technology (QUT), Kelvin Grove 4059, Australia
Interests: gait analysis; postural control; low back pain

Special Issue Information

Dear Colleagues,

Analysis of a person’s walking gait can provide many insights into their ability to control their centre of mass smoothly during ambulation. Gait patterns often become irregular or unstable in populations at increased risk of accidental falls, such as older adults or those with neurological conditions, due to declining sensory and motor function. Therefore, accurate assessment of gait stability and symmetry is important in both clinical and research settings that are focused on fall risk assessment and fall prevention. With the rate of development of gait analysis technologies, it is not always clear to clinicians and researchers which technologies will provide the most relevant and accurate gait information.  Accuracy and repeatability are of utmost importance in clinical and research applications, particularly where the risk of falls is being assessed or the effectiveness of interventions for preventing falls is being evaluated. Therefore, it is the aim of this Special Issue to draw together the latest literature applying gait analysis technologies in populations at increased risk of falls.

Dr. Sheree Hurn
Dr. Anna Hatton
Dr. Wolbert Van den Hoorn
Guest Editors

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Keywords

  • gait analysis
  • gait stability
  • falls risk
  • falls prevention

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Published Papers (3 papers)

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Research

27 pages, 1648 KiB  
Article
Relationship of Community Mobility, Vital Space, and Faller Status in Older Adults
by Diego Robles Cruz, Andrea Lira Belmar, Anthony Fleury, Méline Lam, Rossana M. Castro Andrade, Sebastián Puebla Quiñones and Carla Taramasco Toro
Sensors 2024, 24(23), 7651; https://doi.org/10.3390/s24237651 - 29 Nov 2024
Viewed by 418
Abstract
Community mobility, encompassing both active (e.g., walking) and passive (e.g., driving) transport, plays a crucial role in maintaining autonomy and social interaction among older adults. This study aimed to quantify community mobility in older adults and explore the relationship between GPS- and accelerometer-derived [...] Read more.
Community mobility, encompassing both active (e.g., walking) and passive (e.g., driving) transport, plays a crucial role in maintaining autonomy and social interaction among older adults. This study aimed to quantify community mobility in older adults and explore the relationship between GPS- and accelerometer-derived metrics and fall risk. Methods: A total of 129 older adults, with and without a history of falls, were monitored over an 8 h period using GPS and accelerometer data. Three experimental conditions were evaluated: GPS data alone, accelerometer data alone, and a combination of both. Classification models, including Random Forest (RF), Support Vector Machines (SVMs), and K-Nearest Neighbors (KNN), were employed to classify participants based on their fall history. Results: For GPS data alone, RF achieved 74% accuracy, while SVM and KNN reached 67% and 62%, respectively. Using accelerometer data, RF achieved 95% accuracy, and both SVM and KNN achieved 90%. Combining GPS and accelerometer data improved model performance, with RF reaching 97% accuracy, SVM achieving 95%, and KNN 87%. Conclusion: The integration of GPS and accelerometer data significantly enhances the accuracy of distinguishing older adults with and without a history of falls. These findings highlight the potential of sensor-based approaches for accurate fall risk assessment in community-dwelling older adults. Full article
(This article belongs to the Special Issue Gait Analysis Based on Sensing Technology in Populations at Risk of Falls)
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16 pages, 3956 KiB  
Article
Daily-Life Walking Speed, Quality and Quantity Derived from a Wrist Motion Sensor: Large-Scale Normative Data for Middle-Aged and Older Adults
by Lloyd L. Y. Chan, Stephen R. Lord and Matthew A. Brodie
Sensors 2024, 24(16), 5159; https://doi.org/10.3390/s24165159 - 10 Aug 2024
Viewed by 1314
Abstract
Walking is crucial for independence and quality of life. This study leverages wrist-worn sensor data from UK Biobank participants to establish normative daily-life walking data, stratified by age and sex, to provide benchmarks for research and clinical practice. The Watch Walk digital biomarkers [...] Read more.
Walking is crucial for independence and quality of life. This study leverages wrist-worn sensor data from UK Biobank participants to establish normative daily-life walking data, stratified by age and sex, to provide benchmarks for research and clinical practice. The Watch Walk digital biomarkers were developed, validated, and applied to 92,022 participants aged 45–79 who wore a wrist sensor for at least three days. Normative data were collected for daily-life walking speed, step-time variability, step count, and 17 other gait and sleep biomarkers. Test–retest reliability was calculated, and associations with sex, age, self-reported walking pace, and mobility problems were examined. Population mean maximal and usual walking speeds were 1.49 and 1.15 m/s, respectively. The daily step count was 7749 steps, and step regularity was 65%. Women walked more regularly but slower than men. Walking speed, step count, longest walk duration, and step regularity decreased with age. Walking speed is associated with sex, age, self-reported pace, and mobility problems. Test–retest reliability was good to excellent (ICC ≥ 0.80). This study provides large-scale normative data and benchmarks for wrist-sensor-derived digital gait and sleep biomarkers from real-world data for future research and clinical applications. Full article
(This article belongs to the Special Issue Gait Analysis Based on Sensing Technology in Populations at Risk of Falls)
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14 pages, 1798 KiB  
Article
The Impact of Dual-Tasks and Disease Severity on Posture, Gait, and Functional Mobility among People Living with Dementia in Residential Care Facilities: A Pilot Study
by Deborah A Jehu, Ryan Langston, Richard Sams, Lufei Young, Mark Hamrick, Haidong Zhu and Yanbin Dong
Sensors 2024, 24(9), 2691; https://doi.org/10.3390/s24092691 - 24 Apr 2024
Viewed by 1778
Abstract
Gait speed and timed-up-and-go (TUG) predict cognitive decline, falls, and mortality. Dual-tasks may be useful in cognitive screening among people living with dementia (PWD), but more evidence is needed. This cross-sectional study aimed to compare single- and dual-task performance and determine the influence [...] Read more.
Gait speed and timed-up-and-go (TUG) predict cognitive decline, falls, and mortality. Dual-tasks may be useful in cognitive screening among people living with dementia (PWD), but more evidence is needed. This cross-sectional study aimed to compare single- and dual-task performance and determine the influence of dementia severity on dual-task performance and interference. Thirty PWD in two residential care facilities (Age: 81.3 ± 7.1 years; Montreal Cognitive Assessment: 10.4 ± 6.0 points) completed two trials of single- (feet apart) and dual-task posture (feet apart while counting backward), single- (walk 4 m) and dual-task gait (walk 4m while naming words), and single- (timed-up-and-go (TUG)), and dual-task functional mobility (TUG while completing a category task) with APDM inertial sensors. Dual-tasks resulted in greater sway frequency, jerk, and sway area; slower gait speed; greater double limb support; shorter stride length; reduced mid-swing elevation; longer TUG duration; reduced turn angle; and slower turn velocity than single-tasks (ps < 0.05). Dual-task performance was impacted (reduced double limb support, greater mid-swing elevation), and dual-task interference (greater jerk, faster gait speed) was related to moderate-to-severe compared to mild PWD. Moderate-to-severe PWD had poorer dynamic stability and a reduced ability to appropriately select a cautious gait during dual-tasks than those with mild PWD, indicating the usefulness of dual-tasks for cognitive screening. Full article
(This article belongs to the Special Issue Gait Analysis Based on Sensing Technology in Populations at Risk of Falls)
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