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Wearable Sensors for Postural Stability and Fall Risk Analyses

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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: 15 December 2024 | Viewed by 6889

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Prof. Dr. Tadayoshi Asaka

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Guest Editor

Professor Emeritus at The Hokkaido University, Department of Rehabilitation Science, Hokkaido University, Sapporo 060-0808, Japan
Interests: postural control; motor control; rehabilitation; neurophysiotherapy

Dr. Naoya Hasegawa

E-Mail Website
Co-Guest Editor

Department of Rehabilitation Sciences, Faculty of Health Sciences, Hokkaido University, Sapporo 060-0808, Japan
Interests: motor control; neurological physical therapy; biofeedback training; movement disorders

Special Issue Information

Dear Colleagues,

Recent advances in wearable sensors have demonstrated the ability to collect objective measures of postural stability outside of the laboratory. Wearable sensors are small and low-cost, and require less time than force plate or kinematic analysis systems. Previous studies have shown the validity of wearable sensors to quantify postural stability during dynamic and static postural control tasks, as well as fall risk analysis. In addition to characterizing postural stability, wearable sensors are used for the assessment of training and effective training methods with technology-based approaches, especially in the rehabilitation area.

Better understanding and novel treatments around postural instability and falls in older adults, as well as individuals with neurological disorders, continue to grow to rehabilitation efficacy.

The aims of this Special Issue are therefore to broaden the discussion of recent advances, technologies, solutions, applications, and new challenges in the field of postural control and motor learning. This Special Issue welcomes research not only addressing older adults and neurological disorders but also healthy subjects and sports athletes. In this Special Issue we welcome the submission of original research, review, case report, and short articles, among others.

If you want to learn more information or need any advice, you can contact the Special Issue Editor Andrea Chen via <[email protected]> directly.

Prof. Dr. Tadayoshi Asaka
Dr. Naoya Hasegawa
Guest Editors

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Keywords

fall risk
motor learning
postural control
postural stability
wearable sensors

Published Papers (4 papers)

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Research

13 pages, 1305 KiB

Open AccessArticle

by Shuaijie Wang, Tuan Khang Nguyen and Tanvi Bhatt

Sensors 2023, 23(12), 5536; https://doi.org/10.3390/s23125536 - 13 Jun 2023

Cited by 1 | Viewed by 1408

Abstract

Trip perturbations are proposed to be a leading cause of falls in older adults. To prevent trip-falls, trip-related fall risk should be assessed and subsequent task-specific interventions improving recovery skills from forward balance loss should be provided to the individuals at risk of trip-fall. Therefore, this study aimed to develop trip-related fall risk prediction models from one’s regular gait pattern using machine-learning approaches. A total of 298 older adults (≥60 years) who experienced a novel obstacle-induced trip perturbation in the laboratory were included in this study. Their trip outcomes were classified into three classes: no-falls (n = 192), falls with lowering strategy (L-fall, n = 84), and falls with elevating strategy (E-fall, n = 22). A total of 40 gait characteristics, which could potentially affect trip outcomes, were calculated in the regular walking trial before the trip trial. The top 50% of features (n = 20) were selected to train the prediction models using a relief-based feature selection algorithm, and an ensemble classification model was selected and trained with different numbers of features (1–20). A ten-times five-fold stratified method was utilized for cross-validation. Our results suggested that the trained models with different feature numbers showed an overall accuracy between 67% and 89% at the default cutoff and between 70% and 94% at the optimal cutoff. The prediction accuracy roughly increased along with the number of features. Among all the models, the one with 17 features could be considered the best model with the highest AUC of 0.96, and the model with 8 features could be considered the optimal model, which had a comparable AUC of 0.93 and fewer features. This study revealed that gait characteristics in regular walking could accurately predict the trip-related fall risk for healthy older adults, and the developed models could be a helpful assessment tool to identify the individuals at risk of trip-falls. Full article

(This article belongs to the Special Issue Wearable Sensors for Postural Stability and Fall Risk Analyses)

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10 pages, 1075 KiB

Open AccessArticle

Combination of Clinical and Gait Measures to Classify Fallers and Non-Fallers in Parkinson’s Disease

by Hayslenne A. G. O. Araújo, Suhaila M. Smaili, Rosie Morris, Lisa Graham, Julia Das, Claire McDonald, Richard Walker, Samuel Stuart and Rodrigo Vitório

Sensors 2023, 23(10), 4651; https://doi.org/10.3390/s23104651 - 11 May 2023

Cited by 1 | Viewed by 1706

Abstract

Although the multifactorial nature of falls in Parkinson’s disease (PD) is well described, optimal assessment for the identification of fallers remains unclear. Thus, we aimed to identify clinical and objective gait measures that best discriminate fallers from non-fallers in PD, with suggestions of optimal cutoff scores. METHODS: Individuals with mild-to-moderate PD were classified as fallers (n = 31) or non-fallers (n = 96) based on the previous 12 months’ falls. Clinical measures (demographic, motor, cognitive and patient-reported outcomes) were assessed with standard scales/tests, and gait parameters were derived from wearable inertial sensors (Mobility Lab v2); participants walked overground, at a self-selected speed, for 2 min under single and dual-task walking conditions (maximum forward digit span). Receiver operating characteristic curve analysis identified measures (separately and in combination) that best discriminate fallers from non-fallers; we calculated the area under the curve (AUC) and identified optimal cutoff scores (i.e., point closest-to-(0,1) corner). RESULTS: Single gait and clinical measures that best classified fallers were foot strike angle (AUC = 0.728; cutoff = 14.07°) and the Falls Efficacy Scale International (FES-I; AUC = 0.716, cutoff = 25.5), respectively. Combinations of clinical + gait measures had higher AUCs than combinations of clinical-only or gait-only measures. The best performing combination included the FES-I score, New Freezing of Gait Questionnaire score, foot strike angle and trunk transverse range of motion (AUC = 0.85). CONCLUSION: Multiple clinical and gait aspects must be considered for the classification of fallers and non-fallers in PD. Full article

(This article belongs to the Special Issue Wearable Sensors for Postural Stability and Fall Risk Analyses)

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12 pages, 1648 KiB

Open AccessArticle

Effects of the Loss of Binocular and Motion Parallax on Static Postural Stability

by Keita Ishikawa, Naoya Hasegawa, Ayane Yokoyama, Yusuke Sakaki, Hiromasa Akagi, Ami Kawata, Hiroki Mani and Tadayoshi Asaka

Sensors 2023, 23(8), 4139; https://doi.org/10.3390/s23084139 - 20 Apr 2023

Viewed by 1396

Abstract

Depth information is important for postural stability and is generated by two visual systems: binocular and motion parallax. The effect of each type of parallax on postural stability remains unclear. We investigated the effects of binocular and motion parallax loss on static postural stability using a virtual reality (VR) system with a head-mounted display (HMD). A total of 24 healthy young adults were asked to stand still on a foam surface fixed on a force plate. They wore an HMD and faced a visual background in the VR system under four visual test conditions: normal vision (Control), absence of motion parallax (Non-MP)/binocular parallax (Non-BP), and absence of both motion and binocular parallax (Non-P). The sway area and velocity in the anteroposterior and mediolateral directions of the center-of-pressure displacements were measured. All postural stability measurements were significantly higher under the Non-MP and Non-P conditions than those under the Control and Non-BP conditions, with no significant differences in the postural stability measurements between the Control and Non-BP conditions. In conclusion, motion parallax has a more prominent effect on static postural stability than binocular parallax, which clarifies the underlying mechanisms of postural instability and informs the development of rehabilitation methods for people with visual impairments. Full article

(This article belongs to the Special Issue Wearable Sensors for Postural Stability and Fall Risk Analyses)

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15 pages, 2914 KiB

Open AccessArticle

Trunk Velocity Changes in Response to Physical Perturbations Are Potential Indicators of Gait Stability

by Farahnaz Fallahtafti, Sjoerd Bruijn, Arash Mohammadzadeh Gonabadi, Mohammad Sangtarashan, Julie Blaskewicz Boron, Carolin Curtze, Ka-Chun Siu, Sara A. Myers and Jennifer Yentes

Sensors 2023, 23(5), 2833; https://doi.org/10.3390/s23052833 - 5 Mar 2023

Cited by 1 | Viewed by 1426

Abstract

Response to challenging situations is important to avoid falls, especially after medial perturbations, which require active control. There is a lack of evidence on the relationship between the trunk’s motion in response to perturbations and gait stability. Eighteen healthy adults walked on a treadmill at three speeds while receiving perturbations of three magnitudes. Medial perturbations were applied by translating the walking platform to the right at left heel contact. Trunk velocity changes in response to the perturbation were calculated and divided into the initial and the recovery phases. Gait stability after a perturbation was assessed using the margin of stability (MOS) at the first heel contact, MOS mean, and standard deviation for the first five strides after the perturbation onset. Faster speed and smaller perturbations led to a lower deviation of trunk velocity from the steady state, which can be interpreted as an improvement in response to the perturbation. Recovery was quicker after small perturbations. The MOS mean was associated with the trunk’s motion in response to perturbations during the initial phase. Increasing walking speed may increase resistance to perturbations, while increasing the magnitude of perturbation leads to greater trunk motions. MOS is a useful marker of resistance to perturbations. Full article

(This article belongs to the Special Issue Wearable Sensors for Postural Stability and Fall Risk Analyses)

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