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18 pages, 891 KiB

Open AccessArticle

Prediction of Margin of Gait Stability by Using Six-DoF Motion of Pelvis

by Tomohito Kuroda, Shogo Okamoto and Yasuhiro Akiyama

Sensors 2024, 24(22), 7342; https://doi.org/10.3390/s24227342 - 18 Nov 2024

Viewed by 276

Unstable gait increases the risk of falls, posing a significant danger, particularly for frail older adults. The margin of stability (MoS) is a quantitative index that reflects the risk of falling due to postural imbalance in both the anterior-posterior and mediolateral directions during [...] Read more.

Unstable gait increases the risk of falls, posing a significant danger, particularly for frail older adults. The margin of stability (MoS) is a quantitative index that reflects the risk of falling due to postural imbalance in both the anterior-posterior and mediolateral directions during walking. Although MoS is a reliable indicator, its computation typically requires specialized equipment, such as motion capture systems, limiting its application to laboratory settings. To address this limitation, we propose a method for estimating MoS using time-series data from the translational and angular velocities of a single body segment—the pelvis. By applying principal motion analysis to process the multivariate time-series data, we successfully estimated MoS. Our results demonstrate that the estimated MoS in the mediolateral direction achieved an RMSE of 0.88 cm and a correlation coefficient of 0.72 with measured values, while in the anterior-posterior direction, the RMSE was 0.73 cm with a correlation coefficient of 0.87. These values for the mediolateral direction are better than those obtained in previous studies using only the three translational velocity components of the pelvis, whereas the values for the anterior direction are comparable to previous approaches. Our findings suggest that MoS can be reliably estimated using six-axial kinematic data of the pelvis, offering a more accessible method for assessing gait stability. Full article

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

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14 pages, 10386 KiB

Open AccessArticle

Utilizing Inertial Measurement Units for Detecting Dynamic Stability Variations in a Multi-Condition Gait Experiment

by Yasuhirio Akiyama, Kyogo Kazumura, Shogo Okamoto and Yoji Yamada

Sensors 2024, 24(21), 7044; https://doi.org/10.3390/s24217044 - 31 Oct 2024

Viewed by 771

Abstract

This study proposes a wearable gait assessment method using inertial measurement units (IMUs) to evaluate gait ability in daily environments. By focusing on the estimation of the margin of stability (MoS), a key kinematic stability parameter, a method using a convolutional neural network, [...] Read more.

This study proposes a wearable gait assessment method using inertial measurement units (IMUs) to evaluate gait ability in daily environments. By focusing on the estimation of the margin of stability (MoS), a key kinematic stability parameter, a method using a convolutional neural network, was developed to estimate the MoS from IMU acceleration time-series data. The relationship between MoS and other stability indices, such as the Lyapunov exponent and the multi-site time-series (MSTS) index, using data from five IMU sensors placed on various body parts was also examined. To simulate diverse gait conditions, treadmill speed was varied, and a knee–ankle–foot orthosis was used to restrict left knee extension, inducing gait asymmetry. The model achieved over 90% accuracy in classifying MoS in both forward and lateral directions using three-axis acceleration data from the IMUs. However, the correlation between MoS and the Lyapunov exponent or MSTS index was weak, suggesting that these indices may capture different aspects of gait stability. Full article

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

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20 pages, 6142 KiB

Open AccessArticle

One-Dimensional Motion Representation for Standing/Sitting and Their Transitions

by Geunho Lee, Yusuke Hayakawa, Takuya Watanabe and Chunhe Li

Sensors 2024, 24(21), 6967; https://doi.org/10.3390/s24216967 - 30 Oct 2024

Viewed by 1051

Abstract

In everyday life, people often stand up and sit down. Unlike young, able-bodied individuals, older adults and those with disabilities usually stand up or sit down slowly, often pausing during the transition. It is crucial to design interfaces that accommodate these movements. Additionally, [...] Read more.

In everyday life, people often stand up and sit down. Unlike young, able-bodied individuals, older adults and those with disabilities usually stand up or sit down slowly, often pausing during the transition. It is crucial to design interfaces that accommodate these movements. Additionally, in public settings, protecting personal information is essential. Addressing these considerations, this paper presents a distance-based representation scheme for the motions of standing up and sitting down. This proposed scheme identifies both standing and sitting positions, as well as the transition process between these two states. Our scheme is based solely on the variations in distance between a sensor and the surfaces of the human body during these movements. Specifically, the proposed solution relies on distance as input, allowing for the use of a proximity sensor without the need for cameras or additional wearable sensor attachments. A single microcontroller is adequate for this purpose. Our contribution highlights that using a proximity sensor broadens the applicability of the approach while ensuring that personal information remains secure. Additionally, the scheme alleviates users’ mental burden, particularly regarding privacy concerns. Extensive experiments were performed on 58 subjects, including 19 people over the age of 70, to verify the effectiveness of the proposed solution, and the results are described in detail. Full article

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

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15 pages, 1166 KiB

Open AccessArticle

Snapshot of Fall Prevention in Patients Referred to a Neurorehabilitation Unit: A Feasibility Study on the Use of an Airbag Device

by Laura Comini, Adriana Olivares, Lucia Marchina, Adrian Suruniuc, Fabio Vanoglio, Gian Pietro Bonometti, Alberto Luisa and Giacomo Corica

Sensors 2024, 24(19), 6272; https://doi.org/10.3390/s24196272 - 27 Sep 2024

Viewed by 498

Abstract

Active wearable devices such as protective smart belts have been proposed to reduce hip impact in the event of a fall. This study primarily evaluated the feasibility and acceptance of a specific protective belt among selected patients identified as being at risk of [...] Read more.

Active wearable devices such as protective smart belts have been proposed to reduce hip impact in the event of a fall. This study primarily evaluated the feasibility and acceptance of a specific protective belt among selected patients identified as being at risk of falling who were admitted to an ICS Maugeri Neurorehabilitation Unit from September 2022 to April 2023. According to previous institutional observations, the device was worn between the 6th and 21st days of recovery. Out of 435 admitted patients, 118 were considered eligible, but 101 declined to participate (about 50% refused to wear the belt without first trying it on; the other 50% found it too heavy or difficult to manage). Among the 17 patients who accepted (users), 9 used the belt correctly. The remaining eight patients refused to wear it after 24 h, due to discomfort. Out of 435 patients admitted, we observed at least one fall in 49 patients, of whom 5 were eligible patients; 1 was a user who had quickly refused to use the belt and fell with mild damage. Two non-eligible patients and one eligible non-user patient experienced falls resulting in hip fractures; only in the latter case could the use of the belt have limited the damage to the hip. Difficulties in recruiting patients and low acceptance of the proposed intervention present further challenges. Full article

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

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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 4 | Viewed by 2041

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 [...] Read more.

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 2 | Viewed by 2216

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 [...] Read more.

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 1896

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 [...] Read more.

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 1851

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 [...] Read more.

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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