Structure-Agnostic Gait Cycle Segmentation for In-Home Gait Health Monitoring Through Footstep-Induced Structural Vibrations

Dong, Yiwen; Noh, Hae Young

doi:10.1007/978-3-031-36663-5_8

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…5f). The difference across sensors can come from three sources, summarized from a few existing studies [13,14]: 1) floor heterogeneity during vibration wave propagation, 2) locationdependent environmental noises, and 3) hardware variations (e.g., fabrication quality, sensor-floor coupling). In our study, we minimized the environmental noises and hardware variations by inspecting the surroundings and selecting sensors with consistent signals during preliminary tests on pure noise data.…”

Section: Discussion On the Effects Of MD Types And Sensor Locationsmentioning

confidence: 99%

“…First, we apply a wavelet transform using the Morse wavelet (an efficient and commonly used wavelet type) to obtain the time-frequency relationship in the vibration signals. Then, we isolate the frequency range where the natural frequency of the floor lies (typically 5-25 Hz [14,15]) to filter out noises caused by mechanical device disturbances from the environment. The processed wavelet coefficient series is then processed through an anomaly detection and peak picking algorithm to extract the foot strike time and the number of footsteps.…”

Section: Physics-informed Gait Pattern Extraction From Floor Vibratio...mentioning

confidence: 99%

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Ambient Floor Vibration Sensing Advances Accessibility of Functional Gait Assessment for Children with Muscular Dystrophies

Dong

Iammarino

Liu

et al. 2023

Preprint

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Muscular dystrophies (MD) are a group of genetic neuromuscular disorders that cause progressive weakness and loss of muscles over time, influencing 1 in 3500 to 5000 children worldwide. New and exciting treatment options have led to a critical need for a clinical post-marketing surveillance tool to confirm the efficacy and safety of these treatments after individuals receive them in a commercial setting. For MDs, functional gait assessment is a common approach to evaluate the efficacy of the treatments because muscle weakness is reflected in individuals' walking patterns. However, there is little incentive for the family to continue to travel for such assessments due to the lack of access to specialty centers. While various existing sensing devices, such as cameras, force plates, and wearables can assess gait at home, they are limited by privacy concerns, area of coverage, and discomfort in carrying devices, which is not practical for long-term, continuous monitoring in daily settings. In this study, we introduce a novel functional gait assessment system using ambient floor vibrations, which is non-invasive and scalable, requiring only low-cost and sparsely deployed geophone sensors attached to the floor surface, suitable for in-home usage. Our system captures floor vibrations generated by footsteps from patients while they walk around and analyzes such vibrations to extract essential gait health information. To enhance interpretability and reliability under various sensing scenarios, we translate the signal patterns of floor vibration to pathological gait patterns related to MD, and develop a hierarchical learning algorithm that aggregates insights from individual footsteps to estimate a person's overall gait performance. When evaluate through real-world experiments with 36 subjects (including 15 patients with MD), our floor vibration sensing system achieves a 94.8% accuracy in predicting functional gait stages for patients with MD. Our approach enables accurate, accessible, and scalable functional gait assessment, bringing MD progressive tracking into real life.

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Section: Discussion On the Effects Of MD Types And Sensor Locationsmentioning

confidence: 99%

Section: Physics-informed Gait Pattern Extraction From Floor Vibratio...mentioning

confidence: 99%

Ambient Floor Vibration Sensing Advances Accessibility of Functional Gait Assessment for Children with Muscular Dystrophies

Dong

Iammarino

Liu

et al. 2023

Preprint

Self Cite

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“…Henceforth, the terms vibro-localization and vibro-measurements will refer to such localization techniques and the measurements used in these techniques, respectively. Vibro-localization techniques facilitate a myriad of applications ranging from smart home monitoring and event classification [1][2][3][4][5] to human gait assessment [6][7][8][9][10][11][12] and occupant identification and tracking [13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Sensor Stochastic Energy-Based Vibro-Localization Technique with Byzantine Sensor Elimination

Ambarkutuk,

Alajlouni,

Tarazaga

et al. 2023

Sensors

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This paper presents an occupant localization technique that determines the location of individuals in indoor environments by analyzing the structural vibrations of the floor caused by their footsteps. Structural vibration waves are difficult to measure as they are influenced by various factors, including the complex nature of wave propagation in heterogeneous and dispersive media (such as the floor) as well as the inherent noise characteristics of sensors observing the vibration wavefronts. The proposed vibration-based occupant localization technique minimizes the errors that occur during the signal acquisition time. In this process, the likelihood function of each sensor—representing where the occupant likely resides in the environment—is fused to obtain a consensual localization result in a collective manner. In this work, it becomes evident that the above sources of uncertainties can render certain sensors deceptive, commonly referred to as “Byzantines.” Because the ratio of Byzantines among the set sensors defines the success of the collective localization results, this paper introduces a Byzantine sensor elimination (BSE) algorithm to prevent the unreliable information of Byzantine sensors from affecting the location estimations. This algorithm identifies and eliminates sensors that generate erroneous estimates, preventing the influence of these sensors on the overall consensus. To validate and benchmark the proposed technique, a set of previously conducted controlled experiments was employed. The empirical results demonstrate the proposed technique’s significant improvement (3~0%) over the baseline approach in terms of both accuracy and precision.

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Ubiquitous Gait Analysis through Footstep-Induced Floor Vibrations

Dong,

Noh

2024

Sensors

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Quantitative analysis of human gait is critical for the early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson’s disease, stroke, and cerebral palsy. Gait analysis typically involves estimating gait characteristics, such as spatiotemporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates but are limited in operational requirements when applied in daily life, such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing floor vibrations generated by human footsteps using vibration sensors mounted on the floor surface. Our approach is low-cost, non-intrusive, and perceived as privacy-friendly, making it suitable for continuous gait health monitoring in daily life. Our algorithm estimates various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step time, stride time, stance time, swing time, double-support time, and single-support time), spatial parameters (step length, width, angle, and stride length), and extracts gait health indicators (cadence/walking speed, left–right symmetry, gait balance, and initial contact types). The main challenge we addressed in this paper is the effect of different floor types on the resultant vibrations. We develop floor-adaptive algorithms to extract features that are generalizable to various practical settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively.

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Structure-Agnostic Gait Cycle Segmentation for In-Home Gait Health Monitoring Through Footstep-Induced Structural Vibrations

Cited by 3 publications

References 28 publications

Ambient Floor Vibration Sensing Advances Accessibility of Functional Gait Assessment for Children with Muscular Dystrophies

Ambient Floor Vibration Sensing Advances Accessibility of Functional Gait Assessment for Children with Muscular Dystrophies

A Multi-Sensor Stochastic Energy-Based Vibro-Localization Technique with Byzantine Sensor Elimination

Ubiquitous Gait Analysis through Footstep-Induced Floor Vibrations

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