A Robust Automatic Gait Monitoring Approach Using a Single Imu for Home-Based Applications

Ghobadi, Mostafa; Esfahani, Ehsan T.

doi:10.1142/s0219519417500774

Cited by 12 publications

(2 citation statements)

References 39 publications

(64 reference statements)

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“…For instance, errors of ≤3° and correlation coefficients of ≥0.8 were obtained while comparing joint angles assessed using both MIMU-based methods and optoelectronic systems [26,27,28]. Other researchers performed a trajectory reconstruction of feet and the BCOM using a small set of inertial units [19,29,30,31,32,33,34], achieving displacement errors of ≈3 cm between the MIMUs and camera-based systems. Finally, a recent study considering the reconstruction of the whole lower body kinematics of a gait reported a correlation coefficient greater than 0.9 when the outcome of the MIMU-based estimation was compared to that of a camera-based system.…”

Section: Introductionmentioning

confidence: 99%

Ambulatory Assessment of the Dynamic Margin of Stability Using an Inertial Sensor Network

Guaitolini

Aprigliano

Mannini

et al. 2019

Sensors

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Loss of stability is a precursor to falling and therefore represents a leading cause of injury, especially in fragile people. Thus, dynamic stability during activities of daily living (ADLs) needs to be considered to assess balance control and fall risk. The dynamic margin of stability (MOS) is often used as an indicator of how the body center of mass is located and moves relative to the base of support. In this work, we propose a magneto-inertial measurement unit (MIMU)-based method to assess the MOS of a gait. Six young healthy subjects were asked to walk on a treadmill at different velocities while wearing MIMUs on their lower limbs and pelvis. We then assessed the MOS by computing the lower body displacement with respect to the leading inverse kinematics approach. The results were compared with those obtained using a camera-based system in terms of root mean square deviation (RMSD) and correlation coefficient (ρ). We obtained a RMSD of ≤1.80 cm and ρ ≥ 0.85 for each walking velocity. The findings revealed that our method is comparable to camera-based systems in terms of accuracy, suggesting that it may represent a strategy to assess stability during ADLs in unstructured environments.

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Section: Introductionmentioning

confidence: 99%

Ambulatory Assessment of the Dynamic Margin of Stability Using an Inertial Sensor Network

Guaitolini

Aprigliano

Mannini

et al. 2019

Sensors

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“…Regarding the use of a minimum number of sensors, a lot of work has been done by using a single inertial sensor located on the pelvis or foot mostly for activity recognition [22], [8], [23], estimation of temporal events and parameters [24], [25], [26]. For person identification, a single sensor on the pelvis [27], [28], foot [19] and ankles [29] have been illustrated.…”

Section: Introductionmentioning

confidence: 99%

Gait-based Human Identification through Minimum Gait-phases and Sensors

Arshad¹,

Jung²,

Park³

et al. 2021

Preprint

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The incredible pace at which the world's elderly population is growing will put severe burdens on current healthcare systems and resources. To alleviate this concern the health care systems must rely on the transformation of eldercare and old homes to use Ambient Assisted Living (AAL). Human identification is one of the most common and critical tasks for condition monitoring, human-machine interaction, and providing assistive services in such environments. Recently, human gait has gained new attention as a biometric for identification to achieve contactless identification from a distance robust to physical appearances. However, an important aspect of gait identification through wearables and image-based systems alike is accurate identification when limited information is available for example, when only a fraction of the whole gait cycle or only a part of the subject's body is visible. In this paper, we present a gait identification technique based on temporal and descriptive statistic parameters of different gait phases as the features and we investigate the performance of using only single gait phases for the identification task using a minimum number of sensors. Gait data were collected from 60 individuals through pelvis and foot sensors. Six different machine learning algorithms were used for identification. It was shown that it is possible to achieve high accuracy of over 95.5% by monitoring a single phase of the whole gait cycle through only a single sensor. It was also shown that the proposed methodology could be used to achieve 100% identification accuracy when the whole gait cycle was monitored through pelvis and foot sensors combined. The ANN was found to be more robust to less number of data features compared to SVM and was concluded as the best machine algorithm for the purpose.

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The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review

Swain,

McNarry,

Runacres

et al. 2023

Sports Med

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Background Movement quality is typically assessed by drawing comparisons against predetermined movement standards. Movements are often discretely scored or labelled against pre-set criteria, though movement quality can also be evaluated using motion-related measurements (e.g., spatio-temporal parameters and kinematic variables). Wearable technology has the potential to measure and assess movement quality and offer valuable, practical feedback. Objectives A systematic approach was taken to examine the benefits associated with multi-sensor and multiple wearable-device usage, compared with unimodal applications, when assessing movement quality. Consequently, this review considers the additional variables and features that could be obtained through multi-sensor devices for use in movement analyses. Processing methods and applications of the various configurations were also explored. Methods Articles were included within this review if they were written in English, specifically studied the use of wearable sensors to assess movement quality, and were published between January 2010 and December 2022. Of the 62,635 articles initially identified, 27 papers were included in this review. The quality of included studies was determined using a modified Downs and Black checklist, with 24/27 high quality. Results Fifteen of the 27 included studies used a classification approach, 11 used a measurement approach, and one used both methods. Accelerometers featured in all 27 studies, in isolation (n = 5), with a gyroscope (n = 9), or with both a gyroscope and a magnetometer (n = 13). Sampling frequencies across all studies ranged from 50 to 200 Hz. The most common classification methods were traditional feature-based classifiers (n = 5) and support vector machines (SVM; n = 5). Sensor fusion featured in six of the 16 classification studies and nine of the 12 measurement studies, with the Madgwick algorithm most prevalent (n = 7). Conclusions This systematic review highlights the differences between the applications and processing methods associated with the use of unimodal and multi-sensor wearable devices when assessing movement quality. Further, the use of multiple devices appears to increase the feasibility of effectively assessing holistic movements, while multi-sensor devices offer the ability to obtain more output metrics.

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A Robust Automatic Gait Monitoring Approach Using a Single Imu for Home-Based Applications

Cited by 12 publications

References 39 publications

Ambulatory Assessment of the Dynamic Margin of Stability Using an Inertial Sensor Network

Ambulatory Assessment of the Dynamic Margin of Stability Using an Inertial Sensor Network

Gait-based Human Identification through Minimum Gait-phases and Sensors

The Role of Multi-Sensor Measurement in the Assessment of Movement Quality: A Systematic Review

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