The identification of vertical velocity profiles using an inertial sensor to investigate pre-impact detection of falls

Bourke, Alan K.; O’Donovan, Karol; ÓLaighin, Gearóid

doi:10.1016/j.medengphy.2007.12.003

Cited by 130 publications

(90 citation statements)

References 12 publications

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“…The largest RMSD values were found for HW, even indicating a significant difference for vertical CoM position and velocity, and upper body angular velocity. One of the fall indicators, the vertical velocity of the center of mass, of which the threshold was set at −1.3 m/s by Bourke et al (2008), showed RMSD values that were considerably lower (0.05-0.20 m/s) than the threshold value. The RMSD values found with a 95% confidence interval of vertical CoM position and upper body orientation were, due to their small magnitude, considered to be practically not important.…”

Section: Figure 7 | Coefficients Of Determination Rmentioning

confidence: 99%

“…A limitation of IMUs is that they do not allow to directly measure one highly specific predictor for imminent falls: linear velocity in vertical direction. This has proven to be a valuable source of information both in literature (Wu, 2000;Bourke et al, 2008) and in our own experience with the FLOAT rehabilitation robot of Vallery et al (2013). Despite recent advances in filtering techniques for IMUs (Seel et al, 2014), they cannot deliver linear velocity information without drift or additional assumptions.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, most existing fall detection algorithms are based on data-driven or heuristic approaches (Bourke et al, 2008;Nyan et al, 2008a,b) (Kangas et al, 2007), where velocity or acceleration thresholds are used to assess loss of balance. Black-box, datadriven approaches intrinsically rely on large amounts of training data to achieve acceptable specificity (low rate of true positives) and sensitivity (low rate of true negatives) in real-world conditions.…”

Section: Introductionmentioning

confidence: 99%

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Observing the State of Balance with a Single Upper-Body Sensor

2016

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The occurrence of falls is an urgent challenge in our aging society. For wearable devices that actively prevent falls or mitigate their consequences, a critical prerequisite is knowledge on the user's current state of balance. To keep such wearable systems practical and to achieve high acceptance, only very limited sensor instrumentation is possible, often restricted to inertial measurement units at waist level. We propose to augment this limited sensor information by combining it with additional knowledge on human gait, in the form of an observer concept. The observer contains a combination of validated concepts to model human gait: a spring-loaded inverted pendulum model with articulated upper body, where foot placement and stance leg are controlled via the extrapolated center of mass (XCoM) and the virtual pivot point (VPP), respectively. State estimation is performed via an Additive Unscented Kalman Filter (Additive UKF). We investigated sensitivity of the proposed concept to model uncertainties, and we evaluated observer performance with real data from human subjects walking on a treadmill. Data were collected from an Inertial Measurement Unit (IMU) placed near the subject's center of mass (CoM), and observer estimates were compared to the ground truth as obtained via infrared motion capture. We found that the root mean squared deviation did not exceed 13 cm on position, 22 cm/s on velocity (0.56-1.35 m/s), 1.2°on orientation, and 17°/s on angular velocity.Keywords: human gait and balance, wearable sensors, state estimation, virtual pivot point, extrapolated center of mass, capture point, additive unscented kalman filter, fall detection

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Section: Figure 7 | Coefficients Of Determination Rmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Observing the State of Balance with a Single Upper-Body Sensor

2016

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“…Sway and standing balance have been estimated using accelerometry and gyro-sensors. Several other gait performance measures, including the TUG test, have also been evaluated [8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Various parameters have been measured and collected to classify the risk of falling among the elderly.…”

Section: Introductionmentioning

confidence: 99%

Fall-Risk Classification of the Timed Up-And-Go Test with Principle Component Analysis

Zakaria¹

2014

Int J Neurorehabilitation Eng

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This study aimed to show that important fall risk measures among the elderly can be classified using multiple parameters obtained from wearable inertial sensors. The timed up-and-go (TUG) test, a well-known standard assessment test, was used to evaluate the risk of falling among elderly individuals. The use of wearable inertial sensors enables extraction of triaxial acceleration and angular velocity signals for offline analysis. Thirty-eight elderly patients from Fujimoto Hayasuzu Hospital participated in this study. Specific results were provided from the signals obtained from acceleration and angular velocity, and analysis was carried out in each phase of various activities, such as sit-to-stand, walking, etc. Seventy-eight parameters were obtained from the extracted acceleration and angular velocity signals in all phases to classify the risk of falling among the elderly. Using principle component analysis, the most important measures were selected from the gathered parameters. The most influential measure in differentiating subjects with high and low fall risks was the turning angular velocity signal. Methods SubjectsThirty-eight elderly subjects (male, 20; female, 18) with an average age of 65.18 ± 8.90 years from Fujimoto Hayasuzu Hospital, Japan participated in the TUG test. The low-fall-risk (LFR) group comprised 27 subjects, and the high-fall-risk (HFR) group comprised 11 subjects.

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“…It should be noted that the attitude information is sufficient for this subtraction and does not require the full 3-D orientation. This is essential if one wishes to estimate velocities and positions through the integration of the external acceleration in applications such as fall detection [19], [20]. However, although external acceleration is the main source of the attitude estimation error and despite the need for its accurate estimation in many applications, the problem of estimating the external acceleration during the orientation estimation has not been explicitly discussed in the literature.…”

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confidence: 99%

Estimation of Attitude and External Acceleration Using Inertial Sensor Measurement During Various Dynamic Conditions

Lee

Park

Robinovitch

2012

IEEE Trans. Instrum. Meas.

182

120

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This paper proposes a Kalman filter-based attitude (i.e., roll and pitch) estimation algorithm using an inertial sensor composed of a triaxial accelerometer and a triaxial gyroscope. In particular, the proposed algorithm has been developed for accurate attitude estimation during dynamic conditions, in which external acceleration is present. Although external acceleration is the main source of the attitude estimation error and despite the need for its accurate estimation in many applications, this problem that can be critical for the attitude estimation has not been addressed explicitly in the literature. Accordingly, this paper addresses the combined estimation problem of the attitude and external acceleration. Experimental tests were conducted to verify the performance of the proposed algorithm in various dynamic condition settings and to provide further insight into the variations in the estimation accuracy. Furthermore, two different approaches for dealing with the estimation problem during dynamic conditions were compared, i.e., threshold-based switching approach versus acceleration model-based approach. Based on an external acceleration model, the proposed algorithm was capable of estimating accurate attitudes and external accelerations for short accelerated periods, showing its high effectiveness during short-term fast dynamic conditions. Contrariwise, when the testing condition involved prolonged high external accelerations, the proposed algorithm exhibited gradually increasing errors. However, as soon as the condition returned to static or quasi-static conditions, the algorithm was able to stabilize the estimation error, regaining its high estimation accuracy.

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The identification of vertical velocity profiles using an inertial sensor to investigate pre-impact detection of falls

Cited by 130 publications

References 12 publications

Observing the State of Balance with a Single Upper-Body Sensor

Observing the State of Balance with a Single Upper-Body Sensor

Fall-Risk Classification of the Timed Up-And-Go Test with Principle Component Analysis

Estimation of Attitude and External Acceleration Using Inertial Sensor Measurement During Various Dynamic Conditions

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