Nonlinear optimization for drift removal in estimation of gait kinematics based on accelerometers

Djurić-Jovičić, Milica; Jovičić, Nenad S.; Popović, Dejan B.; Djordjević, A.R.

doi:10.1016/j.jbiomech.2012.08.028

Cited by 24 publications

(28 citation statements)

References 28 publications

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“…These are the reasons why IUs are seldom used alone in the measurement of displacement and different techniques are available through the literature in order to solve this problem. They range from using either aided sensors or sensing systems [11]- [13], [18] to using signal analysis such as filters [22], frequency treatment [17]- [21], or baseline estimation [23] to remove the sensor noise. Regarding signal analysis two techniques are typically used: adaptive filtering methods based on Fourier series and the wavelet transform.…”

Section: Discussionmentioning

confidence: 99%

“…Through the literature, different methods can be found to partially solve this problem: aided sensors or sensing systems data fusion [11]- [14], wavelet analysis [15], [16], Fourier-based filters [17]- [21], band-pass filtering [22], and polynomial data fitting [23]. Generally, they tend to use the aid of an externally referenced sensor or prior knowledge of the motion as well as complex linear and adaptive filtering or other data processing to estimate displacement from the acceleration signal.…”

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confidence: 99%

“…This study develops a new method to cancel the drift effect based on the use of a single IU and jointly considering different processing methods. In particular, polynomial data fitting [23], spline interpolation, and wavelet transform were employed one after the other. The idea is to obtain an accurate estimation of the Z-position signal without any restriction (i.e., previous knowledge of acceleration, velocity, and/or position at reference points [23]) other than the movement's periodicity.…”

mentioning

confidence: 99%

“…In particular, polynomial data fitting [23], spline interpolation, and wavelet transform were employed one after the other. The idea is to obtain an accurate estimation of the Z-position signal without any restriction (i.e., previous knowledge of acceleration, velocity, and/or position at reference points [23]) other than the movement's periodicity. This way, the inertial technology arises as a powerful tool to measure activity during mobility related activities in a noninvasive manner.…”

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confidence: 99%

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Drift-Free Position Estimation for Periodic Movements Using Inertial Units

Millor

Lecumberri

Gómez

et al. 2014

IEEE J. Biomed. Health Inform.

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Latest advances in microelectromechanical systems have made inertial units (IUs) a powerful tool for human motion analysis. However, difficulties in handling their output signals must be overcome. The purpose of this study was to develop the novel "PB-algorithm" based on polynomial data fitting, splines interpolation, and the wavelet transform, one after the other, to cancel drift disturbances in position estimation for periodic movements. High-accuracy position measurements from an optical system (Vicon Nexus 1.0) were used to validate the proposed method and comparison with another drift-correction algorithm was provided. Results indicate the accuracy with respect to the Vicon's reference signal (euclidean error lower than 54.62 × 10(-3) m and correlation coefficient higher than 0.968). A reduction of the root-mean-square error of 68.74% was obtained when the proposed two-step method was compared with a modified-band limited Fourier linear combiner. All methods were applied to data from the 30-s chair stand test, which is one of the most used clinical tests dealing with lower body strength assessment, falls prediction, and gait disorders in older adults. The relevance of this study is that after cancelling drift disturbances, and obtaining an accurate Z-position estimation, it is possible to evaluate the sit-to-stand and stand-to-sit transitions from the whole test.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Drift-Free Position Estimation for Periodic Movements Using Inertial Units

Millor

Lecumberri

Gómez

et al. 2014

IEEE J. Biomed. Health Inform.

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“…Such aiding information makes the magnetometer superfluous for heading information [44,153,223], can be used to mitigate sensory drift [208] and does not require alignment and calibration procedures beforehand [128].…”

Section: Introductionmentioning

confidence: 99%

Assessment of hand kinematics and interactions with the environment

Kortier¹

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The hand is one of the most important instruments of our body. Its versatility enables the execution of a wide range of tasks that ask for a powerful, precise or gentle approach. Measuring hand and finger movements, and interaction forces, is therefore important for the assessment of tasks in daily life. However, measuring on-body kinematic and kinetic quantities is a delicate procedure due to the dexterity of the hand, and moreover, the little and complex shaped skin places for sensor attachment. This thesis proposes a new on-body assessment system that allows the measurement of movements and interaction forces of the hand, fingers and thumb.The first objective, the development, evaluation and validation of an inertial and magnetic sensing system for the measurement of hand and finger kinematics is the topic of chapters 2 to 5. The second objective, assessment of the dynamic interaction between human hand and environment using combined force and movement sensing, is the topic of chapter 6.Chapter 2 describes the hardware and algorithms for a sensing system which can be attached to the hand, fingers and the thumb. The hardware consists of multiple inertial and magnetic sensors to measure angular velocities, accelerations and the magnetic field. Each individual finger and the thumb is modelled as a kinematic chain where the bones correspond to the linkages and each joint is considered as an ideal ball-socket joint. Segmental lengths were determined by manual measurement, whereas the inertial sensors provided the input for a Kalman filter to estimate the 3D orientation of the corresponding segment. Hereafter, the orientation and tip position of each finger was estimated by applying forward kinematics. To our knowledge, it is the first system that uses inertial sensors for estimating finger kinematics. The estimation quality was expressed in terms of static and dynamic accuracy, dynamic range and repeatability. Differences with an active optical reference system were found to be a maximum of 13 mm for the finger tip distance difference during circular pointing movements. A standardized test protocol for instrumented gloves showed very good repeatability results compared to other datagloves, proven by the mean angle difference of < 2 degrees. Finally, a dynamic range was specified as a measure of how well the system is able to reconstruct joint angles when experiencing large angular velocities. The system showed accurate reconstruction up to 116 full index finger flex-extension movements per minute.Chapter 3 reports an extensive comparison of our inertial sensing system against a passive opto-electronic marker system. It aims on typical hand-function tasks, including tapping, (fast) finger flexion, hand opening/closing, ab-adduction and circular pointing, which are used to quantify various motor symptoms for clinical diagnosis. Three subjects were included and instrumented with both systems. Differences in position, Range of Motion (RoM) and 3D vii joint angles were noted of which the largest were found in fast and ...

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Use of Smartphones and Portable Media Devices for Quantifying Human Movement Characteristics of Gait, Tendon Reflex Response, and Parkinson’s Disease Hand Tremor

LeMoyne

Mastroianni²

2014

Methods in Molecular Biology

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Smartphones and portable media devices are both equipped with sensor components, such as accelerometers. A software application enables these devices to function as a robust wireless accelerometer platform. The recorded accelerometer waveform can be transmitted wireless as an e-mail attachment through connectivity to the Internet. The implication of such devices as a wireless accelerometer platform is the experimental and post-processing locations can be placed anywhere in the world. Gait was quantified by mounting a smartphone or portable media device proximal to the lateral malleolus of the ankle joint. Attributes of the gait cycle were quantified with a considerable accuracy and reliability. The patellar tendon reflex response was quantified by using the device in tandem with a potential energy impact pendulum to evoke the patellar tendon reflex. The acceleration waveform maximum acceleration feature of the reflex response displayed considerable accuracy and reliability. By mounting the smartphone or portable media device to the dorsum of the hand through a glove, Parkinson's disease hand tremor was quantified and contrasted with significance to a non-Parkinson's disease steady hand control. With the methods advocated in this chapter, any aspect of human movement may be quantified through smartphones or portable media devices and post-processed anywhere in the world. These wearable devices are anticipated to substantially impact the biomedical and healthcare industry.

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Nonlinear optimization for drift removal in estimation of gait kinematics based on accelerometers

Cited by 24 publications

References 28 publications

Drift-Free Position Estimation for Periodic Movements Using Inertial Units

Drift-Free Position Estimation for Periodic Movements Using Inertial Units

Assessment of hand kinematics and interactions with the environment

Use of Smartphones and Portable Media Devices for Quantifying Human Movement Characteristics of Gait, Tendon Reflex Response, and Parkinson’s Disease Hand Tremor

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