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Wavelet-based detection of gait events from inertial sensors: analysis of sensitivity to scale choice
... McCamley and colleagues [18] set it to 16 for a 100 samples/s sampling rate (corresponding to a central frequency of 1.25 Hz), as it was shown to provide good results in terms of detection accuracy. A previous study of ours [21] showed the effect induced by the variation of the scale on IC and FC events estimation, when the gait speed was self-selected by healthy participants. It was found that the goodness of gait events estimation varied in response to a change of the scale value. ...
The accurate and reliable extraction of specific gait events from a single inertial sensor at waist level has been shown to be challenging. Among several techniques, a wavelet-based method for initial contact (IC) and final contact (FC) estimation was shown to be the most accurate in healthy subjects. In this study, we evaluated the sensitivity of events detection to the wavelet scale of the algorithm, when walking at different speeds, in order to optimize its selection. A single inertial sensor recorded the lumbar vertical acceleration of 20 subjects walking at three different self-selected speeds (slow, normal, and fast) in a motion analysis lab. The scale of the wavelet method was varied. ICs were generally accurately detected in a wide range of wavelet scales under all the walking speeds. FCs detection proved highly sensitive to scale choice. Different gait speeds required the selection of a different scale for accurate detection and timing, with the optimal scale being strongly correlated with subjects’ step frequency. The best speed-dependent scales of the algorithm led to highly accurate timing in the detection of IC (RMSE < 22 ms) and FC (RMSE < 25 ms) across all speeds. Our results pave the way for the optimal adaptive selection of scales in future applications using this algorithm.
The widespread and pervasive use of smartphones for sending messages, calling, and entertainment purposes, mainly among young adults, is often accompanied by the concurrent execution of other tasks. Recent studies have analyzed how texting, reading or calling while walking–in some specific conditions–might significantly influence gait parameters. The aim of this study is to examine the effect of different smartphone activities on walking, evaluating the variations of several gait parameters. 10 young healthy students (all smartphone proficient users) were instructed to text chat (with two different levels of cognitive load), call, surf on a social network or play with a math game while walking in a real-life outdoor setting. Each of these activities is characterized by a different cognitive load. Using an inertial measurement unit on the lower trunk, spatio-temporal gait parameters, together with regularity, symmetry and smoothness parameters, were extracted and grouped for comparison among normal walking and different dual task demands. An overall significant effect of task type on the aforementioned parameters group was observed. The alterations in gait parameters vary as a function of cognitive effort. In particular, stride frequency, step length and gait speed show a decrement, while step time increases as a function of cognitive effort. Smoothness, regularity and symmetry parameters are significantly altered for specific dual task conditions, mainly along the mediolateral direction. These results may lead to a better understanding of the possible risks related to walking and concurrent smartphone use.
This paper presents a wearable inertial measurement system and its associated spatiotemporal gait analysis algorithm to obtain quantitative measurements and explore clinical indicators from the spatiotemporal gait patterns for patients with stroke or Parkinson’s disease. The wearable system is composed of a microcontroller, a triaxial accelerometer, a triaxial gyroscope, and an RF wireless transmission module. The spatiotemporal gait analysis algorithm, consisting of procedures of inertial signal acquisition, signal preprocessing, gait phase detection, and ankle range of motion estimation, has been developed for extracting gait features from accelerations and angular velocities. In order to estimate accurate ankle range of motion, we have integrated accelerations and angular velocities into a complementary filter for reducing the accumulation of integration error of inertial signals. All twenty-four participants mounted the system on their foot to walk along a straight line of 10 meters at normal speed and their walking recordings were collected to validate the effectiveness of the proposed system and algorithm. Experimental results show that the proposed inertial measurement system with the designed spatiotemporal gait analysis algorithm is a promising tool for automatically analyzing spatiotemporal gait information, serving as clinical indicators for monitoring therapeutic efficacy for diagnosis of stroke or Parkinson’s disease.
In ultrasound elastography, the axial strain distribution within biological tissues is calculated as the numerical derivative (differentiation) of the estimated axial displacement field. Unfortunately, the numerical derivative is unstable because it can greatly amplify the noises, especially at high frequencies. This work focuses on the axial strain calculation from the estimated axial displacements using wavelet transforms (WTs), including continuous wavelet transforms (CWTs) and discrete wavelet transforms (DWTs). The feasibility of the WT-based method using the quadratic spline function is verified by computer simulations and some phantom data. Results indicate that the WT-based method can effectively reduce the noise amplification in axial strain calculation.
We present a waist-worn personal navigation system based on inertial measurement units. The device makes use of the human bipedal pattern to reduce position errors. We describe improved algorithms, based on detailed description of the heel strike biomechanics and its translation to accelerations of the body waist to estimate the periods of zero velocity, the step length, and the heading estimation. The experimental results show that we are able to support pedestrian navigation with the high-resolution positioning required for most applications.
In this paper we present a quaternion-based Extended Kalman Filter (EKF) for estimating the three-dimensional orientation of a rigid body. The EKF exploits the measurements from an Inertial Measurement Unit (IMU) that is integrated with a tri-axial magnetic sensor. Magnetic disturbances and gyro bias errors are modeled and compensated by including them in the filter state vector. We employ the observability rank criterion based on Lie derivatives to verify the conditions under which the nonlinear system that describes the process of motion tracking by the IMU is observable, namely it may provide sufficient information for performing the estimation task with bounded estimation errors. The observability conditions are that the magnetic field, perturbed by first-order Gauss-Markov magnetic variations, and the gravity vector are not collinear and that the IMU is subject to some angular motions. Computer simulations and experimental testing are presented to evaluate the algorithm performance, including when the observability conditions are critical.
In this paper we report on a novel algorithm for the real-time detection and timing of initial (IC) and final contact (FC) gait events. We process the vertical and antero-posterior accelerations registered at the lower trunk (L3 vertebra). The algorithm is based on a set of heuristic rules extracted from a set of 1719 steps. An independent experiment was conducted to compare the results of our algorithms with those obtained from a Digimax force platform. The results show small deviations from times of occurrence of events recorded from the platform (13+/-35 ms for IC and 9+/-54 ms for FC). Results for the FC timing are especially relevant in this field, as no previous work has addressed its temporal location through the processing of lower trunk accelerations. The delay in the real-time detection of the IC is 117+/-39 ms and 34+/-72 ms for the FC, improving previously reported results for real-time detection of events from lower trunk accelerations.
This paper studies the feasibility of an analysis of spatio-temporal gait parameters based upon accelerometry. To this purpose, acceleration patterns of the trunk and their relationships with spatio-temporal gait parameters were analysed in healthy subjects. Based on model predictions of the body's centre of mass trajectory during walking, algorithms were developed to determine spatio-temporal gait parameters from trunk acceleration data. In a first experiment, predicted gait parameters were compared with gait parameters determined from ground reaction forces measured by a treadmill. In a second experiment, spatio-temporal gait parameters were determined during overground walking. From the results of these experiments, it is concluded that, in healthy subjects, the duration of subsequent stride cycles and left/right steps, and estimations of step length and walking speed can be obtained from lower trunk accelerations. The possibility to identify subsequent stride cycles can be the basis for an analysis of other signals (e.g. kinematic or muscle activity) within the stride cycle.
Wearable devices are able to capture movement-related characteristics from inertial sensors integrated in them. Many spatio-temporal parameters of gait can be estimated by the acquisition of inertial data, but their accuracy depends on the placement of the devices on the body, as well as on the numerical values chosen for the estimation techniques. In this work, three inertial sensors placed at three different heights of the trunk are used to collect data from healthy adult participants walking at three different speeds. Step length and step velocity were calculated from accelerometer data. For the estimation of these parameters high-pass filtering is required: fifteen different values of the filter cut-off frequency were analyzed for the subsequent step length estimation. The results were compared against those measured from a marker-based movement analysis system. Estimation accuracy of both step length and step velocity resulted significantly affected by both sensor location and cut-off frequency of the filter. These preliminary results suggest that placing the sensor too low leads to an increased estimation error, while frequencies up to around 1 Hz lead to acceptable results, with a significant decrease in estimation accuracy above that value.
Over the last decade, many studies have been conducted on the effects of mechanical vibration on the physical hormonal and neuromuscular responses of muscles: in some works the growth hormone response showed a dependence on the acceleration provided by Whole Body Vibration (WBV) platforms with respect to subjects responsiveness. Therefore the accuracy of acceleration measurements related to the excitation system is important and should be assessed. To this aim a preliminary study on the identification and evaluation of the measurement uncertainty sources in the measurement chain of a novel WBV platform developed by the Authors is here proposed. After the main measurement error sources have been identified, a Monte Carlo simulation Method has been implemented to obtain the uncertainty in accelerations and displacements provided by the WBV platform. Since main causes of uncertainty has been identified in the accelerometer sensitivity, its mounting and the data acquisition processing, Results of the test showed a relative uncertainty of about ±4% for acceleration and displacement measurements for frequencies between 20Hz and 60Hz
Accuracy of systems able to recognize in real time daily living activities heavily depends on the processing step for signal segmentation. So far, windowing approaches are used to segment data and the window size is usually chosen based on previous studies. However, literature is vague on the investigation of its effect on the obtained activity recognition accuracy, if both short and long duration activities are considered. In this work, we present the impact of window size on the recognition of daily living activities, where transitions between different activities are also taken into account. The study was conducted on nine participants who wore a tri-axial accelerometer on their waist and performed some short (sitting, standing, and transitions between activities) and long (walking, stair descending and stair ascending) duration activities. Five different classifiers were tested, and among the different window sizes, it was found that 1.5 s window size represents the best trade-off in recognition among activities, with an obtained accuracy well above 90%. Differences in recognition accuracy for each activity highlight the utility of developing adaptive segmentation criteria, based on the duration of the activities.
Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.
The ability to track pedestrians without any infrastructure support is required by numerous applications in the healthcare, augmented reality, and entertainment industries. In this paper, we present a simple self-contained pedestrian tracking method using a foot-mounted inertial and magnetic sensor module. Traditional methods normally incorporate double integration of the measured acceleration, but such methods are susceptible to the acceleration noise and integration drift. To avoid this issue, alternative approaches which make use of walking dynamics to aggregate individual stride have been explored. The key for stride aggregating is to accurately and reliably detect stride boundary and estimate the associated heading direction for each stride, but it is still not well solved yet due to sensor noise and external disturbance. In this paper, we propose to make use of the inertial sensor and magnetometer measurements for stride detection and heading direction determination. In our method, a simple and reliable stride detection method, which is resilient to random bouncing motions and sensor noise, is designed based on gyroscope and accelerometer measurements. Heading direction is then determined from the foot's orientation which fuses all the three types of sensor information together. The proposed pedestrian tracking method has been evaluated using experiments, including both short distance walking with different patterns and long distance walking performed indoors and outdoors. The good experimental results have illustrated the effectiveness of the proposed pedestrian tracking method.
A novel fiber-optic accelerometer has been developed for the quantitative assessment of parkinsonian tremor at rest. The transducers is based on a fiber-optic sensing technique, that reduce some important drawbacks of biomedical applications, such as patient electrical safety and electromagnetic interference, and allows a non-invasive solution for monitoring of human movement, due to their limited mass. The sensing principle is based on the measurement of the transversal displacement of an emitting optical fiber cantilever due to the acceleration, conducted by means of a photodiode array: the detection of the light intensity profile makes the developed measurement system less sensitive to the light intensity variations independent from acceleration than intensity-based sensors. A dynamic calibration of the optical fiber accelerometer has been performed and a linear relationship between the lateral displacement of the fiber cantilever and acceleration has been experimentally evaluated. Moreover, a flat frequency response function between 3 Hz and 7 Hz (i.e. typical frequency range in which tremor at rest occurs in parkinsonian patients) and an high sensitivity in this frequency range (about 14 pixel/(m∙s-2) have been experimentally derived. These metrological features confirm that the proposed measurement system is particularly suitable for the quantitative assessment of parkinsonian tremor at rest.
Gait parameters such as stride length, width, and period, as well as their respective variabilities, are widely used as indicators of mobility and walking function. Foot placement and its variability have thus been applied in areas such as aging, fall risk, spinal cord injury, diabetic neuropathy, and neurological conditions. But a drawback is that these measures are presently best obtained with specialized laboratory equipment such as motion capture systems and instrumented walkways, which may not be available in many clinics and certainly not during daily activities. One alternative is to fix inertial measurement units (IMUs) to the feet or body to gather motion data. However, few existing methods measure foot placement directly, due to drift associated with inertial data. We developed a method to measure stride-to-stride foot placement in unconstrained environments, and tested whether it can accurately quantify gait parameters over long walking distances. The method uses ground contact conditions to correct for drift, and state estimation algorithms to improve estimation of angular orientation. We tested the method with healthy adults walking over-ground, averaging 93 steps per trial, using a mobile motion capture system to provide reference data. We found IMU estimates of mean stride length and duration within 1% of motion capture, and standard deviations of length and width within 4% of motion capture. Step width cannot be directly estimated by IMUs, although lateral stride variability can. Inertial sensors measure walks over arbitrary distances, yielding estimates with good statistical confidence. Gait can thus be measured in a variety of environments, and even applied to long-term monitoring of everyday walking.
Gait analysis through on-body wireless accelerometers can provide valuable information for multiple health-related applications. The dynamic nature of human body acceleration signals makes their analysis with the wavelet transform optimum. Nevertheless, one of the main issues for the practical development of this signal processing tool in real time is the difficulty in the selection of the appropriate scale and mother wavelet for each particular gait. In this paper we show how these problems can be solved, resulting in a system that can accurately monitor patients' gait in real time without the need for calibration.
This study introduces a new method of extracting initial and final contact gait time events from vertical acceleration, measured with one waist mounted inertial measurement unit, by means of continuous wavelet transforms. The method was validated on 18 young healthy subjects and compared to two others available in the literature. Of the three methods investigated, the new one was the most accurate at identifying the existence and timing of initial and final contacts with the ground, with an average error of 0.02±0.02 s and 0.03±0.03 s (approximately 2% and 3% of mean stride duration), respectively.
The clinical objective of the gait analysis laboratory, developed by United Technologies Corporation (Hartford, CT, USA) in 1980, at the Newington Children's Hospital is to provide quantified assessments of human locomotion which assist in the orthopaedic management of various pediatric gait pathologies. The motion measurement system utilizes a video-based data collection strategy similar to commercially available systems for motion data collection. Anatomically aligned, passive, retroreflective markers placed on the subject are illuminated, detected, and stored in dedicated camera hardware while data are acquired from force platforms and EMG transducers. Three-dimensional marker position information is used to determine: (i) the orientation of segmentally-embedded coordinate systems, (ii) instantaneous joint center locations, and (iii) joint angles. Joint kinetics, i.e., moments and powers, may also be computed if valid force plate data are collected.
A novel method based on continuous wavelet transform (CWT) using Haar wavelet function for approximate derivative calculation of analytical signals is proposed and successfully used in processing the photoacoustic signal. An approximate nth derivative of an analytical signal can be obtained by applying n times of the wavelet transform to the signal. The results obtained from four other different methods--the conventional numerical differentiation, the Fourier transform method, the Savitzky-Golay method, and the discrete wavelet transform (DWT) method--were compared with the proposed CWT method; it was demonstrated that all the results are almost the same for signals without noise, but the proposed CWT method is superior to the former four methods for noisy signals. The approximate first and second derivative of the photoacoustic spectrum of Pr(Gly)3Cl3.3H2O and PrCl3.6H2O were obtained using the proposed CWT method; the results are satisfactory.
This study reports on the novel use of a portable system to measure gait cycle parameters. Measurements were made by a triaxial accelerometer over the lower trunk during timed walking over a range of self-administered speeds. Signals from each trial were transformed to a horizontal-vertical coordinate system and analyzed by an unbiased autocorrelation procedure to obtain cadence, step length, and measures of gait regularity and symmetry. By curvilinear interpolation, speed-dependent gait parameters could be compared at a normalized speed. It was demonstrated that analysis of gait cycle parameters which previously required fixed laboratory equipment and paced walking procedures, now can be made from data obtained by a timing device and a portable sensor at free walking speeds.
Gait parameters are differently affected by concurrent smartphone-based activities with scaled levels of cognitive effort
- C Caramia
- I Bernabucci
- C Anna
- C De Marchis
- M Schmid