1,417 research outputs found
Inertial sensor-based knee flexion/extension angle estimation
A new method for estimating knee joint flexion/extension angles from segment acceleration and angular velocity data is described. The approach uses a combination of Kalman filters and biomechanical constraints based on anatomical knowledge. In contrast to many recently published methods, the proposed approach does not make use of the earth’s magnetic field and hence is insensitive to the complex field distortions commonly found in modern buildings. The method was validated experimentally by calculating knee angle from measurements taken from two IMUs placed on adjacent body segments. In contrast to many previous studies which have validated their approach during relatively slow activities or over short durations, the performance of the algorithm was evaluated during both walking and running over 5 minute periods. Seven healthy subjects were tested at various speeds from 1 to 5 miles/hour. Errors were estimated by comparing the results against data obtained simultaneously from a 10 camera motion tracking system (Qualysis). The average measurement error ranged from 0.7 degrees for slow walking (1 mph) to 3.4 degrees for running (5mph). The joint constraint used in the IMU analysis was derived from the Qualysis data. Limitations of the method, its clinical application and its possible extension are discussed
CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics From Measured and Simulated Inertial Sensor Data
Machine learning is a promising approach to evaluate human movement based on wearable sensor data. A representative dataset for training data-driven models is crucial to ensure that the model generalizes well to unseen data. However, the acquisition of sufficient data is time-consuming and often infeasible. We present a method to create realistic inertial sensor data with corresponding biomechanical variables by 2D walking and running simulations. We augmented a measured inertial sensor dataset with simulated data for the training of convolutional neural networks to estimate sagittal plane joint angles, joint moments, and ground reaction forces (GRFs) of walking and running. When adding simulated data, the root mean square error (RMSE) of the test set of hip, knee, and ankle joint angles decreased up to 17 %, 27 % and 23 %, the RMSE of knee and ankle joint moments up to 6 % and the RMSE of anterior-posterior and vertical GRF up to 2 and 6 %. Simulation-aided estimation of joint moments and GRFs was limited by inaccuracies of the biomechanical model. Improving the physics-based model and domain adaptation learning may further increase the benefit of simulated data. Future work can exploit biomechanical simulations to connect different data sources in order to create representative datasets of human movement. In conclusion, machine learning can benefit from available domain knowledge on biomechanical simulations to supplement cumbersome data collections
Functional Rotation Axis Based Approach for Estimating Hip Joint Angles Using Wearable Inertial Sensors: Comparison to Existing Methods
Wearable sensors are at the heart of the digital health revolution. Integral to the use of these sensors for monitoring conditions impacting balance and mobility are accurate estimates of joint angles. To this end a simple and novel method of estimating hip joint angles from small wearable magnetic and inertial sensors is proposed and its performance is established relative to optical motion capture in a sample of human subjects. Improving upon previous work, this approach does not require precise sensor placement or specific calibration motions, thereby easing deployment outside of the research laboratory. Specific innovations include the determination of sensor to segment rotations based on functionally determined joint centers, and the development of a novel filtering algorithm for estimating the relative orientation of adjacent body segments. Hip joint angles and range of motion determined from the proposed approach and an existing method are compared to those from an optical motion capture system during walking at a variety of speeds and tasks designed to exercise the hip through its full range of motion. Results show that the proposed approach estimates flexion/extension angle more accurately (RMSE from 7.08 to 7.29 deg) than the existing method (RMSE from 11.64 deg to 14.33 deg), with similar performance for the other anatomical axes. Agreement of each method with optical motion capture is further characterized by considering correlation and regression analyses. Mean ranges of motion for the proposed method are not largely different from those reported by motion capture, and showed similar values to the existing method. Results indicate that this algorithm provides a promising approach for estimating hip joint angles using wearable inertial sensors, and would allow for use outside of constrained research laboratories
Human Motion Analysis with Wearable Inertial Sensors
High-resolution, quantitative data obtained by a human motion capture system can be used to better understand the cause of many diseases for effective treatments. Talking about the daily care of the aging population, two issues are critical. One is to continuously track motions and position of aging people when they are at home, inside a building or in the unknown environment; the other is to monitor their health status in real time when they are in the free-living environment. Continuous monitoring of human movement in their natural living environment potentially provide more valuable feedback than these in laboratory settings. However, it has been extremely challenging to go beyond laboratory and obtain accurate measurements of human physical activity in free-living environments. Commercial motion capture systems produce excellent in-studio capture and reconstructions, but offer no comparable solution for acquisition in everyday environments. Therefore in this dissertation, a wearable human motion analysis system is developed for continuously tracking human motions, monitoring health status, positioning human location and recording the itinerary.
In this dissertation, two systems are developed for seeking aforementioned two goals: tracking human body motions and positioning a human. Firstly, an inertial-based human body motion tracking system with our developed inertial measurement unit (IMU) is introduced. By arbitrarily attaching a wearable IMU to each segment, segment motions can be measured and translated into inertial data by IMUs. A human model can be reconstructed in real time based on the inertial data by applying high efficient twists and exponential maps techniques. Secondly, for validating the feasibility of developed tracking system in the practical application, model-based quantification approaches for resting tremor and lower extremity bradykinesia in Parkinson’s disease are proposed. By estimating all involved joint angles in PD symptoms based on reconstructed human model, angle characteristics with corresponding medical ratings are employed for training a HMM classifier for quantification. Besides, a pedestrian positioning system is developed for tracking user’s itinerary and positioning in the global frame. Corresponding tests have been carried out to assess the performance of each system
Contributions to physical exercises monitoring with inertial measurement units
Resumen: La monitorización de movimientos trata de obtener información sobre su ejecución, siendo esencial en múltiples aplicaciones, como el seguimiento de terapias fÃsicas. La monitorización tiene un doble objetivo esencial para lograr los beneficios de dichas terapias: asegurar la corrección en la ejecución de movimientos y mejorar la adherencia a los programas prescritos. Para lograr esta monitorización de forma remota y poco intrusiva, se necesitan recursos tecnológicos. Este trabajo se centra en las soluciones basadas en sensores inerciales.
Esta tesis estudia los algoritmos de la literatura para el análisis de movimientos con sensores inerciales, determinando un parámetro anatómico requerido en diversas propuestas: la posición de las articulaciones respecto de los sensores, asà como longitud de los segmentos anatómicos. En este trabajo se introducen dos algoritmos de calibración anatómica. El primero, basado en mÃnimos cuadrados, determina el punto o el eje medios de aceleración nula presente en las articulaciones fijas. El algoritmo está adaptado a los movimientos lentos dados en los miembros inferiores para estabilizar las articulaciones. El segundo, adaptado a la variación de la posición relativa del punto de aceleración nula respecto de los sensores a causa del caracterÃstico tejido blando asociado al cuerpo humano, emplea las medidas inerciales como entradas en un filtro de Kalman extendido.
Por otro lado, esta tesis aborda la falta de datos comunes para la evaluación y comparación de los algoritmos. Para ello, se diseña y crea una base de datos centrada en movimientos habituales en rutinas fÃsicas, que se encuentra publicada en Zenodo. Esta base de datos contiene movimientos de calibración articular y de ejercicios de miembros inferiores y superiores ejecutados de forma correcta e incorrecta por 30 voluntarios de ambos sexos con un amplio rango de edades, grabados con cuatro sensores inerciales y un sistema de referencia óptico de alta precisión. Además, las grabaciones se encuentran etiquetadas acorde al tipo de ejercicio realizado y su evaluación.
Finalmente, se estudia un segundo enfoque de monitorización de rutinas fÃsicas, cuyo objetivo es reconocer y evaluar simultáneamente los ejercicios ejecutados, retos comúnmente estudiados individualmente. Se proponen tres sistemas que emplean las medidas de cuatro sensores inerciales y difieren en el nivel de detalle en las salidas del sistema. Para realizar las clasificaciones propuestas, se evalúan seis algoritmos de machine learning determinando el más adecuado.This thesis is framed in the field of remote motion monitoring, which aims to obtain
information about the execution of movements. This information is essential in many
applications, including the clinical ones, to measure the evolution of patients, to assess
possible pathologies, such as motor or cognitive ones, and to follow up physical therapies.
The monitoring of physical therapies has twofold purpose: to ensure the correct
execution of movements and to improve adherence to the programs. Both purposes
are essential to achieve the benefits associated with physical therapies. To accomplish
this monitoring in a remote and non-intrusive way, technological resources such as
the well-known inertial sensors are needed, which are commonly integrated into the
so-called wearables.
This work focuses on inertial-based solutions for monitoring physical therapy routines.
However, the results of this work are not exclusive of this field, being able to be applied
in other fields that require a motion monitoring. This work is intended to meet the
needs of the monitoring systems found in the literature.
In the review of previous proposals for remote monitoring of rehabilitation routines,
we found two different main approaches. The first one is based on the analysis of
movements, which estimates kinematic parameters, and the second one focuses on the
qualitative characterization of the movements. From this differentiation, we identify
and contribute to the limitations of each approach.
With regard to the motion analysis for the estimation of kinematic parameters, we
found an anatomical parameter required in various methods proposed in the literature.
This parameter consists in the position of the joints with respect to the sensors,
and sometimes these methods also require the length of the anatomical segments. The
determination of these internal parameters is complex and is usually performed in
controlled environments with optical systems or through palpation of anatomical landmarks
by trained personnel. There is a lack of algorithms that determine these anatomical
parameters using inertial sensors.
This work introduces an algorithm for this anatomical calibration, which is based on
the determination of the point of zero acceleration present in fixed joints. We use one
inertial sensor per joint in order to simplify the complexity of algorithms versus using
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more than one. Since the relative position of this point may vary due to soft tissue
movements or joint motion, the mean null acceleration point for the calibration motion
is estimated by least squares. This algorithm is adapted to slow movements occurring
in the lower-limbs to meet the required joint stabilization. Moreover, it can be applied
to both joint centers and axes, although the latter is more complex to determine. Since
we are dealing with the calibration of a system as complex as the human body, we evaluate
different movements and their relation to the accuracy of the proposed system.
This thesis also proposes a second, more versatile calibration method, which is adapted
to the characteristic soft tissue associated with the human body. This method is based
on the measurements of one inertial sensors used as inputs of an extended Kalman
filter. We test the proposal both in synthetic data and in the real scenario of hip center
of rotation determination. In simulations it provides an accuracy of 3% and in the
real scenario, where the reference is obtained with a high precision optical system, the
accuracy is 10 %. In this way, we propose a novel algorithm that localizes the joints
adaptively to the motion of the tissues.
In addition, this work addresses another limitation of motion analysis which is the lack
of common datasets for the evaluation of algorithms and for the development of new
proposals of motion monitoring methods. For this purpose, we design and create a
public database focused on common movements in rehabilitation routines. Its design
takes into account the joint calibration that is usually considered for the monitoring
of joint parameters, performing functional movements for it. We monitor lower and
upper limb exercises correctly and incorrectly performed by 30 volunteers of both sexes
and a wide range of ages. One of the main objectives to be fulfilled by this database
is the validation of algorithms based on inertial systems. Thus, it is recorded by using
four inertial systems placed on different body limbs and including a highly accurate
reference system based on infrared cameras. In addition, the recorded movements
are labeled according to their characterization, which is based on the type of exercise
performed and their quality. We provide a total of 7 076 files of inertial kinematic data
with a high-precision reference, characterized with respect to the kind of performed
motion and their correctness in performance, together with a function for automatic
processing.
Finally, we focus on the analysis of the second approach of monitoring physical routines,
whose objective is to obtain qualitative information of their execution. This work
contributes to the characterization of movements including their recognition and evaluation,
which are usually studied separately. We propose three classification systems
which use four inertial sensors. The proposals differ in the distribution of data and,
therefore, the level of detail in the system outputs. We evaluate six machine learning
techniques for the proposed classification systems in order to determine the most
suitable for each of them: Support Vector Machines, Decision Trees, Random Forest,
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K Nearest Neighbors, Extreme Learning Machines and Multi-Layer Perceptron. The
proposals result in accuracy, F1-value, precision and sensitivity above the 88 %. Furthermore,
we achieve a system with an accuracy of 95% in the complete qualitative
characterization of the motions, which recognizes the performed motion and evaluates
the correctness of its performance. It is worth highlighting that the highest metrics
are always obtained with Support Vector Machines, among all the methods evaluated.
The proposed classifier that provides the highest metrics is the one divided into two
stages, that first recognizes the exercises and then evaluates them, compared with the
other proposals that perform both tasks in one single-stage classification.
From our work, it can be concluded that inertial systems are appropriate for remote
physical exercise monitoring. On the one hand, they are suitable for the calibration
of human joints necessary for various methods of motion analysis using one inertial
sensor per joint. These sensors allow to obtain the estimation of an average joint location
as well as the average length of anatomical segments. Also, joint centers can
be located in scenarios where joint-related sensor movements occur, associated with
soft tissue movement. On the other hand, a complete characterization of the physical
exercises performed can be achieved with four inertial sensors and the appropriate algorithms.
In this way, anatomical information can be obtained, as well as quantitative
and qualitative information on the execution of physical therapies through the use of
inertial sensors
The Use of Wearable Inertial Motion Sensors in Human Lower Limb Biomechanics Studies: A Systematic Review
Wearable motion sensors consisting of accelerometers, gyroscopes and magnetic sensors are readily available nowadays. The small size and low production costs of motion sensors make them a very good tool for human motions analysis. However, data processing and accuracy of the collected data are important issues for research purposes. In this paper, we aim to review the literature related to usage of inertial sensors in human lower limb biomechanics studies. A systematic search was done in the following search engines: ISI Web of Knowledge, Medline, SportDiscus and IEEE Xplore. Thirty nine full papers and conference abstracts with related topics were included in this review. The type of sensor involved, data collection methods, study design, validation methods and its applications were reviewed
Gait analysis in a box: A system based on magnetometer-free IMUs or clusters of optical markers with automatic event detection
Gait analysis based on full-body motion capture technology (MoCap) can be used in rehabilitation to aid in decision making during treatments or therapies. In order to promote the use of MoCap gait analysis based on inertial measurement units (IMUs) or optical technology, it is necessary to overcome certain limitations, such as the need for magnetically controlled environments, which affect IMU systems, or the need for additional instrumentation to detect gait events, which affects IMUs and optical systems. We present a MoCap gait analysis system called Move Human Sensors (MH), which incorporates proposals to overcome both limitations and can be configured via magnetometer-free IMUs (MH-IMU) or clusters of optical markers (MH-OPT). Using a test–retest reliability experiment with thirty-three healthy subjects (20 men and 13 women, 21.7 ± 2.9 years), we determined the reproducibility of both configurations. The assessment confirmed that the proposals performed adequately and allowed us to establish usage considerations. This study aims to enhance gait analysis in daily clinical practice
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