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Author: Samaneh Davarzani
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Gait recognition systems have gained tremendous attention due to its potential applications in healthcare, criminal investigation, sports biomechanics, and so forth. A new solution to gait recognition tasks can be provided by wearable sensors integrated in wearable objects or mobile devices. In this research a sock prototype designed with embedded soft robotic sensors (SRS) is implemented to measure foot ankle kinematic and kinetic data during three experiments designed to track participants’ feet ankle movement. Deep learning and statistical methods have been employed to model SRS data against Motion capture system (MoCap) to determine their ability to provide accurate kinematic and kinetic data using SRS measurements. In the first study, the capacitance of SRS related to foot-ankle basic movements was quantified during the gait movements of twenty participants on a flat surface and a cross-sloped surface. I have conducted another study regarding kinematic features in which deep learning models were trained to estimate the joint angles in sagittal and frontal planes measured by a MoCap system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. The focus of the last study is measuring the kinetic features and the goal is determining the validity of SRS measurements, to this end the pressure data measured with SRS embedded into the sock prototype would be compared with the force plate data.
Author: Samaneh Davarzani
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Gait recognition systems have gained tremendous attention due to its potential applications in healthcare, criminal investigation, sports biomechanics, and so forth. A new solution to gait recognition tasks can be provided by wearable sensors integrated in wearable objects or mobile devices. In this research a sock prototype designed with embedded soft robotic sensors (SRS) is implemented to measure foot ankle kinematic and kinetic data during three experiments designed to track participants’ feet ankle movement. Deep learning and statistical methods have been employed to model SRS data against Motion capture system (MoCap) to determine their ability to provide accurate kinematic and kinetic data using SRS measurements. In the first study, the capacitance of SRS related to foot-ankle basic movements was quantified during the gait movements of twenty participants on a flat surface and a cross-sloped surface. I have conducted another study regarding kinematic features in which deep learning models were trained to estimate the joint angles in sagittal and frontal planes measured by a MoCap system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. The focus of the last study is measuring the kinetic features and the goal is determining the validity of SRS measurements, to this end the pressure data measured with SRS embedded into the sock prototype would be compared with the force plate data.
Author: Anup Nandy
Publisher: Academic Press
ISBN: 0323852467
Category : Technology & Engineering
Languages : en
Pages : 196
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Book Description
Modern Methods for Affordable Clinical Gait Analysis: Theories and Applications in Healthcare Systems is a handbook of techniques, tools and procedures for the study and improvement of human gait. It gives a concise description of clinical gait analysis, especially gait abnormality detection problems and therapeutic interventions using inexpensive devices. A brief demonstration on validation testing of these devices for its clinical applicability is also presented. Content coverage also includes step-by-step processing of the data acquired from these devices. Future perspectives of low-cost clinical gait assessment systems are explored. This book bridges the gap between engineering and biomedical fields as it diagnoses and monitors neuro-musculoskeletal abnormalities using the latest technologies. The authors discuss how early detection technology allows us to take precautionary measures, in order to delay the degeneration process, through development of a clinical gait analysis tool. One unique feature of this book is that it pays significant attention to the challenges of conducting gait analysis in developing countries with limited resources. This reference will guide you through setting up a low-cost gait analysis lab. It explores the relationship between vision-based pathological gait detection, the design of tools for gait diagnosis and therapeutic interventions. Provides a concise tutorial on affordable clinical gait analysis Analyses clinical validation of low-cost sensors for gait assessment Documents recent and state-of-the-art low-cost gait abnormality detection systems and therapeutic intervention procedures
Author: Robert LeMoyne
Publisher: Springer
ISBN: 9811056846
Category : Technology & Engineering
Languages : en
Pages : 142
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Book Description
This book provides visionary perspective and interpretation regarding the role of wearable and wireless systems for the domain of gait and reflex response quantification. These observations are brought together in their application to smartphones and other portable media devices to quantify gait and reflex response in the context of machine learning for diagnostic classification and integration with the Internet of things and cloud computing. The perspective of this book is from the first-in-the-world application of these devices, as in smartphones, for quantifying gait and reflex response, to the current state of the art. Dr. LeMoyne has published multiple groundbreaking applications using smartphones and portable media devices to quantify gait and reflex response.
Author: Matteo Zago
Publisher: Frontiers Media SA
ISBN: 2889665615
Category : Science
Languages : en
Pages : 328
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Book Description
Author: Yuchao Ma
Publisher:
ISBN:
Category : Intelligent sensors
Languages : en
Pages : 200
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Book Description
With the rapid integration of wireless sensing technologies and computational algorithms, various motion analysis applications have come to realization for use with wearable sensor devices. These technologies have attracted significant attention in recent years, due to their great potential in healthcare and wellness domains. This dissertation focuses on (1) enhancing the utilization of wearable sensing systems to advance personalized health examination, and (2) developing computational autonomy solutions to support accurate motion analysis for use in uncontrolled environments. Gait analysis is a typical application in motion analysis, which measures one's mobility for health assessment. We design and develop a shoe-integrated sensing system for continuous data collection and quantitative gait analysis. We analyze sensor data collected from patients with Rett Syndrome and glaucoma during a series of standard gait tests in two clinical studies. We present signal processing algorithms to obtain patients' biophysical information, and develop machine learning models using feature representation of the sensor data, to automatically identify pathological gait patterns. To scale up gait analysis applications to less controlled environments, we propose a platform-independent framework for gait cycle detection, a major task in gait analysis. We utilize physical properties of human gait to enable autonomous parameter learning and model reconfiguration as sensor platform properties such as bit resolution, sampling frequency, signal dynamic range, and sensor orientation change. To further support sensor-enabled motion analysis in dynamic settings, we propose two autonomous algorithms to improve the performance of machine learning models for activity recognition. We design an asynchronous transfer learning algorithm, to reduce the performance degradation of activity recognition models caused by considerable differences among various sensing platforms, as well as variations in movement patterns across users. In addition, we propose a novel non-parametric semi-supervised learning framework, to overcome the dependency on large amounts of labeled data for machine learning model training. The proposed framework employs a graph-based approach for sample selection and label inference, and a silhouette-based filtering strategy to finalize the obtained labels to construct a highly reliable activity recognition model.
Author: Shuo Gao
Publisher: Springer Nature
ISBN: 3030973328
Category : Technology & Engineering
Languages : en
Pages : 244
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Book Description
Wearable Systems Based Gait Monitoring and Analysis provides a thorough overview of wearable gait monitoring techniques and their use in health analysis. The text starts with an examination of the relationship between the human body’s physical condition and gait, and then introduces and explains nine mainstream sensing mechanisms, including piezoresistive, resistive, capacitive, piezoelectric, inductive, optical, air pressure, EMG and IMU-based architectures. Gait sensor design considerations in terms of geometry and deployment are also introduced. Diverse processing algorithms for manipulating sensors outputs to transform raw data to understandable gait features are discussed. Furthermore, gait analysis-based health monitoring demonstrations are given at the end of this book, including both medical and occupational applications. The book will enable students of biomedical engineering, electrical engineering, signal processing, and ergonomics and practitioners to understand the medical and occupational applications of engineering-based gait analysis and falling injury prevention methods.
Author: Eric Yun Xiao Ma
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Gait analysis is useful in characterizing impaired gait in patients with various neuromusculoskeletal disorders. Contemporary gait analysis is conducted using numerous motion capture cameras and force plates. However, this setup is restricted to a constrained and artificial testing environment, which may lead patients to make unnatural movements that poorly represent real-world human gait. We proposed a shoe-mounted gait analysis system composed of two 9-axis Inertial Measurement Units (IMUs), an IR sensor, and a pair of instrumented shoes. The system was validated against a state-of-the-art gait lab in a study with ten subjects. The results showed that the system was able to estimate the 3D kinematics, the global Centre of Pressure (COP), and gait parameters such as step length and gait phases acceptably well. This work provides a portable and unobtrusive method of performing lower-limb gait analysis in unconstrained and ambulatory environments.
Author: Raoul Hoffmann
Publisher: Logos Verlag Berlin GmbH
ISBN: 3832557482
Category :
Languages : en
Pages : 202
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Book Description
Gait analysis is valuable in medical research and diagnosis, by delivering information that helps in choosing methods of intervention and rehabilitation that are beneficial for a patient. In gait laboratories, cameras or IMUs are often used to gather gait patterns. This thesis explores the possibility of using sensors below the floor as a gait data source. These sensors measure changes in the electrical capacitance to recognise steps. The construction is designed for indoor environments and is hidden under common flooring layer types. Therefore, it is very robust and suitable for practical use in daily clinical routine. A formal framework was developed to represent the measurements, considering the special characteristics of this floor sensor. The data were then used as input for artificial neural networks that were applied on classification and regression tasks. In a feature construction and extraction approach, the spatial spread of footfalls was derived and used with a feed-forward neural network. Then, in a feature learning approach, the time series data was transformed into a local receptive field, and used with a recurrent neural network. Three studies were conducted for the goals to distinguish between people with low and high risk of falling, to estimate age, and to recognise walking challenges as an external gait intervention. The combination of a robust and hidden floor sensor and machine learning opens up the prospect of future applications in health and care.
Author: Tin-Chih Toly Chen
Publisher: Springer Nature
ISBN: 3031594436
Category :
Languages : en
Pages : 103
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Book Description
Author: Carlos A. Cifuentes
Publisher: Springer
ISBN: 3319340638
Category : Technology & Engineering
Languages : en
Pages : 125
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Book Description
This book presents the development of a new multimodal human-robot interface for testing and validating control strategies applied to robotic walkers for assisting human mobility and gait rehabilitation. The aim is to achieve a closer interaction between the robotic device and the individual, empowering the rehabilitation potential of such devices in clinical applications. A new multimodal human-robot interface for testing and validating control strategies applied to robotic walkers for assisting human mobility and gait rehabilitation is presented. Trends and opportunities for future advances in the field of assistive locomotion via the development of hybrid solutions based on the combination of smart walkers and biomechatronic exoskeletons are also discussed.
