661 research outputs found

    Assessing the Utility of a Video-Based Motion Capture Alternative in the Assessment of Lumbar Spine Planar Angular Joint Kinematics

    Get PDF
    Markerless motion capture is a novel technique to measure human movement kinematics. The purpose of this research is to evaluate the markerless algorithm, DeepLabCut (DLC) against a 3D motion capture system (Vicon Motion Systems Ltd., Oxford, UK) in the analysis of planar spine and elbow flexion-extension movement. Data were acquired concurrently from DLC and Vicon for all movements. A novel DLC model was trained using data derived from a subset of participants (training group). Accuracy and precision were assessed from data derived from the training group as well as in a new set of participants (testing group). Two-way SPM ANOVAs were used to detect significant differences between the training vs. testing sets, capture methods (Vicon vs. DLC), as well as potential higher order interaction effect between these independent variables in the estimation of flexion extension angles and variability. No significant differences were observed in any planar angles, nor were any higher order interactions observed between each motion capture modality and the training vs. testing datasets. Bland Altman plots were also generated to depict the mean bias and level of agreement between DLC and Vicon for both training, and testing datasets. Supplemental analyses, suggest that these results are partially affected by the alignment of each participant’s body segments with respect to each planar reference frame. This research suggests that DLC-derived planar kinematics of both the elbow and lumbar spine are of acceptable accuracy and precision when compared to conventional laboratory gold-standards (Vicon)

    Development of a mobile technology system to measure shoulder range of motion

    Get PDF
    In patients with shoulder movement impairment, assessing and monitoring shoulder range of motion is important for determining the severity of impairments due to disease or injury and evaluating the effects of interventions. Current clinical methods of goniometry and visual estimation require an experienced user and suffer from low inter-rater reliability. More sophisticated techniques such as optical or electromagnetic motion capture exist but are expensive and restricted to a specialised laboratory environment.;Inertial measurement units (IMU), such as those within smartphones and smartwatches, show promise as tools bridge the gap between laboratory and clinical techniques and accurately measure shoulder range of motion during both clinic assessments and in daily life.;This study aims to develop an Android mobile application for both a smartphone and a smartwatch to assess shoulder range of motion. Initial performance characterisation of the inertial sensing capabilities of both a smartwatch and smartphone running the application was conducted against an industrial inclinometer, free-swinging pendulum and custom-built servo-powered gimbal.;An initial validation study comparing the smartwatch application with a universal goniometer for shoulder ROM assessment was conducted with twenty healthy participants. An impaired condition was simulated by applying kinesiology tape across the participants shoulder girdle. Agreement, intra and inter-day reliability were assessed in both the healthy and impaired states.;Both the phone and watch performed with acceptable accuracy and repeatability during static (within ±1.1°) and dynamic conditions where it was strongly correlated to the pendulum and gimbal data (ICC > 0.9). Both devices could perform accurately within optimal responsiveness range of angular velocities compliant with humerus movement during activities of daily living (frequency response of 377°/s and 358°/s for the phone and watch respectively).;The concurrent agreement between the watch and the goniometer was high in both healthy and impaired states (ICC > 0.8) and between measurement days (ICC > 0.8). The mean absolute difference between the watch and the goniometer were within the accepted minimal clinically important difference for shoulder movement (5.11° to 10.58°).;The results show promise for the use of the developed Android application to be used as a goniometry tool for assessment of shoulder ROM. However, the limits of agreement across all the tests fell out with the acceptable margin and further investigation is required to determine validity. Evaluation of validity in clinical impairment patients is also required to assess the feasibility of the use of the application in clinical practice.In patients with shoulder movement impairment, assessing and monitoring shoulder range of motion is important for determining the severity of impairments due to disease or injury and evaluating the effects of interventions. Current clinical methods of goniometry and visual estimation require an experienced user and suffer from low inter-rater reliability. More sophisticated techniques such as optical or electromagnetic motion capture exist but are expensive and restricted to a specialised laboratory environment.;Inertial measurement units (IMU), such as those within smartphones and smartwatches, show promise as tools bridge the gap between laboratory and clinical techniques and accurately measure shoulder range of motion during both clinic assessments and in daily life.;This study aims to develop an Android mobile application for both a smartphone and a smartwatch to assess shoulder range of motion. Initial performance characterisation of the inertial sensing capabilities of both a smartwatch and smartphone running the application was conducted against an industrial inclinometer, free-swinging pendulum and custom-built servo-powered gimbal.;An initial validation study comparing the smartwatch application with a universal goniometer for shoulder ROM assessment was conducted with twenty healthy participants. An impaired condition was simulated by applying kinesiology tape across the participants shoulder girdle. Agreement, intra and inter-day reliability were assessed in both the healthy and impaired states.;Both the phone and watch performed with acceptable accuracy and repeatability during static (within ±1.1°) and dynamic conditions where it was strongly correlated to the pendulum and gimbal data (ICC > 0.9). Both devices could perform accurately within optimal responsiveness range of angular velocities compliant with humerus movement during activities of daily living (frequency response of 377°/s and 358°/s for the phone and watch respectively).;The concurrent agreement between the watch and the goniometer was high in both healthy and impaired states (ICC > 0.8) and between measurement days (ICC > 0.8). The mean absolute difference between the watch and the goniometer were within the accepted minimal clinically important difference for shoulder movement (5.11° to 10.58°).;The results show promise for the use of the developed Android application to be used as a goniometry tool for assessment of shoulder ROM. However, the limits of agreement across all the tests fell out with the acceptable margin and further investigation is required to determine validity. Evaluation of validity in clinical impairment patients is also required to assess the feasibility of the use of the application in clinical practice

    Development of a miniaturized motion sensor for tracking warning signs of low-back pain

    Full text link
    Low-back pain (LBP) is a widespread disease which can also be highly disabling, but physicians lack of basic understanding and diagnosis tools. During this study, we have designed and built a new wearable device capable of detecting features helpful in LBP follow-up while being non-invasive. The device has been carefully validated, and shows good metrological features, with small noise level (σ\sigma = 1{\textdegree}) and no observable drift. Two simple exercises were proposed to two young volunteers, one of them with LBP history. These exercises are designed to target two characteristics: the lumbar lordosis angle and the hip \& shoulder dissociation. Even if no general rules can be extracted from this study, we have shown that Inertial Measurement Units (IMU) are able to pick up those characteristics and the obtained values are meaningful refereeing to LBP disease. Henceforth, we are confident in going to clinical studies to investigate the link between back related feature and LBP, in particular the hip \& shoulder dissociation which is poorly documented

    Evaluation of the Performances of Two Wearable Systems for Gait Analysis: A Pilot Study

    Get PDF
    Wearable sensor systems to perform human motion analysis are receiving increasing attention in different application fields. Among wearable sensors, inertial sensors have promising features. However, before they can be employed routinely in clinical applications, it is important to evaluate their reliability. Gait analysis was performed on one male volunteer: data were simultaneously collected with HGait System, based on magnetic and inertial measurement sensor units system, and with STEP32, a commercial electromechanical system already used in clinics. Spatio temporal parameters and joint kinematics in the sagittal plane obtained with H-Gait and STEP32 are compared. The MIMUs system provides a reliable estimation of spatiotemporal parameters, and acceptable hip and knee kinematic curves, while ankle joint measurements must be improved to be clinically useful

    An automatic wearable multi-sensor based gait analysis system for older adults.

    Get PDF
    Gait abnormalities in older adults are very common in clinical practice. They lead to serious adverse consequences such as falls and injury resulting in increased care cost. There is therefore a national imperative to address this challenge. Currently gait assessment is done using standardized clinical tools dependent on subjective evaluation. More objective gold standard methods (motion capture systems such as Qualisys and Vicon) to analyse gait rely on access to expensive complex equipment based in gait laboratories. These are not widely available for several reasons including a scarcity of equipment, need for technical staff, need for patients to attend in person, complicated time consuming procedures and overall expense. To broaden the use of accurate quantitative gait monitoring and assessment, the major goal of this thesis is to develop an affordable automatic gait analysis system that will provide comprehensive gait information and allow use in clinic or at home. It will also be able to quantify and visualize gait parameters, identify gait variables and changes, monitor abnormal gait patterns of older people in order to reduce the potential for falling and support falls risk management. A research program based on conducting experiments on volunteers is developed in collaboration with other researchers in Bournemouth University, The Royal Bournemouth Hospital and care homes. This thesis consists of five different studies toward addressing our major goal. Firstly, a study on the effects on sensor output from an Inertial Measurement Unit (IMU) attached to different anatomical foot locations. Placing an IMU over the bony prominence of the first cuboid bone is the best place as it delivers the most accurate data. Secondly, an automatic gait feature extraction method for analysing spatiotemporal gait features which shows that it is possible to extract gait features automatically outside of a gait laboratory. Thirdly, user friendly and easy to interpret visualization approaches are proposed to demonstrate real time spatiotemporal gait information. Four proposed approaches have the potential of helping professionals detect and interpret gait asymmetry. Fourthly, a validation study of spatiotemporal IMU extracted features compared with gold standard Motion Capture System and Treadmill measurements in young and older adults is conducted. The results obtained from three experimental conditions demonstrate that our IMU gait extracted features are highly valid for spatiotemporal gait variables in young and older adults. In the last study, an evaluation system using Procrustes and Euclidean distance matrix analysis is proposed to provide a comprehensive interpretation of shape and form differences between individual gaits. The results show that older gaits are distinguishable from young gaits. A pictorial and numerical system is proposed which indicates whether the assessed gait is normal or abnormal depending on their total feature values. This offers several advantages: 1) it is user friendly and is easy to set up and implement; 2) it does not require complex equipment with segmentation of body parts; 3) it is relatively inexpensive and therefore increases its affordability decreasing health inequality; and 4) its versatility increases its usability at home supporting inclusivity of patients who are home bound. A digital transformation strategy framework is proposed where stakeholders such as patients, health care professionals and industry partners can collaborate through development of new technologies, value creation, structural change, affordability and sustainability to improve the diagnosis and treatment of gait abnormalities

    Master of Science

    Get PDF
    thesisComputing and data acquisition have become an integral part of everyday life. From reading emails on cell phones to kids playing with motion sensing game consoles, we are surrounded with sensors and mobile computing devices. As the availability of powerful computing devices increases, applications in previously limited environments become possible. Training devices in rehabilitation are becoming increasingly common and more mobile. Community based rehabilitative devices are emerging that embrace these mobile advances. To further the flexibility of devices used in rehabilitation, research has explored the use of smartphones as a means to process data and provide feedback to the user. In combination with sensor embedded insoles, smartphones provide a powerful tool for the clinician in gathering data and as a standalone training tool in rehabilitation. This thesis presents the continuing research of sensor based insoles, feedback systems and increasing the capabilities of the Adaptive Real-Time Instrumentation System for Tread Imbalance Correction, or ARTISTIC, with the introduction of ARTISTIC 2.0. To increase the capabilities of the ARTISTIC an Inertial Measurement Unit (IMU) was added, which gave the system the ability to quantify the motion of the gait cycle and, more specifically, determine stride length. The number of sensors in the insole was increased from two to ten, as well as placing the microprocessor and a vibratory motor in the insole. The transmission box weight was reduced by over 50 percent and the volume by over 60 percent. Stride length was validated against a motion capture system and found the average stride length to be within 2.7 ± 6.9 percent. To continue the improvement of the ARTISTIC 2.0, future work will include implementing real-time stride length feedback

    Global positioning system: a new opportunity in physical activity measurement

    Get PDF
    Accurate measurement of physical activity is a pre-requisite to monitor population physical activity levels and design effective interventions. Global Positioning System (GPS) technology offers potential to improve the measurement of physical activity. This paper 1) reviews the extant literature on the application of GPS to monitor human movement, with a particular emphasis on free-living physical activity, 2) discusses issues associated with GPS use, and 3) provides recommendations for future research. Overall findings show that GPS is a useful tool to augment our understanding of physical activity by providing the context (location) of the activity and used together with Geographical Information Systems can provide some insight into how people interact with the environment. However, no studies have shown that GPS alone is a reliable and valid measure of physical activity

    Wearable haptic systems for the fingertip and the hand: taxonomy, review and perspectives

    Get PDF
    In the last decade, we have witnessed a drastic change in the form factor of audio and vision technologies, from heavy and grounded machines to lightweight devices that naturally fit our bodies. However, only recently, haptic systems have started to be designed with wearability in mind. The wearability of haptic systems enables novel forms of communication, cooperation, and integration between humans and machines. Wearable haptic interfaces are capable of communicating with the human wearers during their interaction with the environment they share, in a natural and yet private way. This paper presents a taxonomy and review of wearable haptic systems for the fingertip and the hand, focusing on those systems directly addressing wearability challenges. The paper also discusses the main technological and design challenges for the development of wearable haptic interfaces, and it reports on the future perspectives of the field. Finally, the paper includes two tables summarizing the characteristics and features of the most representative wearable haptic systems for the fingertip and the hand

    Evaluation of Angular Velocity Data from Inertial Measurement Units for Use in Clinical Settings

    Get PDF
    Evaluating the human gait cycle with inertial measurement units (IMU) may prove beneficial for applications such as diagnoses of musculoskeletal diseases and assessment of rehabilitation regimes. An IMU system is potentially applicable for diagnosing and assessing rehabilitation outcomes for a variety of neuromuscular diseases since it is small, portable, and less expensive than a camera system. IMUs directly measure angular velocity, whereas position data from a camera system must be processed twice to obtain this information. The purpose of this research is to determine repeatability of IMU angular velocity data, and agreement between angular velocity data from an IMU system and a camera system during normal gait. From this data, the feasibility of using IMU systems in clinical or rehabilitative settings for obtaining reliable angular velocity data will be determined. Lower limb motion data was collected simultaneously from six XSens MTx IMUs (XSens Technologies, Enschede, The Netherlands) and an 8-camera Qualisys Motion Capture system (Pro-Reflex, 240 Hz system). Each IMU consists of three orthogonal accelerometers, gyroscopes, and magnetometers. Data from 4 subjects (3 males, 2 females) were collected after an initialization technique before each trial to reduce effects of electro-magnetic interference with the IMUs. Knee joint angular velocities (Gx, Gy, Gz) corresponding to appropriate knee joint angles (flexion/extension, adduction/abduction, and internal/external rotations) from both systems were used in this analysis. Coefficients of variation (COV) were calculated for both IMU and camera data to determine variability of data from both systems. Knee joint Average angular velocities from both systems for each subject and limb were plotted together to visually evaluate correlation between data sets. F-test analyses were performed on linear models of the data to determine areas of co-linearity within the gait cycle, and at different intervals of angular velocities. The IMUs had lower COV\u27s than the camera system, likely due to the fact that the IMUs directly measure angular velocity, and camera system derives angular velocity from position data. However, these differences were not statistically different, likely due to variability within trials for individual subjects. Linearity between camera system and IMU angular velocity was visually observed only about the flexion/extension axis during segments of the gait cycle occurring from 0-4% (heel strike) and 65-100% (swing phase) of the gait cycle. Comparisons about the adduction/abduction and internal/external axes showed evidence of linearity for lower angular velocities. Linear regression statistics showed that the only correlational trend between the two systems was around 8-12% of the gait cycle for all three rotational axes. This may be due to drift of the IMU data. Although the camera system is the \u27gold standard\u27 in motion analysis, IMUs may be used for applications in which angular velocity for a flexion-extension movement at low joint angles is being evaluated. Future studies will include a larger sample population, and evaluate specific movements within human gait that affect drift of the IMUs. In addition, other IMU system designs could be evaluated for clinical use, and other algorithms that further reduce the effects of drift should be implemented

    Smart Technology for Telerehabilitation: A Smart Device Inertial-sensing Method for Gait Analysis

    Get PDF
    The aim of this work was to develop and validate an iPod Touch (4th generation) as a potential ambulatory monitoring system for clinical and non-clinical gait analysis. This thesis comprises four interrelated studies, the first overviews the current available literature on wearable accelerometry-based technology (AT) able to assess mobility-related functional activities in subjects with neurological conditions in home and community settings. The second study focuses on the detection of time-accurate and robust gait features from a single inertial measurement unit (IMU) on the lower back, establishing a reference framework in the process. The third study presents a simple step length algorithm for straight-line walking and the fourth and final study addresses the accuracy of an iPod’s inertial-sensing capabilities, more specifically, the validity of an inertial-sensing method (integrated in an iPod) to obtain time-accurate vertical lower trunk displacement measures. The systematic review revealed that present research primarily focuses on the development of accurate methods able to identify and distinguish different functional activities. While these are important aims, much of the conducted work remains in laboratory environments, with relatively little research moving from the “bench to the bedside.” This review only identified a few studies that explored AT’s potential outside of laboratory settings, indicating that clinical and real-world research significantly lags behind its engineering counterpart. In addition, AT methods are largely based on machine-learning algorithms that rely on a feature selection process. However, extracted features depend on the signal output being measured, which is seldom described. It is, therefore, difficult to determine the accuracy of AT methods without characterizing gait signals first. Furthermore, much variability exists among approaches (including the numbers of body-fixed sensors and sensor locations) to obtain useful data to analyze human movement. From an end-user’s perspective, reducing the amount of sensors to one instrument that is attached to a single location on the body would greatly simplify the design and use of the system. With this in mind, the accuracy of formerly identified or gait events from a single IMU attached to the lower trunk was explored. The study’s analysis of the trunk’s vertical and anterior-posterior acceleration pattern (and of their integrands) demonstrates, that a combination of both signals may provide more nuanced information regarding a person’s gait cycle, ultimately permitting more clinically relevant gait features to be extracted. Going one step further, a modified step length algorithm based on a pendulum model of the swing leg was proposed. By incorporating the trunk’s anterior-posterior displacement, more accurate predictions of mean step length can be made in healthy subjects at self-selected walking speeds. Experimental results indicate that the proposed algorithm estimates step length with errors less than 3% (mean error of 0.80 ± 2.01cm). The performance of this algorithm, however, still needs to be verified for those suffering from gait disturbances. Having established a referential framework for the extraction of temporal gait parameters as well as an algorithm for step length estimations from one instrument attached to the lower trunk, the fourth and final study explored the inertial-sensing capabilities of an iPod Touch. With the help of Dr. Ian Sheret and Oxford Brookes’ spin-off company ‘Wildknowledge’, a smart application for the iPod Touch was developed. The study results demonstrate that the proposed inertial-sensing method can reliably derive lower trunk vertical displacement (intraclass correlations ranging from .80 to .96) with similar agreement measurement levels to those gathered by a conventional inertial sensor (small systematic error of 2.2mm and a typical error of 3mm). By incorporating the aforementioned methods, an iPod Touch can potentially serve as a novel ambulatory monitor system capable of assessing gait in clinical and non-clinical environments
    • 

    corecore