Use Of Inertial Sensors Research Articles

Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling.

The emergence of motion sensors as a tool that provides objective motor performance data on individuals afflicted with Parkinson's disease offers an opportunity to expand the horizon of clinical care for this neurodegenerative condition. Subjective clinical scales and patient based motor diaries have limited clinometric properties and produce a glimpse rather than continuous real time perspective into motor disability. Furthermore, the expansion of machine learn algorithms is yielding novel classification and probabilistic clinical models that stand to change existing treatment paradigms, refine the application of advance therapeutics, and may facilitate the development and testing of disease modifying agents for this disease. We review the use of inertial sensors and machine learning algorithms in Parkinson's disease.

KINEMATIC ANALYSIS BY GENDER IN DIFFERENT JUMP TESTS BASED ON A SMARTPHONE INERTIAL SENSOR

ABSTRACT Introduction: Vertical jump tests can be used as estimators of muscular power, physical capacity, motor development and functional capacity. The ability to jump can be analyzed with different methods, including the use of inertial sensors. Objective: To describe and analyze kinematic characteristics using the inertial sensor integrated into the iPhone 4S® and jump contact mat variables in the squat jump (SJ) and countermovement jump (CMJ) tests, and to determine the interaction between kinetic and kinematic variables. Methods: A cross-sectional study was conducted with 27 healthy young adults. The primary outcome measures were linear acceleration, flight time, contact time, jump height and dynamometry of the knee extensors. Spearman's rho was used to investigate the correlation between variables. The Mann–Whitney U rank-sum test was used for the analysis of intergender variance. Results: The greatest difference between groups (gender) was in the dynamometry variables (p<0.001) and contact mat variables (p<0.001). Between the jump tests, the greatest difference between groups (gender) was in the CMJ test (p<0.001). Conclusion: The inertial sensor embedded in the smartphone demonstrated a correlation with the jump mat and the dynamometry. Finally, the higher kinetic and kinematic scores observed in the jumps performed by male participants than in those performed by female participants suggest that they can be used to better characterize their jumping profile. Level of Evidence IV; Diagnostic Studies - Investigating a Diagnostic Test.

INDOOR PHOTOGRAMMETRY AIDED WITH UWB NAVIGATION

Abstract. The subject of photogrammetric surveying with mobile devices, in particular smartphones, is becoming of significant interest in the research community. Nowadays, the process of providing 3D point clouds with photogrammetric procedures is well known. However, external information is still typically needed in order to move from the point cloud obtained from images to a 3D metric reconstruction. This paper investigates the integration of information provided by an UWB positioning system with visual based reconstruction to produce a metric reconstruction. Furthermore, the orientation (with respect to North-East directions) of the obtained model is assessed thanks to the use of inertial sensors included in the considered UWB devices. Results of this integration are shown on two case studies in indoor environments.

Utilización de sensores inerciales para la evaluación de la movilidad espinal en pacientes con espondiloartritis axial

IntroductionAxial Spondyloarthritis (AxSpa) is a rheumatic disease that affects the entire spine of the patient (lumbar, dorsal and cervical) and produces a reduction of mobility as the disease progresses. Several metrological tools have been proposed to assess this mobility. The objective of our study is to evaluate the use of inertial sensors “Inertial Measuring Unit” (IMU) as a tool for the measurement of spinal mobility in patients with AxSpA. Material and methodDescriptive and transversal study where spinal mobility is defined by cervical and dorsal-lumbar movement in frontal, lateral and transverse planes; by means of non-probabilistic sampling, 7 patients with AxSpA and 7 healthy subjects were recruited as control group. Mobility was measured synchronously with a system based on inertial motion sensors (IMU) and another video-based motion capture system (UCOTrack©). In addition, measurements of conventional metrology. Finally the BASMI (Bath Ankylosing Spondylitis Metrology Index), UCOASMI (University of Cordoba Ankilosing Spondylitis Metrology Index) and iUCOASMI index were obtained. ResultsBoth the individual measures obtained with the IMU and the composite index obtained from these measures, the iUCOASMI, show good reliability and concordance with intraclass correlation coefficients (ICC) above .7 (ICC˃.95 in the patient group), and an excellent correlation (P<.01) between iUCOASMI with UCOASMI and BASMI. In an adjustment of linear correlation of iUCOASMI/UCOASMI a R2 of .97 was obtained. DiscussionResults obtained shown that inertial sensors could be useful for analyzing spinal mobility in patients with AxSpA in a more accurate and reliable way than conventional metrology, and more flexible and economical than other advanced systems.

The Future of Classification in Wheelchair Sports: Can Data Science and Technological Advancement Offer an Alternative Point of View?

Classification is a defining factor for competition in wheelchair sports, but it is a delicate and time-consuming process with often questionable validity. New inertial sensor-based measurement methods applied in match play and field tests allow for more precise and objective estimates of the impairment effect on wheelchair-mobility performance. The aim of the present research was to evaluate whether these measures could offer an alternative point of view for classification. Six standard wheelchair-mobility performance outcomes of different classification groups were measured in match play (n = 29), as well as best possible performance in a field test (n = 47). In match results, a clear relationship between classification and performance level is shown, with increased performance outcomes in each adjacent higher-classification group. Three outcomes differed significantly between the low- and mid-classified groups, and 1, between the mid- and high-classified groups. In best performance (field test), there was a split between the low- and mid-classified groups (5 out of 6 outcomes differed significantly) but hardly any difference between the mid- and high-classified groups. This observed split was confirmed by cluster analysis, revealing the existence of only 2 performance-based clusters. The use of inertial sensor technology to obtain objective measures of wheelchair-mobility performance, combined with a standardized field test, produced alternative views for evidence-based classification. The results of this approach provide arguments for a reduced number of classes in wheelchair basketball. Future use of inertial sensors in match play and field testing could enhance evaluation of classification guidelines, as well as individual athlete performance.

Walking algorithm for a robotic transfemoral prosthesis capable of walking pattern recognition and posture stabilization

This paper describes a walking pattern generation algorithm for a robotic transfemoral prosthesis that is synchronized with the walking motion of a transfemoral amputee, and posture stabilization for ground adaptation and maintaining balance on inclined grounds. The developed robotic transfemoral prosthesis in this study has a knee joint and ankle roll/pitch joints for walking on complex slopes. The walking motion data obtained from the motion capture system are used as the standard walking pattern data to accurately imitate the inherent gait of the wearer. Walking intention, percent of gait cycle (PGC), and walking stride are predicted through two inertial sensors attached at both thighs, and the joint angles of the robotic transfemoral prosthesis are then generated in real-time from the PGC and the standard walking pattern data. Additionally, variable impedance control and zero moment point (ZMP) control are carried out with a force/torque sensor for posture stabilization against variable ground slopes, and ground slope compensation and disturbance rejection are also done with the use of inertial sensors at the foot and shank. Consequently, the performance of the walking pattern generation algorithm and posture stabilization control was verified through walking experiments of an author on an inclined treadmill.

Identification of activities of daily living in tremorous patients using inertial sensors

Tremor and voluntary movement of the upper limb are separated from IMUs signals.Seven daily living tasks are classified with an accuracy of 86%.Grain (precise or gross) of daily living tasks is identified with 79% accuracy.Direction (distal/ proximal) of daily living tasks is identified with 89% accuracy.Tremor kinematics can be coupled with the kind of task where it appears. BACKGROUNDMuch attention has been given to the use of inertial sensors for remote monitoring of individuals suffering from neurological pathologies. However, the focus has been mostly on the detection of symptoms like tremor or dyskinesia, not on identifying specific activities carried out by subjects. The objective of this study was to develop an automated segmentation and recognition methodology, from inertial sensor data, to identify tasks and motor patterns during activities of daily living. This will enable clinicians to contextualize the symptoms of these diseases and improve their treatment. METHODSWe designed and tested a methodology to automatically label continuous upper-limb activity from IMUs (Inertial Measurement Units). Three classification problems are considered: the task itself, the precision level required by the task with respect to movement (fine or gross) and the trajectory of the task (distal or proximal). These problems were identified by tremor experts as clinically important aspects to be monitored in tremor patients while performing daily activities. The proposed methodology reveals the relation between the functional context or activity and the patient's on-going tremor to clinicians. RESULTSOverall task identification rate was 86%. Task precision and task trajectory are classified with 79% and 89% accuracy, respectively. Aligning the semantic nature of the activity with the tremor location and intensity can also provide novel and relevant information for clinical monitoring of tremor and help clinicians and researchers as a useful tool to develop new therapies or strategies and novel anti-tremor medications to combat context dependent tremor. CONCLUSIONSThe present study describes the development of a comprehensive methodology based on machine learning techniques to segment and detect activities of daily living in people with tremor using inertial sensors, which aims at facilitating detailed interpretation of tremor movements by neurologists.

Evaluating the Use of Inertial-Magnetic Sensors to Assess Fatigue in Boxing During Intensive Training

Automating measures of performance can allow for heightened understanding of how an athlete is performing, not only during a session but over time. Physical and mental performance degrades in high-intensity skilled sports like boxing as the athlete fatigues. Although the use of low cost, ubiquitous inertial sensors have been reported effective for performance classification in boxing, no assessment of a boxer's efficacy has been reported under fatigue conditions. This letter evaluates the use of inertial sensors for automatic classification of fatigue by assessing the punch consistency in terms of pitch angle, punch force through acceleration, and hand speed, using the inverse time between punches. To achieve this, bespoke software was created in MATLAB, aided by the use of an attitude and heading reference system orientation filter. Six right-handed male elite boxers from the Pacific Nations, in preparation for the 2018 Gold Coast Commonwealth Games, Australia, consented to participate in the study. A noticeable decrease in performance for both hand speed (inverse time between punches) and force production (acceleration) was observed over time during intensive training, resulting in a Pearson's correlation coefficient of r 0.97 for the acceleration component and r 0.89 for the timing component. Inertial-magnetic sensors, with bespoke software, were experimentally found to be an effective tool for the automatic classification of boxing fatigue performance metrics. This study was conducted under ethical approval (ENG1413HREC).

209 QUANTITATIVE ANALYSIS OF THE TIMED UP AND GO IN ELDERLY PATIENTS POST HIP FRACTURE IN A SUB-ACUTE REHABILITATION SETTING

Introduction The Timed Up and Go is an outcome measure, which has been shown to be reliable and valid to assess change in patients post hip fracture. Advances in technology have included the use of inertial sensors for the quantitative analysis of the Timed Up and Go (QTUG) and enhance the scope for harnessing information from the test. However no studies have investigated their use in hip fracture patients. Aims and Objectives The aim of this study was to investigate the use of the QTUG in the assessment of hip fracture patients in a sub-acute geriatric rehabilitation setting. This included investigating which components of the QTUG were sensitive to change pre and post rehab and investigating the use of the QTUG as a measure of falls risk and frailty level. Methods A prospective cohort study design was employed using a sample of convenience. Sixteen participants post hip fracture admitted to an inpatient post-acute rehabilitation unit were assessed on admission and at discharge using the QTUG. Individual components of QTUG (e.g. sit to stand, walk, turn time) were compared between admission and discharge. Falls risk and frailty level were also assessed using QTUG and these were compared to secondary measures of Tinetti Gait and Balance Assessment and Clinical Frailty Scale (CFS). Results There was a significant improvement in the main outcome measure of overall QTUG time between admission and discharge of 9.3 (95% CI 5.6 to 12.9) seconds with a p value

A Novel AHRS Inertial Sensor-Based Algorithm for Wheelchair Propulsion Performance Analysis

With the increasing rise of professionalism in sport, athletes, teams, and coaches are looking to technology to monitor performance in both games and training in order to find a competitive advantage. The use of inertial sensors has been proposed as a cost effective and adaptable measurement device for monitoring wheelchair kinematics; however, the outcomes are dependent on the reliability of the processing algorithms. Though there are a variety of algorithms that have been proposed to monitor wheelchair propulsion in court sports, they all have limitations. Through experimental testing, we have shown the Attitude and Heading Reference System (AHRS)-based algorithm to be a suitable and reliable candidate algorithm for estimating velocity, distance, and approximating trajectory. The proposed algorithm is computationally inexpensive, agnostic of wheel camber, not sensitive to sensor placement, and can be embedded for real-time implementations. The research is conducted under Griffith University Ethics (GU Ref No: 2016/294).

Indoor Pedestrian Tracking by On-Body Multiple Receivers

During the past years, the development of indoor localization systems has been a hot topic in research, because the global navigation satellite systems suffer from a significant performance degradation due to the fact that the line of sight to the satellites is not available. The proposed system employs the received signal strength indicator from multiple anchor nodes from an operating wireless sensor network (WSN). In addition, we place multiple receivers around the user’s body and thanks to machine learning techniques; we are able to estimate the distance and angle between the user and any of the anchor nodes of the WSN. This allows us to estimate the heading of the user without the use of inertial sensors or magnetometers. Finally, the user’s position estimation is refined using an extended Kalman filter that considers the constant velocity kinematic model. The system has been validated in multiple real scenarios obtaining a root mean squared error around the meter for the different tests performed.

Inertial Sensor Technology for Elite Swimming Performance Analysis: A Systematic Review.

Technical evaluation of swimming performance is an essential factor of elite athletic preparation. Novel methods of analysis, incorporating body worn inertial sensors (i.e., Microelectromechanical systems, or MEMS, accelerometers and gyroscopes), have received much attention recently from both research and commercial communities as an alternative to video-based approaches. This technology may allow for improved analysis of stroke mechanics, race performance and energy expenditure, as well as real-time feedback to the coach, potentially enabling more efficient, competitive and quantitative coaching. The aim of this paper is to provide a systematic review of the literature related to the use of inertial sensors for the technical analysis of swimming performance. This paper focuses on providing an evaluation of the accuracy of different feature detection algorithms described in the literature for the analysis of different phases of swimming, specifically starts, turns and free-swimming. The consequences associated with different sensor attachment locations are also considered for both single and multiple sensor configurations. Additional information such as this should help practitioners to select the most appropriate systems and methods for extracting the key performance related parameters that are important to them for analysing their swimmers’ performance and may serve to inform both applied and research practices.

Studying upper-limb kinematics using inertial sensors: a cross-sectional study.

BackgroundIn recent years, there has been a great interest in analyzing upper-limb kinematics in order to investigate scapulohumeral rhythm, as its alteration has been associated with shoulder joint complex injuries. The use of inertial sensors is presented as a convenient and portable analysis method for studying kinematics in terms of angular mobility and linear acceleration. The aim of this study was to analyze upper-limbs kinematics in the three anatomical axes, obtained by inertial sensors.ResultsDescriptive graphics of analytical tasks performed were obtained. The main difference in mobility between the scapula and humerus was found in pitch axis for abduction ({bar{text{X}}} = 107.6°, SD = 9.3°) and flexion ({bar{text{X}}} = 113.1°, SD = 9.3°).ConclusionThe use of inertial sensors for human kinematics analysis is favorable. Although this study identified movement patterns, and supports inertial sensors as a useful device to analyze upper-limb kinematics, further studies with subjects with shoulder pathology to establish differences in movement patterns and scapulohumeral rhythm between healthy and pathological shoulders should be carried out.

An investigation into the use of inertial sensors to quantify joint position sense

An investigation into the use of inertial sensors to quantify joint position sense

Quantitative assessment of gait parameters in horses: Useful for aiding clinical decision making?

SummaryGait analysis in horses has moved from the laboratory into the outside world enabling researchers and clinicians to make use of quantitative measurements to provide evidence aiding clinical decision making. In this article we explain how the two major visual signs of lameness – head nod and hip hike –are linked to the mechanics of lameness. These parameters should be regarded as well suited for incorporation into clinical decision making being easily measurable e.g. with inertial sensors under nonlaboratory conditions and representative of the underlying mechanical changes. A brief overview of the results of validation studies of the use of inertial sensors for quantification of movement symmetry shows that accuracy, precision and repeatability with current sensor generations are in the order of 3–7 mm; this is below the reported detection threshold for movement symmetries ‘by eye’. Recent studies with inertial sensors have investigated conditions relevant to the clinical lameness examination: lungeing, flexion tests and diagnostic analgesia. Lungeing introduces kinematic adaptations rendering head and pelvic movement asymmetrical, even in sound horses. First guideline values are available for changes after flexion tests and diagnostic analgesia, and we understand which parameters show consistent changes. This is crucial for investigating the effects in horses with different orthopaedic deficits. In conclusion, the current inertial sensor systems, while not quantifying all the gait adaptations seen in lame horses, characterise the major mechanical changes affecting fore‐ and hindlimb lame horses. In order to avoid misinterpretation of the generated data, users should have a thorough understanding of the gait parameters and be aware of sensor limitations in terms of accuracy, precision and repeatability.

Use of inertial sensors as devices for upper limb motor monitoring exercises for motor rehabilitation

This paper presents the development of a system that uses inertial sensors, wireless transceivers and virtual models to monitor the exercises of motor rehabilitation of the upper limbs based on Kabat’s method. This method involves performing rehabilitation complex exercises that cannot be easily reproduced by the patient, requiring permanent assistance of a qualified professional. However, it is very expensive to have a professional expert assisting the patient throughout the treatment. Therefore, the development of technologies to monitor this type of exercise is necessary. The Kabat’s method has several applications, e.g. in motor rehabilitation of stroke patients. Stroke is considered the second most common cardiovascular disorder and affects about 9.6 million people in Europe alone, and an estimated 6 million people worldwide die from this disorder. Also, the natural aging process increases the number of strokes, and the demand for healthcare and motor rehabilitation services. To minimize this problem, we propose an experimental system consisting of inertial sensors, wireless transceivers and virtual models according to the models of Denavit & Hartenberg and Euler Angles & Tait Bryan. Through inertial sensors, this system can characterize the movement performed by the patient, compare it with a predefined motion and then indicate if the motor system performed the correct movement. The patients monitor their own movements and the movement pattern (correct movement). All movements are stored in a database allowing continuous checking by a qualified professional. Several experimental tests have shown that the average system error was 0.97°, which is suitable to the proposed system.

Auto detection and segmentation of physical activities during a Timed-Up-and-Go (TUG) task in healthy older adults using multiple inertial sensors.

BackgroundRecently, much attention has been given to the use of inertial sensors for remote monitoring of individuals with limited mobility. However, the focus has been mostly on the detection of symptoms, not specific activities. The objective of the present study was to develop an automated recognition and segmentation algorithm based on inertial sensor data to identify common gross motor patterns during activity of daily living.MethodA modified Time-Up-And-Go (TUG) task was used since it is comprised of four common daily living activities; Standing, Walking, Turning, and Sitting, all performed in a continuous fashion resulting in six different segments during the task. Sixteen healthy older adults performed two trials of a 5 and 10 meter TUG task. They were outfitted with 17 inertial motion sensors covering each body segment. Data from the 10 meter TUG were used to identify pertinent sensors on the trunk, head, hip, knee, and thigh that provided suitable data for detecting and segmenting activities associated with the TUG. Raw data from sensors were detrended to remove sensor drift, normalized, and band pass filtered with optimal frequencies to reveal kinematic peaks that corresponded to different activities. Segmentation was accomplished by identifying the time stamps of the first minimum or maximum to the right and the left of these peaks. Segmentation time stamps were compared to results from two examiners visually segmenting the activities of the TUG.ResultsWe were able to detect these activities in a TUG with 100% sensitivity and specificity (n = 192) during the 10 meter TUG. The rate of success was subsequently confirmed in the 5 meter TUG (n = 192) without altering the parameters of the algorithm. When applying the segmentation algorithms to the 10 meter TUG, we were able to parse 100% of the transition points (n = 224) between different segments that were as reliable and less variable than visual segmentation performed by two independent examiners.ConclusionsThe present study lays the foundation for the development of a comprehensive algorithm to detect and segment naturalistic activities using inertial sensors, in hope of evaluating automatically motor performance within the detected tasks.

Reliability of the step phase detection using inertial measurement units: pilot study.

The use of inertial sensors for the gait event detection during a long-distance walking, for example, on different surfaces and with different walking patterns, is important to evaluate the human locomotion. Previous studies demonstrated that gyroscopes on the shank or foot are more reliable than accelerometers and magnetometers for the event detection in case of normal walking. However, these studies did not link the events with the temporal parameters used in the clinical practice; furthermore, they did not clearly verify the optimal position for the sensors depending on walking patterns and surface conditions. The event detection quality of the sensors is compared with video, used as ground truth, according to the parameters proposed by the Gait and Clinical Movement Analysis Society. Additionally, the performance of the sensor on the foot is compared with the one on the shank. The comparison is performed considering both normal walking and deviations to the walking pattern, on different ground surfaces and with or without constraints on movements. The preliminary results show that the proposed methodology allows reliable detection of gait events, even in case of abnormal footfall and in slipping surface conditions, and that the optimal location to place the sensors is the shank.

Using Inertial Sensors in Smartphones for Curriculum Experiments of Inertial Navigation Technology

Inertial technology has been used in a wide range of applications such as guidance, navigation, and motion tracking. However, there are few undergraduate courses that focus on the inertial technology. Traditional inertial navigation systems (INS) and relevant testing facilities are expensive and complicated in operation, which makes it inconvenient and risky to perform teaching experiments with such systems. To solve this issue, this paper proposes the idea of using smartphones, which are ubiquitous and commonly contain off-the-shelf inertial sensors, as the experimental devices. A series of curriculum experiments are designed, including the Allan variance test, the calibration test, the initial leveling test and the drift feature test. These experiments are well-selected and can be implemented simply with the smartphones and without any other specialized tools. The curriculum syllabus was designed and tentatively carried out on 14 undergraduate students with a science and engineering background. Feedback from the students show that the curriculum can help them gain a comprehensive understanding of the inertial technology such as calibration and modeling of the sensor errors, determination of the device attitude and accumulation of the sensor errors in the navigation algorithm. The use of inertial sensors in smartphones provides the students the first-hand experiences and intuitive feelings about the function of inertial sensors. Moreover, it can motivate students to utilize ubiquitous low-cost sensors in their future research.

Kinematic measurements for the characterization of the dynamics of hopping

The study of human movement requires the accurate measurement of the forces and of the kinematic quantities involved. Here we consider hopping, that deserves great attention in the literature, since it combines the activation of a complex sensorimotor control with the relative simplicity of the gesture. The movement of the center of mass and the contact conditions are of special interest. Thus a measuring system that combines digital image acquisition, force measurement based on a force plate and extensive use of inertial sensors, for linear and rotational motion, has been developed and characterized. The results concerning the accurate measurement of contact conditions are specially outlined.