scholarly journals Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 82 ◽  
Author(s):  
Udeni Jayasinghe ◽  
William S. Harwin ◽  
Faustina Hwang
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Wearable Sensors ◽  
Correlation Coefficients ◽  
Movement Analysis ◽  
Multiple Sensors ◽  
Sensor Orientation ◽  
Multiple Devices ◽  
Good Potential ◽  
Sensor Errors

Inertial sensors are a useful instrument for long term monitoring in healthcare. In many cases, inertial sensor devices can be worn as an accessory or integrated into smart textiles. In some situations, it may be beneficial to have data from multiple inertial sensors, rather than relying on a single worn sensor, since this may increase the accuracy of the analysis and better tolerate sensor errors. Integrating multiple sensors into clothing improves the feasibility and practicality of wearing multiple devices every day, in approximately the same location, with less likelihood of incorrect sensor orientation. To facilitate this, the current work investigates the consequences of attaching lightweight sensors to loose clothes. The intention of this paper is to discuss how data from these clothing sensors compare with similarly placed body worn sensors, with additional consideration of the resulting effects on activity recognition. This study compares the similarity between the two signals (body worn and clothing), collected from three different clothing types (slacks, pencil skirt and loose frock), across multiple daily activities (walking, running, sitting, and riding a bus) by calculating correlation coefficients for each sensor pair. Even though the two data streams are clearly different from each other, the results indicate that there is good potential of achieving high classification accuracy when using inertial sensors in clothing.

scholarly journals The Use of Wearable Sensors in Human Movement Analysis in Non-Swimming Aquatic Activities: A Systematic Review

2019 ◽  
Vol 16 (24) ◽  
pp. 5067
Author(s):  
Daniel A. Marinho ◽  
Henrique P. Neiva ◽  
Jorge E. Morais
Keyword(s):  
Systematic Review ◽  
Aquatic Environment ◽  
Web Of Science ◽  
Inertial Sensors ◽  
State Of The Art ◽  
Wearable Sensors ◽  
Movement Analysis ◽  
Human Movement ◽  
Dry Land ◽  
Swimming Kinematics

The use of smart technology, specifically inertial sensors (accelerometers, gyroscopes, and magnetometers), to analyze swimming kinematics is being reported in the literature. However, little is known about the usage/application of such sensors in other human aquatic exercises. As the sensors are getting smaller, less expensive, and simple to deal with (regarding data acquisition), one might consider that its application to a broader range of exercises should be a reality. The aim of this systematic review was to update the state of the art about the framework related to the use of sensors assessing human movement in an aquatic environment, besides swimming. The following databases were used: IEEE Xplore, Pubmed, Science Direct, Scopus, and Web of Science. Five articles published in indexed journals, aiming to assess human exercises/movements in the aquatic environment were reviewed. The data from the five articles was categorized and summarized based on the aim, purpose, participants, sensor’s specifications, body area and variables analyzed, and data analysis and statistics. The analyzed studies aimed to compare the movement/exercise kinematics between environments (i.e., dry land versus aquatic), and in some cases compared healthy to pathological participants. The use of sensors in a rehabilitation/hydrotherapy perspective may provide major advantages for therapists.

scholarly journals Assessment of Scapulothoracic, Glenohumeral, and Elbow Motion in Adhesive Capsulitis by Means of Inertial Sensor Technology: A Within-Session, Intra-Operator and Inter-Operator Reliability and Agreement Study

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 876 ◽  
Author(s):  
Liesbet De Baets ◽  
Stefanie Vanbrabant ◽  
Carl Dierickx ◽  
Rob van der Straaten ◽  
Annick Timmermans
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
External Rotation ◽  
Adhesive Capsulitis ◽  
Movement Analysis ◽  
Elbow Flexion ◽  
Sensor Technology ◽  
Flexion Extension ◽  
Agreement Study ◽  
Joint Range

Adhesive capsulitis (AC) is a glenohumeral (GH) joint condition, characterized by decreased GH joint range of motion (ROM) and compensatory ROM in the elbow and scapulothoracic (ST) joint. To evaluate AC progression in clinical settings, objective movement analysis by available systems would be valuable. This study aimed to assess within-session and intra- and inter-operator reliability/agreement of such a motion capture system. The MVN-Awinda® system from Xsens Technologies (Enschede, The Netherlands) was used to assess ST, GH, and elbow ROM during four tasks (GH external rotation, combing hair, grasping a seatbelt, placing a cup on a shelf) in 10 AC patients (mean age = 54 (±6), 7 females), on two test occasions (accompanied by different operators on second occasion). Standard error of measurements (SEMs) were below 1.5° for ST pro-retraction and 4.6° for GH in-external rotation during GH external rotation; below 6.6° for ST tilt, 6.4° for GH flexion-extension, 7.1° for elbow flexion-extension during combing hair; below 4.4° for GH ab-adduction, 13° for GH in-external rotation, 6.8° for elbow flexion-extension during grasping the seatbelt; below 11° for all ST and GH joint rotations during placing a cup on a shelf. Therefore, to evaluate AC progression, inertial sensors systems can be applied during the execution of functional tasks.

Methods for Improving Foot Displacement Measurements Calculated from Inertial Sensors

2011 ◽  
pp. 93-116 ◽  
Author(s):  
Edgar Charry ◽  
Daniel T.H. Lai
Keyword(s):  
Measurement System ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Human Movement ◽  
Sensor Data ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Band Pass ◽  
Sensor Errors ◽  
Displacement Measurements

The use of inertial sensors to measure human movement has recently gained momentum with the advent of low cost micro-electro-mechanical systems (MEMS) technology. These sensors comprise accelerometer and gyroscopes which measure accelerations and angular velocities respectively. Secondary quantities such as displacement can be obtained by integration of these quantities, a method which presents challenging issues due to the problem of accumulative sensor errors. This chapter investigates the spectral evaluation of individual sensor errors and looks at the effectiveness of minimizing these errors using static digital filters. The primary focus is on the derivation of foot displacement data from inertial sensor measurements. The importance of foot, in particular toe displacement measurements is evident in the context of tripping and falling which are serious health concerns for the elderly. The Minimum Toe Clearance (MTC) as an important gait variable for falls-risk prediction and assessment, and therefore the measurement variable of interest. A brief sketch of the current devices employing accelerometers and gyroscopes is presented, highlighting the problems and difficulties reported in literature to achieve good precision. These have been mainly due to the presence of sensor errors and the error accumulative process employed in obtaining displacement measurements. The investigation first proceeds to identify the location of these sensor errors in the frequency domain using the Fast Fourier Transform (FFT) on raw inertial sensor data. The frequency content of velocity and displacement measurements obtained from integrating the inertial data using a well known strap-down method is then explored. These investigations revealed that large sensor errors occurred mainly in the low frequency spectrum while white noise exists in all frequency spectra. The efficacy of employing a band-pass filter to remove a large portion of these errors and their effect on the derived displacements is elaborated on. The cross-correlation of the FFT power spectra from a highly accurate optical measurement system and processed sensor data is used as a metric to evaluate the performance of the band-pass filter at several stages of the processing stage. The motivation is that a more fundamental method would require less computational demand and could lead to more efficient implementations in low-power and systems with limited resources, so that portable sensor based motion measurement system would provide a good degree of measurement accuracy.

scholarly journals A novel use of inertial sensors to measure the craniocervical flexion range of motion associated to the craniocervical flexion test: an observational study

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Tomás Pérez-Fernández ◽  
Susan Armijo-Olivo ◽  
Sonia Liébana ◽  
Pablo José de la Torre Ortíz ◽  
Josué Fernández-Carnero ◽  
...  
Keyword(s):  
Range Of Motion ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Wearable Sensors ◽  
Interclass Correlation Coefficient ◽  
Interclass Correlation ◽  
Pressure Biofeedback ◽  
User Friendly ◽  
And Training ◽  
Flexion Test

Abstract Background The craniocervical flexion test (CCFT) is recommended when examining patients with neck pain related conditions and as a deep cervical retraining exercise option. During the execution of the CCFT the examiner should visually assess that the amount of craniocervical flexion range of motion (ROM) progressively increases. However, this task is very subjective. The use of inertial wearable sensors may be a user-friendly option to measure and objectively monitor the ROM. The objectives of our study were (1) to measure craniocervical flexion range of motion (ROM) associated with each stage of the CCFT using a wearable inertial sensor and to determine the reliability of the measurements and (2) to determine craniocervical flexion ROM targets associated with each stage of the CCFT to standardize their use for assessment and training of the deep cervical flexor (DCF) muscles. Methods Adults from a university community able to successfully perform the CCFT participated in this study. Two independent examiners evaluated the CCFT in two separate sessions. During the CCFT, a small wireless inertial sensor was adhered to the centre of the forehead to provide real-time monitoring and to record craniocervical flexion ROM. The intra- and inter-rater reliability of the assessment of craniocervical ROM was calculated. This study was approved by the Research Ethics Committee of CEU San Pablo University (236/17/08). Results Fifty-six participants (18 males, 23 females; mean [SD] age, 21.8 [3.45] years) were included in the study and successfully completed the study protocol. All interclass correlation coefficient (ICC) values indicated good or excellent reliability of the assessment of craniocervical ROM using a wearable inertial sensor. There was high variability between subjects on the amount of craniocervical ROM necessary to achieve each stage of the CCFT. Conclusions The use of inertial sensors is a reliable method to measure the craniocervical flexion ROM associated with the CCFT. The great variability in the ROM limits the possibility to standardize a set of targets of craniocervical flexion ROM equivalent to each of the pressure targets of the pressure biofeedback unit.

scholarly journals Tuning of the Wavelet Filters for the IMU Data Based on the PDC Method and the GPS Solution in a Bi-Dimensional Navigation Application

2019 ◽  
Vol 11 (3) ◽  
pp. 3-14
Author(s):  
Ioana Raluca ADOCHIEI ◽  
Teodor Lucian GRIGORIE ◽  
Felix Constantin ADOCHIEI
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Wavelet Filters ◽  
Time Frequency ◽  
Tuning Method ◽  
Coupling Dynamics ◽  
Navigation Accuracy ◽  
Sensor Errors

The degradation of navigation accuracy and integrity of GPS in the presence of radio frequency interference, hostile jamming and high dynamical situations, when the satellite signals may get lost due to signal blockage, led to the development of MEMS-INS/GPS integrated navigation systems for various applications of the positioning and navigation technologies. Unfortunately, the short-term advantages brought by the INS systems are overshadowed by their imprecise operation over the long term, mainly due to inertial sensor errors. A critical component of the inertial sensors errors is the noise. To improve the quality of the inertial sensors data, many denoising techniques have been used. Wavelet method has been proven as a useful tool for signal analysis, and it is widely used in signal processing and denoising applications. The here proposed technique is based on a time-frequency approach previously applied in bio-signals processing. In the proposed mechanism, the inertial sensors signals are processed analysed by using an extended version of the Wavelet transform. The optimal levels of decomposition are established for the wavelet filters, based on the evaluation of a parameter called coupling level (CL). It characterizes the coupling dynamics information between the reference signals, provided by a GPS, and the perturbed signal, which are the outputs of the inertial navigation system (INS). The proposed tuning method is experimentally tested in a bi-dimensional navigation application.

scholarly journals Towards ecologically valid biomarkers: real-life gait assessment in cerebellar ataxia

2019 ◽  
Author(s):  
Winfried Ilg ◽  
Jens Seemann ◽  
Martin Giese ◽  
Andreas Traschütz ◽  
Ludger Schöls ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Wearable Sensors ◽  
Real Life ◽  
Movement Analysis ◽  
High Sensitivity ◽  
Step Length ◽  
Gait Variability ◽  
Effect Sizes ◽  
Ataxic Gait ◽  
Disease Modifying Drugs

AbstractBACKGROUNDWith disease-modifying drugs on the horizon for degenerative ataxias, motor biomarkers are highly warranted. While ataxic gait and its treatment-induced improvements can be captured in laboratory-based assessments, quantitative markers of ataxic gait in real life will help to determine ecologically meaningful improvements.OBJECTIVESTo unravel and validate markers of ataxic gait in real life by using wearable sensors.METHODSWe assessed gait characteristics of 43 patients with degenerative cerebellar disease (SARA:9.4±3.9) compared to 35 controls by 3 body-worn inertial sensors in three conditions: (1) laboratory-based walking; (2) supervised free walking; (3) real-life walking during everyday living (subgroup n=21). Movement analysis focussed on measures of movement smoothness and spatio-temporal step variability.RESULTSA set of gait variability measures was identified which allowed to consistently identify ataxic gait changes in all three conditions. Lateral step deviation and a compound measure of step length categorized patients against controls in real life with a discrimination accuracy of 0.86. Both were highly correlated with clinical ataxia severity (effect size ρ=0.76). These measures allowed detecting group differences even for patients who differed only 1 point in the SARAp&g subscore, with highest effect sizes for real-life walking (d=0.67).CONCLUSIONSWe identified measures of ataxic gait that allowed not only to capture the gait variability inherent in ataxic gait in real life, but also demonstrate high sensitivity to small differences in disease severity - with highest effect sizes in real-life walking. They thus represent promising candidates for quantitative motor markers for natural history and treatment trials in ecologically valid contexts.

scholarly journals Automatic methods of hoof-on and -off detection in horses using wearable inertial sensors during walk and trot on asphalt, sand and grass

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254813
Author(s):  
Eloise V. Briggs ◽  
Claudia Mazzà
Keyword(s):  
Event Detection ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Wearable Sensors ◽  
Force Plate ◽  
Field Conditions ◽  
Future Studies ◽  
Gait Classification ◽  
Automatic Methods ◽  
Wearable Inertial Sensors

Detection of hoof-on and -off events are essential to gait classification in horses. Wearable sensors have been endorsed as a convenient alternative to the traditional force plate-based method. The aim of this study was to propose and validate inertial sensor-based methods of gait event detection, reviewing different sensor locations and their performance on different gaits and exercise surfaces. Eleven horses of various breeds and ages were recruited to wear inertial sensors attached to the hooves, pasterns and cannons. Gait events detected by pastern and cannon methods were compared to the reference, hoof-detected events. Walk and trot strides were recorded on asphalt, grass and sand. Pastern-based methods were found to be the most accurate and precise for detecting gait events, incurring mean errors of between 1 and 6ms, depending on the limb and gait, on asphalt. These methods incurred consistent errors when used to measure stance durations on all surfaces, with mean errors of 0.1 to 1.16% of a stride cycle. In conclusion, the methods developed and validated here will enable future studies to reliably detect equine gait events using inertial sensors, under a wide variety of field conditions.

scholarly journals IoT-Based Wearable Sensors and Bidirectional LSTM Network for Action Recognition of Aerobics Athletes

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jing Ye ◽  
Hui Wang ◽  
MeiJie Li ◽  
Ning Wang
Keyword(s):  
Short Term Memory ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Wearable Sensors ◽  
Motion Prediction ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
The Times ◽  
The Internet Of Things

Aerobics is the fusion of gymnastics, dance, and music; it is a body of a sports project, along with the development of the society. The growing demand for aerobics inevitably increases the demand for aerobics coach and teacher and has opened elective aerobics class which is an effective way of cultivating professional talents relevant to aerobics. Aerobics has extended fixed teaching mode and cannot conform to the development of the times. The motion prediction of aerobics athletes is a new set of teaching aid. In this paper, a motion prediction model of aerobics athletes is built based on the wearable inertial sensor of the Internet of Things and the bidirectional long short term memory (BiLSTM) network. Firstly, a wireless sensor network based on ZigBee was designed and implemented to collect the posture data of aerobics athletes. The inertial sensors were used for data collection and transmission of the data to the cloud platform through Ethernet. Then, the movement of aerobics athletes is recognized and predicted by the BiLSTM network. Based on the BiLSTM network and the attention mechanism, this paper proposes to solve the problem of low classification accuracy caused by the traditional method of directly summing and averaging the updated output vectors corresponding to each moment of the BiLSTM layer. The simulation experiment is also carried out in this paper. The experimental results show that the proposed model can recognize aerobics effectively.

Global Positioning System (GPS): A Low-Cost Velocity Sensor for Correcting Inertial Sensor Errors on Ground Vehicles

2004 ◽  
Vol 126 (2) ◽  
pp. 255-264 ◽  
Author(s):  
David M. Bevly
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Positioning System ◽  
Ground Vehicles ◽  
Velocity Sensor ◽  
Global Positioning ◽  
Sensor Errors

This paper demonstrates the ability of a standard low-cost Global Positioning System (GPS) receiver to reduce errors inherent in low-cost accelerometers and rate gyroscopes used on ground vehicles. Specifically GPS velocity is used to obtain vehicle course, velocity, and road grade, as well as to correct inertial sensors errors, providing accurate longitudinal and lateral acceleration, and pitch, roll, and yaw angular velocities. Additionally, it is shown that transient changes in sideslip (or lateral velocity), roll, and pitch angles can be measured. The method utilizes GPS velocity measurements to determine the inertial sensor errors using a kinematic Kalman Filter estimator. Simple models of the inertial sensors, which take into account the sensor noise and bias drift properties, are developed and used to design the estimator. Based on the characteristics of low-cost GPS receivers and IMU sensors, this paper presents the achievable performance of the combined system using the covariance analysis from the Kalman filter. Subsequent simulations and experiments validate both the error analysis and the methodology for utilizing GPS as a velocity sensor for correcting low-cost inertial sensor errors and providing critical vehicle state measurements.

Inertial Sensors are a Valid Tool to Detect and Consistently Quantify Jumping

2018 ◽  
Vol 39 (10) ◽  
pp. 802-808 ◽  
Author(s):  
Rhys Spangler ◽  
Timo Rantalainen ◽  
Paul Gastin ◽  
Daniel Wundersitz
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Team Sports ◽  
Movement Analysis ◽  
Detection Accuracy ◽  
Jump Height ◽  
Analysis Software ◽  
Jump Detection ◽  
Retest Reliability ◽  
Test Retest Reliability

AbstractConsidering the large and repetitive loads associated with jumping in team sports, automatic detection and quantification of jumping may show promise in reducing injury risks. The aim of this study was to validate commercially available inertial-movement analysis software to detect and quantify jumping in team sports. In addition, the test-retest reliability of the software to quantify jumping was assessed. Seventy-six healthy male participants completed a team sport circuit six times containing seven common movements (including three countermovement and two single-leg jumps) whilst wearing an inertial sensor (Catapult Sports, Australia). Jump detection accuracy was assessed by comparing the known number of jumps to the number recorded by the inertial movement analysis software. A further 27 participants separately performed countermovement and single-leg jumps at 33%, 66% and 100% of maximal jump height over two sessions. Jump height quantification accuracy was assessed by comparing criterion three-dimensional motion analysis-derived heights to that recorded by the inertial movement analysis software. Test-retest reliability was assessed by comparing recorded jump heights between both testing sessions. Catapult’s inertial movement analysis software displayed excellent jump detection accuracy (96.9%) and test-retest jump height quantification reliability (ICC: 0.86 [countermovement jump], 0.88 [single-leg jump]). However, significant mean bias (–2.74 cm [95% LoA –10.44 – 4.96]) was observed for jump height quantification. Overall, Catapult’s inertial movement analysis software appears to be a suitable method of automatically detecting jumping in team sports, and although reliable, caution is advised when using the IMA software to quantify jump height.

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