Use Of Wearable Inertial Sensors Research Articles

The Potential Role of Wearable Inertial Sensors in Laboring Women with Walking Epidural Analgesia.

There is a growing interest in wearable inertial sensors to monitor and analyze the movements of pregnant women. The noninvasive and discrete nature of these sensors, integrated into devices accumulating large datasets, offers a unique opportunity to study the dynamic changes in movement patterns during the rapid physical transformations induced by pregnancy. However, the final cut of the third trimester of pregnancy, particularly the first stage of labor up to delivery, remains underexplored. The growing popularity of "walking epidural", a neuraxial analgesia method allowing motor function preservation, ambulation, and free movement throughout labor and during delivery, opens new opportunities to study the biomechanics of labor using inertial sensors. Critical research gaps exist in parturient fall prediction and detection during walking epidural and understanding pain dynamics during labor, particularly in the presence of pelvic girdle pain. The analysis of fetal descent, upright positions, and their relationship with dynamic pelvic movements facilitated by walking during labor is another area where inertial sensors can play an interesting role. Moreover, as contemporary obstetrics advocate for less restricted or non-restricted movements during labor, the role of inertial sensors in objectively measuring the quantity and quality of women's movements becomes increasingly important. This includes studying the impact of epidural analgesia on maternal mobility, walking patterns, and associated obstetrical outcomes. In this paper, the potential use of wearable inertial sensors for gait analysis in the first stage of labor is discussed.

Estimation of Spatio-Temporal Parameters of Gait and Posture of Visually Impaired People Using Wearable Sensors

In rehabilitating orientation and mobility (O&M) for visually impaired people (VIP), the measurement of spatio-temporal gait and postural parameters is of specific interest for rehabilitators to assess performance and improvements in independent mobility. In the current practice of rehabilitation worldwide, this assessment is carried out in people with estimates made visually. The objective of this research was to propose a simple architecture based on the use of wearable inertial sensors for quantitative estimation of distance traveled, step detection, gait velocity, step length and postural stability. These parameters were calculated using absolute orientation angles. Two different sensing architectures were tested for gait according to a selected biomechanical model. The validation tests included five different walking tasks. There were nine visually impaired volunteers in real-time acquisitions, where the volunteers walked indoor and outdoor distances at different gait velocities in their residences. The ground truth gait characteristics of the volunteers in five walking tasks and an assessment of the natural posture during the walking tasks are also presented in this article. One of the proposed methods was selected for presenting the lowest absolute error of the calculated parameters in all of the traveling experimentations: 45 walking tasks between 7 and 45 m representing a total of 1039 m walked and 2068 steps; the step length measurement was 4.6 ± 6.7 cm with a mean of 56 cm (11.59 Std) and 1.5 ± 1.6 relative error in step count, which compromised the distance traveled and gait velocity measurements, presenting an absolute error of 1.78 ± 1.80 m and 7.1 ± 7.2 cm/s, respectively. The results suggest that the proposed method and its architecture could be used as a tool for assistive technology designed for O&M training to assess gait parameters and/or navigation, and that a sensor placed in the dorsal area is sufficient to detect noticeable postural changes that compromise heading, inclinations and balancing in walking tasks.

The Use of Wearable Inertial Sensors and Workplace-Based Exercises to Reduce Lateral Epicondylitis in the Workstation of a Textile Logistics Center.

People whose jobs involve repetitive motions of the wrist and forearm can suffer from lateral epicondylitis, which is a significant burden on both the individual and the employer due to treatment costs, reduced productivity, and work absenteeism. This paper describes an ergonomic intervention to reduce lateral epicondylitis in the workstation of a textile logistics center. The intervention includes workplace-based exercise programs, evaluation of risk factors, and movement correction. An injury- and subject-specific score was calculated from the motion captured with wearable inertial sensors at the workplace to evaluate the risk factors of 93 workers. Then, a new working movement was adapted to the workplace, which limited the observed risk factors and took into account the subject-specific physical abilities. The movement was taught to the workers during personalized sessions. The risk factors of 27 workers were evaluated again after the intervention to validate the effectiveness of the movement correction. In addition, active warm-up and stretching programs were introduced as part of the workday to promote muscle endurance and improve resistance to repetitive stress. The present strategy offered good results at low cost, without any physical modification of the workplace and without any detriment to productivity.

Automatic Recognition of Construction Worker Activities Using Deep Learning Approaches and Wearable Inertial Sensors

The automated evaluation and analysis of employee behavior in an Industry 4.0-compliant manufacturing firm are vital for the rapid and accurate diagnosis of work performance, particularly during the training of a new worker. Various techniques for identifying and detecting worker performance in industrial applications are based on computer vision techniques. Despite widespread computer vision-based approaches, it is challenging to develop technologies that assist the automated monitoring of worker actions at external working sites where camera deployment is problematic. Through the use of wearable inertial sensors, we propose a deep learning method for automatically recognizing the activities of construction workers. The suggested method incorporates a convolutional neural network, residual connection blocks, and multi-branch aggregate transformation modules for high-performance recognition of complicated activities such as construction worker tasks. The proposed approach has been evaluated using standard performance measures, such as precision, F1-score, and AUC, using a publicly available benchmark dataset known as VTT-ConIoT, which contains genuine construction work activities. In addition, standard deep learning models (CNNs, RNNs, and hybrid models) were developed in different empirical circumstances to compare them to the proposed model. With an average accuracy of 99.71% and an average F1-score of 99.71%, the experimental findings revealed that the suggested model could accurately recognize the actions of construction workers. Furthermore, we examined the impact of window size and sensor position on the identification efficiency of the proposed method.

Accuracy and comparison of sensor-based gait speed estimations under standardized and daily life conditions in children undergoing rehabilitation

BackgroundGait speed is a widely used outcome measure to assess the walking abilities of children undergoing rehabilitation. It is routinely determined during a walking test under standardized conditions, but it remains unclear whether these outcomes reflect the children's performance in daily life. An ankle-worn inertial sensor provides a usable opportunity to measure gait speed in the children's habitual environment. However, sensor-based gait speed estimations need to be accurate to allow for comparison of the children's gait speed between a test situation and daily life. Hence, the first aim of this study was to determine the measurement error of a novel algorithm that estimates gait speed based on data of a single ankle-worn inertial sensor in children undergoing rehabilitation. The second aim of this study was to compare the children’s gait speed between standardized and daily life conditions.MethodsTwenty-four children with walking impairments completed four walking tests at different speeds (standardized condition) and were monitored for one hour during leisure or school time (daily life condition). We determined accuracy by comparing sensor-based gait speed estimations with a reference method in both conditions. Eventually, we compared individual gait speeds between the two conditions.ResultsThe measurement error was 0.01 ± 0.07 m/s under the standardized and 0.04 ± 0.06 m/s under the daily life condition. Besides, the majority of children did not use the same speed during the test situation as in daily life.ConclusionThis study demonstrates an accurate method to measure children's gait speed during standardized walking tests and in the children's habitual environment after rehabilitation. It only requires a single ankle sensor, which potentially increases wearing time and data quality of measurements in daily life. We recommend placing the sensor on the less affected side, unless the child wears one orthosis. In this latter case, the sensor should be placed on the side with the orthosis. Moreover, this study showed that most children did not use the same speed in the two conditions, which encourages the use of wearable inertial sensors to assess the children's walking performance in their habitual environment following rehabilitation.

Badminton stroke identification using wireless inertial sensor and neural network

Objective evaluation is essential in sports to monitor athlete performance, provide relevant and timely feedback, and minimize the risk of injury. Activity recognition is the first step in sport skill and technique performance analysis. This study investigated the use of wearable inertial sensors and a neural network (NN) to identify badminton strokes. The study also explored the effect of different NN configurations and a different number of sensors on the classification. Sensors were placed at the dominant wrist, left ankle, and right ankle. Six different strokes, ranging from soft hitting net shots to smashes, were performed with a total of 3300 repetitions from six well-trained badminton players. An automated window segmentation method was developed to identify the stroke instances. A scaled conjugate gradient training algorithm with two hidden layers and 55 neurons in each layer was found to be the best approach to classify badminton strokes with an accuracy of 97.69%. Even just wearing the inertial sensor on the wrist was sufficient, providing an accuracy of 95.09%. These results demonstrate the viability of using inertial sensors and NN to recognize badminton strokes, which can be applied in training and competitive environments.

Wearable Sensors for Real-Time Kinematics Analysis in Sports: A Review

Wearable Inertial sensors have revolutionised the way kinematics analysis is performed in sports. This paper aims to present a comprehensive review of the literature related to the use of wearable inertial sensors for performance analysis in various games. Kinematics analysis using wearable sensors can provide real-time feedback to the players about their adopted techniques in their respective sports and thus help them to perform efficiently. This article reviews the key technologies (IMU sensors, communication technology, data fusion and data analysis techniques) that enable the implementation of wearable sensors for performance analysis in sports. The review focuses on research papers, commercial sports sensors and 3D motion tracking products to provide a holistic and systematic categorisation & analysis of the wearable sensors in sports. The review identifies the importance of sensors classification, applications and performance parameters in sports for structured analysis. The survey also reviews the technology concerning sensor architecture, network and communication protocols, covers various data fusion algorithms and their accuracy while throwing light on essential performance matrices for an athlete. This review paper will assist both end-users and the researchers to have a comprehensive glimpse of the wearable technology pertaining to designing sensors and solutions for athletes in different sports.

The use of wearable inertial sensors effectively quantify arm asymmetry during gait in children with unilateral spastic cerebral palsy

This is a retrospective pilot study of 14 individuals (8 men, 6 women) with unilateral spastic cerebral palsy (USCP), aged 3 to 28 years, and with a mean age = 13 ± 7.8. The inclusion criteria included USCP individuals who ambulate without an assistive device. The exclusion criteria included individuals who are nonambulatory, ambulate with a device, or are unable to follow instructions.

Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review.

Wearable sensors are becoming increasingly popular for complementing classical clinical assessments of gait deficits. The aim of this review is to examine the existing knowledge by systematically reviewing a large number of papers focusing on the use of wearable inertial sensors for the assessment of gait during the 6-minute walk test (6MWT), a widely recognized, simple, non-invasive, low-cost and reproducible exercise test. After a systematic search on PubMed and Scopus databases, two raters evaluated the quality of 28 full-text articles. Then, the available knowledge was summarized regarding study design, subjects enrolled (number of patients and pathological condition, if any, age, male/female ratio), sensor characteristics (type, number, sampling frequency, range) and body placement, 6MWT protocol and extracted parameters. Results were critically discussed to suggest future directions for the use of inertial sensor devices in the clinics.

Use of wearable inertial sensors for the assessment of spatiotemporal gait variables in children: A systematic review

Abstract Aim: The present study aimed to perform a literature review on the use of wearable inertial sensors for gait analysis of children in clinical practice. Methods: Searches were performed in the MEDLINE, EMBASE, Cochrane Library, and PEDro databases for studies involving children or adolescents submitted to gait analysis with the use of wearable inertial sensors. No restrictions were imposed regarding the date of publication or language. Results: Three hundred twenty articles were retrieved, 14 of which met the eligibility criteria and were selected for the present systematic review. Two independent reviewers assessed the risk of bias and study quality using the ROBINS-I and AXIS scale. The studies included in the present review reported multiple outcomes of kinematic gait assessments calculated from the signals provided by the wearable sensors, performed in a hospital setting, outpatient clinic, and a familiar environment, with several types of pediatric conditions. Conclusion: The findings suggest that wearable sensors are effective for the evaluation of quantitative gait variables in children with different pediatric conditions, enabling an objective analysis that should prove useful in the processes of clinical diagnosis and rehabilitation. However, given the relatively small number of studies published on this topic, it is difficult to make strong recommendations regarding the most appropriate equipment, sensor placement, and outcomes for assessing gait in children.

Dynamic balance assessment during gait in children with Down and Prader-Willi syndromes using inertial sensors

Down (DS) and Prader-Willi (PWS) syndromes are chromosomal disorders both characterized by obesity, ligament laxity, and hypotonia, the latter associated with gait instability. Although these shared features may justify a common rehabilitation approach, evidence exists that adults with DS and PWS adopt different postural and walking strategies. The development of an instrumented protocol able to describe these strategies and quantify patients’ gait stability in the current clinical routine would be of great benefit for health professionals, allowing them to design personalized rehabilitation programs. This is particularly true for children with DS and PWS, where motor development is dramatically constrained by severe hypotonia and muscle weakness. The aim of this study was, thus, to propose an instrumented protocol, integrated with the clinical routine and based on the use of wearable inertial sensors, to assess gait stability in DS and PWS children.Fifteen children with DS, 11 children with PWS, and 12 typically developing children (CG) were involved in the study. Participants performed a 10-meter walking test while wearing four inertial sensors located at pelvis, sternum, and both distal tibiae levels. Spatiotemporal parameters (walking speed, stride frequency, and stride length) and a set of indices related to gait symmetry and upper-body stability (Root Mean Square, Attenuation Coefficient and Improved Harmonic Ratio) were estimated from pelvis and sternum accelerations. The Gross Motor Functional Measures (GMFM-88) and Intelligence Quotient (IQ Wechsler) were also assessed for each patient. A correlation analysis among the GMFM-88 and IQ scales and the estimated parameters was then performed.Children with DS and PWS exhibit reduced gait symmetry and higher accelerations at pelvis level than CG. While these accelerations are attenuated by about 40% at sternum level in CG and DS, PWS children display significant smaller attenuations, thus reporting reduced gait stability, most likely due to their typical “Trendelenburg gait”. Significant correlations were found between the estimated parameters and the GMFM-88 scale when considering the whole PWS and DS group and the PWS group alone.These results promote the adoption of wearable technology in clinical routines to monitor gait patterns in children with DS and PWS: the proposed protocol allows to markedly characterize patient-specific motor limitations even when clinical assessment scores provide similar results in terms of pathology severity. This protocol could be adopted to support health professionals in designing personalized treatments that, in turn, could help improving patients’ quality of life in terms of both physical and social perspectives.

Quantification of Axial Abnormality Due to Cerebellar Ataxia with Inertial Measurements.

Cerebellar Ataxia (CA) leads to deficiencies in muscle movement and lack of coordination that is often manifested as gait and balance disabilities. Conventional CA clinical assessments are subjective, cumbersome and provide less insight into the functional capabilities of patients. This cross-sectional study investigates the use of wearable inertial sensors strategically positioned on the front-chest and upper-back locations during the Romberg and Trunk tests for objective assessment of human postural balance due to CA. The primary aim of this paper is to quantify the performance of postural stability of 34 patients diagnosed with CA and 22 healthy subjects as controls. Several forms of entropy descriptions were considered to uncover characteristics of movements intrinsic to CA. Indeed, correlation with clinical observation is vital in ascertaining the validity of the inertial measurements in addition to capturing unique features of movements not typically observed by the practicing clinician. Both of these aspects form an integral part of the underlying objective assessment scheme. Uncertainty in the velocity contained a significant level of information with respect to truncal instability and, based on an extensive clustering and discrimination analysis, fuzzy entropy was identified as an effective measure in characterising the underlying disability. Front-chest measurements demonstrated a strong correlation with clinical assessments while the upper-back measurements performed better in classifying the two cohorts, inferring that the standard clinical assessments are relatively influenced by the frontal observations. The Romberg test was confirmed to be an effective test of neurological diagnosis as well as a potential candidate for objective assessment resulting in a significant correlation with the clinical assessments. In contrast, the Trunk test is observed to be relatively less informative.

Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review

Recent technological developments have led to the production of inexpensive, non-invasive, miniature magneto-inertial sensors, ideal for obtaining sport performance measures during training or competition. This systematic review evaluates current evidence and the future potential of their use in sport performance evaluation. Articles published in English (April 2017) were searched in Web-of-Science, Scopus, Pubmed, and Sport-Discus databases. A keyword search of titles, abstracts and keywords which included studies using accelerometers, gyroscopes and/or magnetometers to analyse sport motor-tasks performed by athletes (excluding risk of injury, physical activity, and energy expenditure) resulted in 2040 papers. Papers and reference list screening led to the selection of 286 studies and 23 reviews. Information on sport, motor-tasks, participants, device characteristics, sensor position and fixing, experimental setting and performance indicators was extracted. The selected papers dealt with motor capacity assessment (51 papers), technique analysis (163), activity classification (19), and physical demands assessment (61). Focus was placed mainly on elite and sub-elite athletes (59%) performing their sport in-field during training (62%) and competition (7%). Measuring movement outdoors created opportunities in winter sports (8%), water sports (16%), team sports (25%), and other outdoor activities (27%). Indications on the reliability of sensor-based performance indicators are provided, together with critical considerations and future trends.

Data Collection and Analysis Using Wearable Sensors for Monitoring Knee Range of Motion after Total Knee Arthroplasty.

Total knee arthroplasty (TKA) is the most common treatment for degenerative osteoarthritis of that articulation. However, either in rehabilitation clinics or in hospital wards, the knee range of motion (ROM) can currently only be assessed using a goniometer. In order to provide continuous and objective measurements of knee ROM, we propose the use of wearable inertial sensors to record the knee ROM during the recovery progress. Digitalized and objective data can assist the surgeons to control the recovery status and flexibly adjust rehabilitation programs during the early acute inpatient stage. The more knee flexion ROM regained during the early inpatient period, the better the long-term knee recovery will be and the sooner early discharge can be achieved. The results of this work show that the proposed wearable sensor approach can provide an alternative for continuous monitoring and objective assessment of knee ROM recovery progress for TKA patients compared to the traditional goniometer measurements.

Fall-Risk Classification of the Timed Up-And-Go Test with Principle Component Analysis

This study aimed to show that important fall risk measures among the elderly can be classified using multiple parameters obtained from wearable inertial sensors. The timed up-and-go (TUG) test, a well-known standard assessment test, was used to evaluate the risk of falling among elderly individuals. The use of wearable inertial sensors enables extraction of triaxial acceleration and angular velocity signals for offline analysis. Thirty-eight elderly patients from Fujimoto Hayasuzu Hospital participated in this study. Specific results were provided from the signals obtained from acceleration and angular velocity, and analysis was carried out in each phase of various activities, such as sit-to-stand, walking, etc. Seventy-eight parameters were obtained from the extracted acceleration and angular velocity signals in all phases to classify the risk of falling among the elderly. Using principle component analysis, the most important measures were selected from the gathered parameters. The most influential measure in differentiating subjects with high and low fall risks was the turning angular velocity signal.

Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients

BackgroundFall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data.MethodsIn a first study phase, 119 inpatients of a geriatric clinic took part in motion measurements using a wireless triaxial accelerometer during a Timed Up&Go (TUG) test and a 20 m walk. Furthermore, the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) was performed, and the multidisciplinary geriatric care team estimated the patients' fall risk. In a second follow-up phase of the study, 46 of the participants were interviewed after one year, including a fall and activity assessment. The predictive performances of the TUG, the STRATIFY and team scores are compared. Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched.ResultsAmong the risk assessment scores, the geriatric team score (sensitivity 56%, specificity 80%) outperforms STRATIFY and TUG. The induced logistic regression models CONV and SENSOR achieve similar performance values (sensitivity 68%/58%, specificity 74%/78%, AUC 0.74/0.72, +LR 2.64/2.61). Both models are able to identify more persons at risk than the simple scores.ConclusionsSensor-based objective measurements of motion parameters in geriatric patients can be used to assess individual fall risk, and our prediction model's performance matches that of a model based on conventional clinical and assessment data. Sensor-based measurements using a small wearable device may contribute significant information to conventional methods and are feasible in an unsupervised setting. More prospective research is needed to assess the cost-benefit relation of our approach.