Development of a Platform to Assess the Risk of Musculoskeletal Disorders in Manual Load Handling Activities - Preliminary Results.

Risk identification on workstations is a crucial step to prevent the occurrence of musculoskeletal disorders (MSD) in workers. The available methods and tools used by ergonomists to assess and estimate the risk related to manual handling of loads under repetitive work cycles are usually biased by the inter-evaluator error that can lead to a subjective determination of work-related risks due to the application of, mainly, observational methods. This paper shows the preliminary results of a platform to assess the risk of musculoskeletal disorders during manual load-handling tasks using an instrumented system and using the National Institute for Occupational Safety & Health (NIOSH) method. Eight healthy subjects were measured during lifting activities using an optical-based and inertial-based motion capture systems. The developed software implements a semi-automated instrumented version of the NIOSH method, helping the evaluator with automated calculations of body segment locations, displacements and joint angles making it possible to obtain a objective risk classification. Also, we achieved a reduction of 85% in the time for the estimation of the necessary factors for the digital evaluation methodology, making the proposed platform a promising and attractive alternative for its application in real environments for risk assessments.Occupational health relevance- This work proposes an assistance tool for the detection of musculoskeletal disorders in activities related to manual handling of loads, essential to initiate modification strategies in the workplace, reduce the occurrence of occupational diseases and reduce the time of risk classification.

Natural Semantic Networks of the Neurorehabilitation Concept by Spanish Physiotherapists-A Qualitative Phenomenological Representational Study.

The Natural Semantic Networks (NSN) model is highly useful in analyzing the words that define a concept in terms of the value, strength, weight, or density that a specific population assigns to the construction of a learned concept. The main objective of this study was to describe the conceptualization of the concept of neurorehabilitation by Spanish physiotherapists specializing in this field using NSN. A phenomenological study is presented. The participants were physiotherapy professionals who graduated from three Spanish universities and were working in the field of neurorehabilitation. A questionnaire was administered via Google Forms, which was constructed using the NSN technique. A total of 191 physiotherapists participated in this study. The Spanish physiotherapists interviewed used a total of 1247 defining words for the concept of neurorehabilitation. The semantic core of the concept was mainly formed by the words 'treatment', 'recovery', 'functionality', 'neuroplasticity', and 'learning', which carried significant weight. Results were also presented taking into account the academic level and years of professional experience of the sample. The semantic network observed in this study allows us to elucidate the polysemy of the concept of neurorehabilitation, which is composed not only of certain associated words but also the meanings they imply.

Utilizing Motion Capture Systems for Instrumenting the OCRA Index: A Study on Risk Classification for Upper Limb Work-Related Activities.

In the search to enhance ergonomic risk assessments for upper limb work-related activities, this study introduced and validated the efficiency of an inertial motion capture system, paired with a specialized platform that digitalized the OCRA index. Conducted in a semi-controlled environment, the proposed methodology was compared to traditional risk classification techniques using both inertial and optical motion capture systems. The inertial method encompassed 18 units in a Bluetooth Low Energy tree topology network for activity recording, subsequently analyzed for risk using the platform. Principal outcomes emphasized the optical system's preeminence, aligning closely with the conventional technique. The optical system's superiority was further evident in its alignment with the traditional method. Meanwhile, the inertial system followed closely, with an error margin of just ±0.098 compared to the optical system. Risk classification was consistent across all systems. The inertial system demonstrated strong performance metrics, achieving F1-scores of 0.97 and 1 for "risk" and "no risk" classifications, respectively. Its distinct advantage of portability was reinforced by participants' feedback on its user-friendliness. The results highlight the inertial system's potential, mirroring the precision of both traditional and optical methods and achieving a 65% reduction in risk assessment time. This advancement mitigates the need for intricate video setups, emphasizing its potential in ergonomic assessments.

Instrumented Timed Up and Go Test (iTUG)-More Than Assessing Time to Predict Falls: A Systematic Review.

The Timed Up and Go (TUG) test is a widely used tool for assessing the risk of falls in older adults. However, to increase the test's predictive value, the instrumented Timed Up and Go (iTUG) test has been developed, incorporating different technological approaches. This systematic review aims to explore the evidence of the technological proposal for the segmentation and analysis of iTUG in elderlies with or without pathologies. A search was conducted in five major databases, following PRISMA guidelines. The review included 40 studies that met the eligibility criteria. The most used technology was inertial sensors (75% of the studies), with healthy elderlies (35%) and elderlies with Parkinson's disease (32.5%) being the most analyzed participants. In total, 97.5% of the studies applied automatic segmentation using rule-based algorithms. The iTUG test offers an economical and accessible alternative to increase the predictive value of TUG, identifying different variables, and can be used in clinical, community, and home settings.

Predicting risk of falls in elderly using a single Inertial Measurement Unit on the lower-back by estimating spatio-temporal gait parameters.

One of the consequences of aging is the increased risk of falls, especially when someone walks in unknown or uncontrolled environments. Usually, gait is evaluated through observation and clinical assessment scales to identify the state and deterioration of the patient's postural control. Lately, technological systems for bio-mechanical analysis have been used to determine abnormal gait states being expensive, difficult to use, and impossible to apply in real conditions. In this article, we explore the ability of a system based on a single inertial measurement unit located in the lower back to estimate spatio-temporal gait parameters by analyzing the signals available in the Physionet repository "Long Term Movement Monitoring Database" which, together with automatic classification algorithms, allow predicting the risk of falls in the elderly population. Different classification algorithms were trained and evaluated, being the Support Vector Machine classifier with a third-degree polynomial kernel, cost function C = 2 with the best performance, with an Accuracy = 59%, Recall = 91%, and F1- score = 71%, providing promising results regarding a proposal for the quantitative, online and realistic evaluation of gait during activities of daily living, which is where falls actually occur in the target population. Clinical Relevance - This work proposes an early risk of falls detection tool, essential to start preventive treatment strategies to maintain the independence of the elderly through a non-invasive, simple, and low-cost alternative.

Static Balance Characterization using a single IMU Located in the Lower Back: Preliminary Results.

Balance refers to the dynamics of body posture to prevent falls. For years, researchers have tried to find out which tasks and measures provide optimal detection of balance disorders, so that they can be quantified. This paper proposes the use of an accelerometer sensor located in the lower back to measure the center of mass accelerations and to characterize the subject's static balance. For characterizing the static balance objectively, we propose using normality circles, a centroid, and a dispersion circle during the modified Clinical Test of Sensory Interaction in Balance (mCTSIB) test. The proposed methodology was tested using two groups of subjects (10 healthy and 3 unhealthy). Our methodology for the static balance was compared to the Berg Balance Scale score. The results shown that a subject with lower BBS score obtain lower dispersion circle and is outside the normality circle. Also, our method outperforms a new option since it characterizes the static balance in an objective, portable, simple, and low-cost way. Clinical Relevance- Our proposed methodology to characterize the static balance can help to simplify objectify and reduce the cost of the clinical practice for balance evaluation.

Utrecht work engagement scale: Construct validity and reliability in Chilean health science students.

OBJECTIVE: The Utrecht Work Engagement Scale-Student (UWES-S) has been extensively applied in different countries to assess engagement in students, but there are discrepancies about its factor structure outcomes. To analyse the factor structure of Utrecht Work Engagement Scale-Student as construct validity evidence and reliability in medical students. METHODS: The non-experimental cross-sectional study was conducted at the public-sector University of Concepción in the Bio-Bio region in central Chile at the end of the first academic semester, between July and August 2017, and comprised students of health science undergraduate degree programmes in Medicine, Kinesiology, Pharmacy, and Speech and Language Therapy. Data was collected using a sociodemographic proforma and the Utrecht Work Engagement Scale-Student. The subjects were randomly divided into groups A and B. With group A, exploratory factor analysis was performed using the method of extraction of the main axis analysis. With group B, confirmatory factor analysis was performed using the maximum likelihood method. Data was analysed using STATA 11 SE. RESULTS: Of the 898 students, 538(59.91%) were females and 360(40.09%) were males. The overall mean age was 21.29±2.51 years (range: 17-34 years). Two factors, 'involvement with studies' and 'enthusiasm for the career', with eigenvalues 7.59 and 1.18 were identified with the potential to explain majority of the total variance of items. Comparative fit index, Tucker-Lewis index and the root mean square error of approximation showed the solution having the best fit. CONCLUSIONS: Involvement with studies and enthusiasm for career were found to be critical factors, but did not show adequate adjustment even though confirmatory factor analysis found this to be the solution with the best fit.

A Novel Capacitive Step Sensor to Trigger Stimulation on Functional Electrical Stimulators Devices for Drop Foot.

Drop foot is a typical clinical condition associated with stroke. According to the World Health Organization, fifteen million people suffer a stroke per year, and one of three people's survival gets drop foot. Functional Electrical Stimulation systems are applied over the peroneal motor nerve to achieve the drop foot problem's dorsiflexion. An accurate and reliable way to identify in real-time the gait phases to trigger and finish the stimulation is needed. This paper proposes a new step sensor with a custom capacitive pressure sensors array located under the heel to detect a gait pattern in real-time to synchronize the stimulation with the user gait. The step sensor uses a capacitive pressure sensors array and hardware, which acquire the signals, execute an algorithm to detect the start and finish of the swing phase in real-time, and send the synchronization signal wirelessly. The step sensor was tested in two ways: 10 meters walk test and walking in a treadmill for 2 minutes. These two tests were performed with two different walk velocities and with thirteen healthy volunteers. Thus, all the 1342 steps were correctly detected. Compared to an inertial sensor located in the lower-back, the proposed step sensor achieves a mean error of 27.60±0.03 [ms] for the detection of the start of the swing phase and a mean error of 20.86±0.02 [ms] for the detection of the end of the swing phase. The results show an improvement in time error (respect to others pressure step sensors), sensibility and specificity (both 100%), and comfortability.

Step capacitive array sensor to trigger stimulation on Functional Electrical Stimulators devices for Drop Foot: Preliminary results.

In this work, a new custom wireless capacitive step sensor and a real-time algorithm are proposed to detect the start and end of the swing phase of the gait to trigger the stimulation in Functional Electrical Stimulator devices (FES) for Drop Foot. For this, an array of capacitive pressure sensors was designed to detect patterns of the gait swing phase through the Heel Center of Pressure (HCOP). The proposed system recognized all the events with an average error of 20.86±0.02[ms] for the heel strike (initial contact) and 27.60±0.03[ms] for the heel-off (final contact) compared with lower-back accelerometer, constituting a viable, robust and promising alternative as a step sensor for functional electrical stimulators.

Simple Wireless Impedance Pneumography System for Unobtrusive Sensing of Respiration.

This extended paper presents the development and implementation at a prototype level of a wireless, low-cost system for the measurement of the electrical bioimpedance of the chest with two channels using the AD5933 in a bipolar electrode configuration to measure impedance pneumography. The measurement device works for impedance measurements ranging from 1 Ω to 1800 Ω. Fifteen volunteers were measured with the prototype. We found that the left hemithorax has higher impedance compared to the right hemithorax, and the acquired signal presents the phases of the respiratory cycle with variations between 1 Ω, in normal breathing, to 6 Ω in maximum inhalation events. The system can measure the respiratory cycle variations simultaneously in both hemithorax with a mean error of -0.18 ± 1.42 BPM (breaths per minute) in the right hemithorax and -0.52 ± 1.31 BPM for the left hemithorax, constituting a useful device for the breathing rate calculation and possible screening applications.

Validation of a portable system for spatial-temporal gait parameters based on a single inertial measurement unit and a mobile application.

There is a lack of commercially available low-cost technologies to assess gait clinically in non-controlled environments. As a consequence of this, there has been poor massification of motion measurement technologies that are both objective and reliable in nature. Advances about the study of gait and its interpretation in recent years using inertial sensors have allowed proposing acceptable alternatives for the development of portable and low-cost systems that contribute to people's health in places and institutions that cannot acquire or maintain the operation of commercially available systems. A system based on a custom single Inertial Measurement Unit and a mobile application is proposed. Thus, an investigation is carried out using methodologies and algorithms found in the literature in order to get the main gait events and the spatial-temporal gait parameters. Twenty healthy Chilean subjects were assessed using a motion capture system simultaneously with the proposed tool. The results show that it is possible to estimate temporal gait parameters with slight differences respect gold--standard. We reach maximum mean differences of -2.35±5.02[step/min] for cadence, 0.03±0.04[sec] for stride time,0.02±0.03[sec] for step time, ±0.02[sec] for a single support time, 0.01±0.02[sec] for double support time and 0.01±0.03[m] for step length. As a result of experimental findings, we propose a new technological tool that can perform gait analysis. Our proposed system is user-friendly, low-cost, and portable. Therefore, we suggest that it could be an attractive technological tool that healthcare professionals could harness to objectively measure gait in environments that are either within the community or controlled. We also suggest that the tool could be used in countries where advanced clinical tools cannot be acquired. Therefore, we propose in this paper that our system is an attractive, alternative system that can be used for gait analysis by health professionals worldwide.

Construct validity and test-retest reliability of a free mobile application for spatio-temporal gait analysis in Parkinson's disease patients.

BACKGROUND: Mobile applications may be used to assess gait pattern deviation through mobile smartphones in people with Parkinson's disease (PD). However, few studies have investigated their psychometrics properties. RESEARCH QUESTION: To study the construct validity and test-retest reliability of the RUNZI® free mobile application in people with mild to moderate PD. METHODS: Thirty individuals were evaluated with the RUNZI® app and with the 10-meter walking test (10 MW), simultaneously. In addition, the Timed Up & Go test (TUG), Tinetti scale, and the Berg Balance Scale (BBS) were used to study the construct validity. Also, test-retest reliability of the mobile for spatio-temporal gait parameters was explored. RESULTS: The correlation indices of the 10 MW test with the RUNZI® app at fast speeds was moderate to excellent (r = .588-.957). At a comfortable speed, the correlation was excellent for walking speed (r = 0.944), moderate for steps (r = 0.780) and stride length (r = 0.760), and poor for cadence (r = .424). Results showed significant correlations between TUG and spatio-temporal gait parameters at fast and comfortable speeds. There were no significant correlations or consistent associations between Tinetti and BBS and RUNZI®. The test-retest reliability was good to excellent for parameters measured with the RUNZI®. SIGNIFICANCE: Our findings highlight specific opportunities for a free smartphone-based spatio-temporal gait analysis to serve as a complement to conventional gait analysis methods in clinical practice with a moderate to excellent construct validity with the 10 MW test and good to excellent test-retest reliability in PD patients.

Use of a Single Wireless IMU for the Segmentation and Automatic Analysis of Activities Performed in the 3-m Timed Up & Go Test.

Falls represent a major public health problem in the elderly population. The Timed Up & Go test (TU & Go) is the most used tool to measure this risk of falling, which offers a unique parameter in seconds that represents the dynamic balance. However, it is not determined in which activity the subject presents greater difficulties. For this, a feature-based segmentation method using a single wireless Inertial Measurement Unit (IMU) is proposed in order to analyze data of the inertial sensors to provide a complete report on risks of falls. Twenty-five young subjects and 12 older adults were measured to validate the method proposed with an IMU in the back and with video recording. The measurement system showed similar data compared to the conventional test video recorded, with a Pearson correlation coefficient of 0.9884 and a mean error of 0.17 ± 0.13 s for young subjects, as well as a correlation coefficient of 0.9878 and a mean error of 0.2 ± 0.22 s for older adults. Our methodology allows for identifying all the TU & Go sub­tasks with a single IMU automatically providing information about variables such as: duration of sub⁻tasks, standing and sitting accelerations, rotation velocity of turning, number of steps during walking and turns, and the inclination degrees of the trunk during standing and sitting.