Validity of the Intelligent Device for Energy Expenditure and Activity Accelerometry System for Quantitative Gait Analysis in Patients With Hip Osteoarthritis

Comparability between wearable inertial sensors and an electronic walkway for spatiotemporal and relative phase data in young children aged 6-11 years

BACKGROUND

Approaches to gait analysis are evolving rapidly and now include a wide range of options: from e-patches to video platforms to wearable inertial measurement unit systems. Newer options for gait analysis are generally more inclusive for the assessment of children, more cost effective and easier to administer. However, there is limited data on the comparability of newer systems with more established traditional approaches in young children.

RESEARCH QUESTION

To determine comparability between the Physilog®5 wearable inertial sensor and GAITRite® electronic walkway for spatiotemporal (stride length, time and velocity, cadence) and relative phase (double support time, stance, swing, loading, foot flat and push off) data in young children.

METHODS

A total 34 typically developing participants (41% female) aged 6-11 years old median age 8.99 years old (interquartile range 2.83) were assessed walking at self-selected speed over the GAITRite® electronic walkway while concurrently wearing shoe-attached Physilog®5 IMU sensors. Level of agreement was analysed by Lin's concordance correlation coefficient (CCC), Bland-Altman plots and 95% limit of agreement. Systematic bias was assessed using 95% confidence interval of the mean difference.

RESULTS

Excellent to almost perfect agreement was observed between systems for spatiotemporal metrics: cadence (CCC=0.996), stride length (CCC=0.993), stride time (CCC=0.996), stride velocity (CCC=0.988). The relative phase metrics adjusted for stride velocity showed improved comparability when compared to the unadjusted metrics: swing adjusted (adj) (CCC=0.635); stance adj (CCC: 0.879); loading adj: (CCC=0.626).

SIGNIFICANCE

Spatiotemporal metrics are highly compatible across GAITRite® electronic walkway and Physilog®5 IMU systems in young children. Relative phase metrics were somewhat compatible between systems when adjusted for stride velocity.

Advances in the application of wearable sensors for gait analysis after total knee arthroplasty: a systematic review

BACKGROUND

Wearable sensors have become a complementary means for evaluation of body function and gait in lower limb osteoarthritis. This study aimed to review the applications of wearable sensors for gait analysis after total knee arthroplasty (TKA).

METHODS

Five databases, including Web of Science Core Collection, Embase, Cochrane, Medline, and PubMed, were searched for articles published between January 2010 and March 2023, using predetermined search terms that focused on wearable sensors, TKA, and gait analysis as broad areas of interest.

RESULTS

A total of 25 articles were identified, involving 823 TKA patients. Methodologies varied widely across the articles, with inconsistencies found in reported patient characteristics, sensor data and experimental protocols. Patient-reported outcome measures (PROMs) and gait variables showed various recovery times from 1 week postoperatively to 5 years postoperatively. Gait analysis using wearable sensors and PROMs showed differences in controlled environments, daily life, and when comparing different surgeries.

CONCLUSION

Wearable sensors offered the potential to remotely monitor the gait function post-TKA in both controlled environments and patients' daily life, and covered more aspects than PROMs. More cohort longitudinal studies are warranted to further confirm the benefits of this remote technology in clinical practice.

Validity study of a triaxial accelerometer for measuring energy expenditure in stroke inpatients of a physical medicine and rehabilitation center

PURPOSE

Establish the validity of a triaxial accelerometer (Dynaport®) for evaluating the energy expenditure of patients with stroke sequelae at a rehabilitation hospital.

METHODS

This is a cross-sectional study with 24 stroke inpatients of a rehabilitation hospital. The participants were assessed on energy expenditure by an ergospirometer system and the triaxial accelerometer simultaneously during a walk test. The data collected by both devices were compared by intraclass correlation coefficient (ICC) and Bland-Altman limits of agreement.

RESULTS

An almost perfect agreement (ICC = 0,94) in the energy expenditure measured by the accelerometer compared to the results of the ergospirometer system was found during the exercise test. The Bland-Altman analysis has shown suitable limits of agreement. Post hoc analyses with the maximum volume of oxygen and the total energy expenditure measured by the ergospirometer system evidenced significant correlation with the energy expenditure measurements by the accelerometer.

CONCLUSION

Our results evidence that the triaxial accelerometer Dynaport® and its built-in software are valid for estimating the energy expenditure of stroke sequelae during a walk exercise.

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review

Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.

Validity of load rate estimation using accelerometers during physical activity on an anti-gravity treadmill

Introduction

A simple tool to estimate loading on the lower limb joints outside a laboratory may be useful for people who suffer from degenerative joint disease. Here, the accelerometers on board of wearables (smartwatch, smartphone) were used to estimate the load rate on the lower limbs and were compared to data from a treadmill force plate. The aim was to assess the validity of wearables to estimate load rate transmitted through the joints.

Methods

Twelve healthy participants (female n = 4, male n = 8; aged 26 ± 3 years; height: 175 ± 15 cm; body mass: 71 ± 9 kg) carried wearables, while performing locomotive activities on an anti-gravity treadmill with an integrated force plate. Acceleration data from the wearables and force plate data were used to estimate the load rate. The treadmill enabled 7680 data points to be obtained, allowing a good estimate of uncertainty to be examined. A linear regression model and cross-validation with 1000 bootstrap resamples were used to assess the validation.

Results

Significant correlation was found between load rate from the force plate and wearables (smartphone: R 2 = 0.71 ; smartwatch: R 2 = 0.67 ).

Conclusion

Wearables' accelerometers can estimate load rate, and the good correlation with force plate data supports their use as a surrogate when assessing lower limb joint loading in field environments.

Wearable Inertial Sensors for Gait Analysis in Adults with Osteoarthritis-A Scoping Review

Our objective was to conduct a scoping review which summarizes the growing body of literature using wearable inertial sensors for gait analysis in lower limb osteoarthritis. We searched six databases using predetermined search terms which highlighted the broad areas of inertial sensors, gait, and osteoarthritis. Two authors independently conducted title and abstract reviews, followed by two authors independently completing full-text screenings. Study quality was also assessed by two independent raters and data were extracted by one reviewer in areas such as study design, osteoarthritis sample, protocols, and inertial sensor outcomes. A total of 72 articles were included, which studied the gait of 2159 adults with osteoarthritis (OA) using inertial sensors. The most common location of OA studied was the knee (n = 46), followed by the hip (n = 22), and the ankle (n = 7). The back (n = 41) and the shank (n = 40) were the most common placements for inertial sensors. The three most prevalent biomechanical outcomes studied were: mean spatiotemporal parameters (n = 45), segment or joint angles (n = 33), and linear acceleration magnitudes (n = 22). Our findings demonstrate exceptional growth in this field in the last 5 years. Nevertheless, there remains a need for more longitudinal study designs, patient-specific models, free-living assessments, and a push for "Code Reuse" to maximize the unique capabilities of these devices and ultimately improve how we diagnose and treat this debilitating disease.

Role of the intelligent exercise rehabilitation management system on adherence of cardiac rehabilitation in patients with coronary heart disease: a randomised controlled crossover study protocol

INTRODUCTION

The benefits of cardiac rehabilitation (CR) on the reduction of cardiac and all-cause mortality are well documented. However, adherence remains suboptimal in China. It is clear that traditional CR does not meet the needs of many eligible patients and innovation is required to improve its application. Home-based CR (HBCR) is a cost-effective method that may be a valuable alternative for many individuals in China. In HBCR, it is often difficult to maintain an exercise intensity that is both effective and within safe limits, factors that are essential for patient safety. Mobile health interventions have the potential to overcome these obstacles and may be efficacious in improving adherence. The purpose of this study is to evaluate whether an Intelligent Exercise Rehabilitation Management System (IERMS)-based HBCR could improve adherence to CR and to assess the effects on exercise capacity, mental health, self-efficacy, quality of life and lifestyle-related risk factors.

METHODS AND ANALYSIS

We propose a single-blinded, two-arm, randomised controlled crossover study of 70 patients with coronary heart disease (CHD). Participants will be randomly assigned in a 1:1 ratio to one of the two groups. Patients in group 1 will receive the IERMS intervention together with usual care for the first 6 weeks and usual care for the last 6 weeks, while patients assigned to group 2 will receive usual care for the first 6 weeks and will use IERMS in the last 6 weeks. The primary outcome is adherence to the programme and secondary outcomes include exercise capacity, psychological well-being, quality of life, self-efficacy and lifestyle-related risk factors. All secondary outcomes will be measured at baseline, 6 weeks and 12 weeks.

ETHICS AND DISSEMINATION

This study has been approved by the Human Research Ethics Committee of the School of Nursing, Jilin University (HREC 2019120901). The results will be published in peer-reviewed journals and at conferences.

TRIAL REGISTRATION NUMBER

ChiCTR1900028182; Pre-results.

Inertial sensors versus standard systems in gait analysis: a systematic review and meta-analysis

INTRODUCTION

The increasing popularity of inertial sensors in clinical practice is not supported by precise information on their reliability or guidelines for their use in rehabilitation. The authors investigated the state of the literature concerning the use of inertial sensors for gait analysis in both healthy and pathological adults comparing traditional systems. Furthermore, trying to define directions for clinicians.

EVIDENCE ACQUISITION

In accordance with the PRISMA statement, authors searched in PubMed, Web of Science and Scopus all paper published from January 1st, 2005 until December 31st, 2017. They included both healthy and pathological adults' subjects as population, wearable or inertial sensors used for gait analysis and compared with classical gait analysis performed in a Motion Lab as intervention and comparison, gait parameters as outcomes. Considering the methodological quality, authors focused on: sample; description of the study; type of gait analysis used for comparison; type of sensor; sensor placement on the body; gait task requested.

EVIDENCE SYNTHESIS

From a total of 888 articles, 16 manuscripts were selected and 7 of them were considered for meta-analysis for different gait parameters. Demographic data, tested devices, reference systems, test procedures and outcomes were analyzed.

CONCLUSIONS

Our results show a good agreement between inertial sensors and classical gait analysis for some gait parameters, supporting their use as a solution for capturing kinematic information over an extended space and time and even outside a laboratory in real-life conditions. Authors can support the use of portable inertial sensors for a practical gait analysis in clinical setting with good reliability. It will then be the experience of the clinician to direct the decision-making process.

Clinical Gait Evaluation of Patients with Lumbar Spine Stenosis

OBJECTIVE

The third generation Intelligent Device for Energy Expenditure and Activity (IDEEA3, MiniSun, CA) has been developed for clinical gait evaluation, and this study was designed to evaluate the accuracy and reliability of IDEEA3 for the gait measurement of lumbar spinal stenosis (LSS) patients.

METHODS

Twelve healthy volunteers were recruited to compare gait cycle, cadence, step length, velocity, and number of steps between a motion analysis system and a high-speed video camera. Twenty hospitalized LSS patients were recruited for the comparison of the five parameters between the IDEEA3 and GoPro camera. Paired t-test, intraclass correlation coefficient, concordance correlation coefficient, and Bland-Altman plots were used for the data analysis.

RESULTS

The ratios of GoPro camera results to motion analysis system results, and the ratios of IDEEA3 results to GoPro camera results were all around 1.00. All P-values of paired t-tests for gait cycle, cadence, step length, and velocity were greater than 0.05, while all the ICC and CCC results were above 0.950 with P < 0.001.

CONCLUSIONS

The measurements for gait cycle, cadence, step length, velocity, and number of steps with the GoPro camera are highly consistent with the measurements with the motion analysis system. The measurements for IDEEA3 are consistent with those for the GoPro camera. IDEEA3 can be effectively used in the gait measurement of LSS patients.

Polestriding Intervention Improves Gait and Axial Symptoms in Mild to Moderate Parkinson Disease

OBJECTIVE

To evaluate the effects of 12-week polestriding intervention on gait and disease severity in people with mild to moderate Parkinson disease (PD).

DESIGN

A-B-A withdrawal study design.

SETTING

Outpatient movement disorder center and community facility.

PARTICIPANTS

Individuals (N=17; 9 women [53%] and 8 men [47%]; mean age, 63.7±4.9y; range, 53-72y) with mild to moderate PD according to United Kingdom brain bank criteria with Hoehn & Yahr score ranging from 2.5 to 3.0 with a stable medication regimen and ability to tolerate "off" medication state.

INTERVENTIONS

Twelve-week polestriding intervention with 12-week follow-up.

MAIN OUTCOME MEASURES

Gait was evaluated using several quantitative temporal, spatial, and variability measures. In addition, disease severity was assessed using clinical scales such as Unified Parkinson's Disease Rating Scale (UPDRS), Hoehn & Yahr scale, and Parkinson's Disease Questionnaire-39.

RESULTS

Step and stride lengths, gait speed, and step-time variability were improved significantly (P<.05) because of 12-week polestriding intervention. Also, the UPDRS motor score, the UPDRS axial score, and the scores of UPDRS subscales on walking and balance improved significantly after the intervention.

CONCLUSIONS

Because increased step-time variability and decreased step and stride lengths are associated with PD severity and an increased risk of falls in PD, the observed improvements suggest that regular practice of polestriding may reduce the risk of falls and improve mobility in people with PD.

Assessing functional mobility after lower limb reconstruction: a psychometric evaluation of a sensor-based mobility score

OBJECTIVE

To develop and validate a robust, objective mobility assessment tool, Hamlyn Mobility Score (HMS), using a wearable motion sensor.

BACKGROUND

Advances in reconstructive techniques allow more limbs to be salvaged. However, evidence demonstrating superior long-term outcomes compared with amputation is unavailable. Lack of access to quality regular functional mobility status may be preventing patients and health care staff from optimizing rehabilitation programs and evaluating the reconstructive services.

METHODS

In this prospective cohort study, 20 patients undergoing lower limb reconstruction and 10 age-matched controls were recruited. All subjects completed the HMS activity protocol twice under different instructors at 3 months postoperatively, and again at 6 months, while wearing an ear-worn accelerometer. Demographic and clinical data were also collected including a short-form health survey (SF-36). HMS parameters included standard test metrics and additional kinematic features extracted from accelerometer data. A psychometric evaluation was conducted to ascertain reliability and validity.

RESULTS

The HMS demonstrated excellent reliability (intraclass correlation coefficient >0.90, P < 0.001) and internal consistency (Cronbach α = 0.897). Concurrent validity was demonstrated by correlation between HMS and SF-36 scores (Spearman ρ = 0.666, P = 0.005). Significant HMS differences between healthy subjects and patients, stratified according to fracture severity, were shown (Kruskal-Wallis nonparametric 1-way analysis of variance, χ = 21.5, P < 0.001). The HMS was 50% more responsive to change than SF-36 (effect size: 1.49 vs 0.99).

CONCLUSIONS

The HMS shows satisfactory reliability and validity and may provide a platform to support adaptable, personalized rehabilitation and enhanced service evaluation to facilitate optimal patient outcomes.

Clinical assessment of spatiotemporal gait parameters in patients and older adults

Spatial and temporal characteristics of human walking are frequently evaluated to identify possible gait impairments, mainly in orthopedic and neurological patients, but also in healthy older adults. The quantitative gait analysis described in this protocol is performed with a recently-introduced photoelectric system (see Materials table) which has the potential to be used in the clinic because it is portable, easy to set up (no subject preparation is required before a test), and does not require maintenance and sensor calibration. The photoelectric system consists of series of high-density floor-based photoelectric cells with light-emitting and light-receiving diodes that are placed parallel to each other to create a corridor, and are oriented perpendicular to the line of progression. The system simply detects interruptions in light signal, for instance due to the presence of feet within the recording area. Temporal gait parameters and 1D spatial coordinates of consecutive steps are subsequently calculated to provide common gait parameters such as step length, single limb support and walking velocity, whose validity against a criterion instrument has recently been demonstrated. The measurement procedures are very straightforward; a single patient can be tested in less than 5 min and a comprehensive report can be generated in less than 1 min.

Using Wearable Activity Type Detection to Improve Physical Activity Energy Expenditure Estimation

Accurate, real-time measurement of energy expended during everyday activities would enable development of novel health monitoring and wellness technologies. A technique using three miniature wearable accelerometers is presented that improves upon state-of-the-art energy expenditure (EE) estimation. On a dataset acquired from 24 subjects performing gym and household activities, we demonstrate how knowledge of activity type, which can be automatically inferred from the accelerometer data, can improve EE estimates by more than 15% when compared to the best estimates from other methods.