Validation of a smartphone embedded inertial measurement unit for measuring postural stability in older adults

Vision-based postural balance assessment of sit-to-stand transitions performed by younger and older adults

BACKGROUND

The use of inertial measurement units (IMUs) in assessing fall risk is often limited by subject discomfort and challenges in data interpretation. Additionally, there is a scarcity of research on attitude estimation features. To address these issues, we explored novel features and representation methods in the context of sit-to-stand transitions. This study recorded sit-to-stand transition test data from three groups: community-dwelling elderly, elderly in day care centers (DCC), and college students, captured using mobile phone cameras.

METHOD

We employed pose estimation technology to extract key point kinematic features from the video data and used 10-fold cross-validation to train a random forest classifier, mitigating the impact of individual differences. We trained classifiers with the top 5, 10, and 15 features, calculating the average area under the receiver operating characteristic curve (AUC) for each model to compare feature importance.

RESULTS

Our results indicated that elbow key point features, such as (KP08) mean Y, (KP08)RMS Y, (KP09) mean Y, and (KP09) RMS Y, are crucial for distinguishing between subject groups. Statistical tests further validated the significance of these features. The application of human pose estimation and key point signals shows promise for clinical postural balance screening. The identified features can be utilized to develop non-invasive tools for assessing postural instability risk, contributing to fall prevention efforts.

CONCLUSION

This study lays the groundwork for integrating additional measurement modalities into sit-to-stand transition analysis to enhance clinical strategies.

Validity and Reliability of an Integrated Smartphone Measurement Approach for Balance

CONTEXT

Clinical balance assessments vary in reliability due to subjectivity in their scoring. A valid and objective accelerometer-based smartphone evaluation could benefit patients, clinicians, and researchers.

OBJECTIVE

Our objective was to assess the validity and reliability of smartphone-based standing balance.

DESIGN

A repeated-measures study was conducted with 23 healthy young adult participants across 2 sessions ∼7 days apart.

METHODS

Participants completed 30-second standing trials during tandem-stance eyes-open, tandem-stance eyes-closed, single-leg eyes-open, and single-leg eyes-closed conditions. Android and iOS smartphones were placed vertically on the lower back via a belt with 3 retroreflective markers attached and tracked by an 8-camera motion capture system. Sway path, range, and area were calculated from smartphone accelerometer and marker data. We assessed reliability using intraclass correlation coefficients (ICC[2,k]) and validity using Pearson r correlations between the marker and smartphones from visit 1.

RESULTS

Across eyes-open conditions, Android (ICC = .84-.96), iOS (ICC = .82-.98), and marker-based (ICC = .84-.95) assessments demonstrated good to excellent reliability. Across eyes-closed conditions, Android (ICC = .41-.87), iOS (ICC = .34-.79), and marker-based (ICC = .31-.87) assessments demonstrated poor to good reliability. Correlations between smartphones and the marker data were moderate to very high (r = .56-.97).

CONCLUSIONS

The smartphone-based assessment was valid and reliable, indicating that clinicians and researchers can implement this method to measure balance with the opportunity for remote administration and increased patient tracking across various recovery timepoints.

Estimating whole-body centre of mass sway during quiet standing with inertial measurement units

Our ability to balance upright provides a stable platform to perform daily activities. Balance deficits associated with various clinical conditions may affect activities of daily living, highlighting the importance of quantifying standing balance in ecological environments. Although typically performed in laboratory settings, the growing availability of low-cost inertial measurement units (IMUs) allows the assessment of balance in the real world. However, it is unclear how many IMUs are required to adequately estimate linear displacements of the centre of mass (CoM) at stance widths associated with daily activities. While wearing IMUs on their head, sternum, back, right thigh, right shank, and left shank, 16 participants stood quietly on a force platform in narrow, hip-width, and shoulder-width stances, each for three two-minute trials. Using a multi-segment biomechanical model, we estimated CoM displacements from all possible combinations of the IMUs. We then calculated the correlation between the IMU- and force platform- CoM estimates to determine the minimal number of IMUs needed to estimate CoM sway. Four IMUs were necessary to accurately estimate anteroposterior (AP) and mediolateral (ML) CoM displacements across stance widths. Using IMUs on the back, right thigh, and both shanks, we found strong correlations between the IMU CoM estimation and the force platform CoM estimation in narrow stance (AP: r = 0.92±0.04, RMSE = 2.39±2.08 mm; ML: r = 0.97±0.02, RMSE = 1.16±0.77 mm), hip-width stance (AP: r = 0.93±0.04, RMSE = 2.00±1.18 mm; ML: r = 0.92±0.06, RMSE = 0.92±0.70 mm), and shoulder-width stance (AP: r = 0.93±0.03, RMSE = 1.95±1.66 mm; ML: r = 0.86±0.13, RMSE = 1.39±1.46 mm). These results indicate that IMUs can be used to estimate CoM displacements during quiet standing and that four IMUs are necessary to do so. Using an algorithm based on a simple biomechanical model, researchers and clinicians can estimate whole-body CoM displacements accurately during unperturbed quiet standing. This approach can improve the ecological validity of standing balance research and opens the possibility for assessing/monitoring patients with standing balance deficits.

Balance Assessment Using a Handheld Smartphone with Principal Component Analysis for Anatomical Calibration

Most balance assessment studies using inertial measurement units (IMUs) in smartphones use a body strap and assume the alignment of the smartphone with the anatomical axes. To replace the need for a body strap, we have used an anatomical alignment method that employs a calibration maneuver and Principal Component Analysis (PCA) so that the smartphone can be held by the user in a comfortable position. The objectives of this study were to determine if correlations existed between angular velocity scores derived from a handheld smartphone with PCA functional alignment vs. a smartphone placed in a strap with assumed alignment, and to analyze acceleration score differences across balance poses of increasing difficulty. The handheld and body strap smartphones exhibited moderately to strongly correlated angular velocity scores in the calibration maneuver (r = 0.487-0.983, p < 0.001). Additionally, the handheld smartphone with PCA functional calibration successfully detected significant variance between pose type scores for anteroposterior, mediolateral, and superoinferior acceleration data (p < 0.001).

Validation of the Equidyn protocol for evaluation of dynamic balance in older adults through a smartphone application

BACKGROUND

Different tasks and proxy measurements have been employed to evaluate dynamic balance in older individuals. However, due to inherent limitations, results from most evaluations could hardly be taken as valid measurements of dynamic balance.

RESEARCH QUESTION

Is the Equidyn smartphone application-based protocol valid and sensitive for assessment of dynamic balance in older adults?

METHODS

Dynamic balance was evaluated in 52 physically active individuals, age range 60-80 years (M = 69.36). The dynamic tasks were one-leg sway either in the mediolateral (ML) or anteroposterior (AP) direction while supported on the contralateral leg, and cyclic sit-to-stand with a narrow support base. These tasks were performed under standardized movement amplitude and rhythm. Outcomes were correlated with unipedal quiet standing. A smartphone was attached to the trunk backside, and a custom-made application (Equidyn) was employed to provide guidance throughout evaluation, timed beeps to pace the movements, and three-dimensional trunk acceleration measurement for balance evaluation.

RESULTS

Our data showed (a) that both ML and AP leg sway tasks were sensitive to aging and to direction of leg sway movements; (b) referenced to quiet unipedal stance, moderate/strong correlations for the ML/AP leg sway tasks and moderate correlations for the sit-to-stand task; and (c) moderate/strong correlations between the ML and AP leg sway tasks, and moderate correlations between the sit-to-stand and the two unipedal dynamic tasks in the ML acceleration direction.

SIGNIFICANCE

The current results support the conclusion that the Equidyn protocol is a sensitive and valid tool to evaluate dynamic balance in healthy older individuals. The protocol tasks standardized in amplitude and rhythm favor their reproducibility and trunk acceleration data interpretation. As the whole assessment is made through a smartphone application, this dynamic balance evaluation could be made in a low-cost simple way both in the laboratory and clinical settings.

Early detection of diseases causing dementia using digital navigation and gait measures: A systematic review of evidence

Wearable digital technologies capable of measuring everyday behaviors could improve the early detection of dementia-causing diseases. We conducted two systematic reviews following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to establish the evidence base for measuring navigation and gait, two everyday behaviors affected early in AD and non-AD disorders and not adequately measured in current practice. PubMed and Web of Science databases were searched for studies on asymptomatic and early-stage symptomatic individuals at risk of dementia, with the Newcastle-Ottawa Scale used to assess bias and evaluate methodological quality. Of 316 navigation and 2086 gait records identified, 27 and 83, respectively, were included in the final sample. We highlight several measures that may identify at-risk individuals, whose quantifiability with different devices mitigates the risk of future technological obsolescence. Beyond navigation and gait, this review also provides the framework for evaluating the evidence base for future digital measures of behaviors considered for early disease detection.

Watch your step: A pilot study of smartphone use effect on young females' gait performance while walking up and down stairs and escalators

The increasingly ubiquitous use of smartphones has made distracted walking common, not only on flat ground, but also on stairs. Available information regarding changes in gait performance while walking and using a smartphone in different environments is still lacking. We aimed to investigate the differences in gait behavior and subjective walking confidence while walking up and down stairs and escalators, with and without smartphone use. A field experiment involving 32 female adults was conducted at a subway station. Gait parameters collected included step frequency, acceleration root mean square, step variability, step regularity, and step symmetry. The results showed that walking task, walking environment, and walking direction significantly affected gait performance and walking confidence. Overall, playing games or texting while walking down escalators resulted in the lowest walking confidence and the largest gait performance decrement: slower step frequency; reduced root mean square; decreased step regularity and step symmetry; and increased step variability. Step frequency, step variability, and step regularity significantly correlated with walking confidence. Smartphone use while walking on stairs and escalators significantly affects gait behavior and might increase the risk of falls. Interventions and prevention are needed to increase safety education and hazard warnings for the general population.

Assessing Gait & Balance in Adults with Mild Balance Impairment: G&B App Reliability and Validity

Smartphone applications (apps) that utilize embedded inertial sensors have the potential to provide valid and reliable estimations of different balance and gait parameters in older adults with mild balance impairment. This study aimed to assess the reliability, validity, and sensitivity of the Gait&Balance smartphone application (G&B App) for measuring gait and balance in a sample of middle- to older-aged adults with mild balance impairment in Pakistan. Community-dwelling adults over 50 years of age (N = 83, 50 female, range 50-75 years) with a Berg Balance Scale (BBS) score between 46/56 and 54/56 were included in the study. Data collection involved securing a smartphone to the participant's lumbosacral spine. Participants performed six standardized balance tasks, including four quiet stance tasks and two gait tasks (walking looking straight ahead and walking with head turns). The G&B App collected accelerometry data during these tasks, and the tasks were repeated twice to assess test-retest reliability. The tasks in quiet stance were also recorded with a force plate, a gold-standard technology for measuring postural sway. Additionally, participants completed three clinical measures, the BBS, the Functional Reach Test (FRT), and the Timed Up and Go Test (TUG). Test-retest reliability within the same session was determined using intraclass correlation coefficients (ICCs) and the standard error of measurement (SEM). Validity was evaluated by correlating the G&B App outcomes against both the force plate data and the clinical measures using Pearson's product-moment correlation coefficients. To assess the G&B App's sensitivity to differences in balance across tasks and repetitions, one-way repeated measures analyses of variance (ANOVAs) were conducted. During quiet stance, the app demonstrated moderate reliability for steadiness on firm (ICC = 0.72) and compliant surfaces (ICC = 0.75) with eyes closed. For gait tasks, the G&B App indicated moderate to excellent reliability when walking looking straight ahead for gait symmetry (ICC = 0.65), walking speed (ICC = 0.93), step length (ICC = 0.94), and step time (ICC = 0.84). The TUG correlated with app measures under both gait conditions for walking speed (r -0.70 and 0.67), step length (r -0.56 and -0.58), and step time (r 0.58 and 0.50). The BBS correlated with app measures of walking speed under both gait conditions (r 0.55 and 0.51) and step length when walking with head turns (r = 0.53). Force plate measures of total distance wandered showed adequate to excellent correlations with G&B App measures of steadiness. Notably, G&B App measures of walking speed, gait symmetry, step length, and step time, were sensitive to detecting differences in performance between standard walking and the more difficult task of walking with head turns. This study demonstrates the G&B App's potential as a reliable and valid tool for assessing some gait and balance parameters in middle-to-older age adults, with promise for application in low-income countries like Pakistan. The app's accessibility and accuracy could enhance healthcare services and support preventive measures related to fall risk.

Accelerometry applications and methods to assess standing balance in older adults and mobility-limited patient populations: a narrative review

Accelerometers provide an opportunity to expand standing balance assessments outside of the laboratory. The purpose of this narrative review is to show that accelerometers are accurate, objective, and accessible tools for balance assessment. Accelerometry has been validated against current gold standard technology, such as optical motion capture systems and force plates. Many studies have been conducted to show how accelerometers can be useful for clinical examinations. Recent studies have begun to apply classification algorithms to accelerometry balance measures to discriminate populations at risk for falls. In addition to healthy older adults, accelerometry can monitor balance in patient populations such as Parkinson's disease, multiple sclerosis, and traumatic brain injury. The lack of software packages or easy-to-use applications have hindered the shift into the clinical space. Lack of consensus on outcome metrics has also slowed the clinical adoption of accelerometer-based balance assessments. Future studies should focus on metrics that are most helpful to evaluate balance in specific populations and protocols that are clinically efficacious.

Estimation of center of pressure information by smartphone sensors for postural control training

Postural control training based on physical information is a common rehabilitation training method for patients with movement disorder. This research aims to verify the feasibility of using one smartphone embedded sensors to estimate CoP (Center of Pressure) displacement to take postural control training. We tested the reliability of smartphone sensor by motion capture system based on the following two CoP calculation models: (1) one-link inverted pendulum model; (2) two-link inverted pendulum model. We compared the estimation results with real CoP values measured by force plate. Sway training experiment was conducted under two tasks conditions: feet apart and feet together. The results show that data obtained from smartphone sensors is capable of representing human body CoM (Center of Mass) information. These two models can roughly estimate CoP displacement; and the results suggested that the two-link model performed better than one-link model. The estimation error between smartphone and real value is 0.70 - 2.01 cm in feet apart task and 1.03 - 1.12 cm in feet together task with two-link model.Clinical Relevance- This study verified the performance of smartphone in estimating CoP displacement for postural control training.

iPhone Accelerometry Provides a Sensitive In-Home Assessment of Age-Related Changes in Standing Balance

Remote health monitoring has become increasingly important, especially in aging populations. We aimed to identify tasks that are sensitive to age-related changes in balance during fully remote, at-home balance assessment. Participants were 12 healthy young adults (mean age = 26.08 years, range: 18-33) and 12 healthy older adults (mean age = 67.33 years, range: 60-75). Participants performed standing tasks monitored via video conference while their balance was quantified using a custom iPhone application measuring mediolateral center of mass acceleration. We included three stances (feet together, tandem, and single leg) with eyes open or closed, with or without a concurrent cognitive task. Older adults demonstrated significantly more variable center of mass accelerations in tandem (p = .04, ηp2=.25) and significantly higher (p < .01, ηp2=.45) and more variable (p < .01, ηp2=.44) center of mass accelerations in single leg compared with young adults. We also observed that as task challenge increased, balance dual-task cost diminished for older, but not young, adults.

A Review of MEMS Vibrating Gyroscopes and Their Reliability Issues in Harsh Environments

Micro-electromechanical systems (MEMS) vibrating gyroscopes have gained a lot of attention over the last two decades because of their low power consumption, easy integration, and low fabrication cost. The usage of the gyroscope equipped with an inertial measurement unit has increased tremendously, with applications ranging from household devices to smart electronics to military equipment. However, reliability issues are still a concern when operating this inertial sensor in harsh environments, such as to control the movement and alignment of mini-satellites in space, tracking firefighters at an elevated temperature, and assisting aircraft navigation in gusty turbulent air. This review paper focuses on the key fundamentals of the MEMS vibrating gyroscopes, first discussing popular designs including the tuning fork, gimbal, vibrating ring, and multi-axis gyroscopes. It further investigates how bias stability, angle random walk, scale factor, and other performance parameters are affected in harsh environments and then discusses the reliability issues of the gyroscopes.

Static Balance Digital Endpoints with Mon4t: Smartphone Sensors vs. Force Plate

Static balance tests are conducted in various clinics for diagnosis and treatment adjustment. As a result of population aging, the accessibility of these tests should be increased, in the clinic, and for remote patient examination. A number of publications have already conducted static balance evaluations using the sensors embedded in a smartphone. This study focuses on the applicability of using smartphone-based balance assessment on a large scale while considering ease of use, safety, and reliability. The Mon4t^® app was used to acquire the postural motion using different smartphone devices, different smartphone locations, and various standing postures. The signals derived from the app were compared to the center of pressure displacement derived from a force plate. The results showed moderate to high agreement between the two methods, particularly at the tandem stance (0.69 ≤ r ≤ 0.91). Preliminary data collection was conducted on three healthy participants, followed by 50 additional healthy volunteers, aged 65+. The results demonstrated that the Mon4t app can serve as an accessible and inexpensive static balance assessment tool, both in clinical settings and for remote patient monitoring, which is key for enabling telehealth.

Mobile Technology for Falls Prevention in Older Adults

Falls are the leading cause of accidental death in older adults that result from a complex interplay of risk factors. Recently, the need for person-centered approach utilizing personalization, prediction, prevention and participation, known as the P4 model, in fall prevention has been highlighted. Features of mobile technology make it a suitable technological infrastructure to employ such an approach. This narrative review aims to review the evidence for using mobile technology for personalized fall risk assessment and prevention since 2017 in older adults. We aim to identify lessons learned and future directions for using mobile technology as a fall risk assessment and prevention tool. Articles were searched in PubMed and Web of Science with search terms related to older adults, mobile technology, and falls prevention. A total of 23 articles were included. Articles were identified as those examining aspects of the P4 model including prediction (measurement of fall risk), personalization (usability), prevention, and participation. Mobile technology appears to be comparable to gold-standard technology in measuring well-known fall risk factors including static and dynamic balance. Seven applications were developed to measure different fall risk factors and tested for personalization, and/or participation aspects, and four were integrated into a falls prevention program. Mobile health technology offers an innovative solution to provide tailored fall risk screening, prediction, and participation. Future studies should incorporate multiple, objective fall risk measures and implement them in community settings to determine if mobile technology can offer tailored and scalable interventions.

Fisheye Image Detection of Trees Using Improved YOLOX for Tree Height Estimation

Tree height is an essential indicator in forestry research. This indicator is difficult to measure directly, as well as wind disturbance adds to the measurement difficulty. Therefore, tree height measurement has always been an issue that experts and scholars strive to improve. We propose a tree height measurement method based on tree fisheye images to improve the accuracy of tree height measurements. Our aim is to extract tree height extreme points in fisheye images by proposing an improved lightweight target detection network YOLOX-tiny. We added CBAM attention mechanism, transfer learning, and data enhancement methods to improve the recall rate, F₁ score, AP, and other indicators of YOLOX-tiny. This study improves the detection performance of YOLOX-tiny. The use of deep learning can improve measurement efficiency while ensuring measurement accuracy and stability. The results showed that the highest relative error of tree measurements was 4.06% and the average relative error was 1.62%. The analysis showed that the method performed better at all stages than in previous studies.

Smartphone applications to assess gait and postural control in people with multiple sclerosis: A systematic review

BACKGROUND

Methods to effectively assess gait and balance impairments are necessary to guide interventions among people with Multiple Sclerosis (PwMS). Smartphone-based evaluations are becoming popular due to the ubiquitous use of smartphones in society.

OBJECTIVE

To determine the current state of smartphone applications that assess gait and balance among PwMS.

METHODS

Two independent reviewers screened articles retrieved from PubMed, Web of Science, Scopus, CINAHL, and SportDiscuss. Articles meeting eligibility criteria were summarized and qualitatively discussed. Participant characteristics, validity, reliability, sensitivity and specificity measures, and main results of smartphone-based gait and balance evaluations were summarized. Methodological quality appraisal of the studies was performed using the quality assessment tool for observational cohort and cross-sectional studies.

RESULTS

Eight articles were included in this review. The studies present mostly with low risk of bias. All studies successfully tested the use of smartphone applications in assessing gait and balance among PwMS. In total, 75% of the studies evaluated the validity; 38% evaluated the reliability, sensitivity, and specificity of smartphone applications to assess gait and balance. Of those, all studies except one found smartphone applications to be appropriately valid, reliable, sensitive, and specific in assessing gait and balance. Most studies (88%) reported PwMS and clinicians as their intended users.

CONCLUSION

There is evidence supporting the use of smartphone applications to assess gait and balance among PwMS. Future studies should further examine the psychometric properties of smartphone-based gait and postural control assessments as well as the sensitivity and specificity to improve the interpretation of the results.