Spatio-temporal gait parameters obtained from foot-worn inertial sensors are reliable in healthy adults in single- and dual-task conditions

Reliability of Obstacle-Crossing Parameters during Overground Walking in Young Adults

We aimed to evaluate the intra-session relative and absolute reliability of obstacle-crossing parameters during overground walking in young adults, and to determine the number of trials required to ensure reliable assessment. We analysed data from 43 young male adults who were instructed to walk at a self-selected velocity on a pathway and to step over an obstacle (height = 15 cm; width = 80 cm, thickness = 2 cm) three times. Spatial-temporal gait parameters of the approaching and crossing phases (i.e., before and after the obstacle) and obstacle clearance parameters (i.e., vertical and horizontal distance between the foot and the obstacle during crossing) were computed using a three-dimensional motion analysis system. Intraclass correlation coefficients were used to compute the relative reliability, while standard error of measurement and minimal detectable change were used to assess the absolute reliability for all possible combinations between trials. Results showed that most spatial-temporal gait parameters and obstacle clearance parameters are reliable using the average of three trials. However, the mean of the second and third trials ensures the best relative and absolute reliabilities of most obstacle-crossing parameters. Further works are needed to generalize these results in more realistic conditions and in other populations.

Can transcranial direct current stimulation combined with interactive computerized cognitive training boost cognition and gait performance in older adults with mild cognitive impairment? a randomized controlled trial

BACKGROUND

Older adults with Mild Cognitive Impairment (MCI) are often subject to cognitive and gait deficits. Interactive Computerized Cognitive Training (ICCT) may improve cognitive function; however, the effect of such training on gait performance is limited. Transcranial Direct Current Stimulation (tDCS) improves cognition and gait performance. It remains unclear whether combining tDCS with ICCT produces an enhanced synergistic effect on cognition and complex gait performance relative to ICCT alone. This study aimed to compare the effects of tDCS combined with ICCT on cognition and gait performance in older adults with MCI.

METHOD

Twenty-one older adults with MCI were randomly assigned to groups receiving either anodal tDCS and ICCT ( tDCS + ICCT ) or sham tDCS and ICCT ( sham + ICCT ). Participants played Nintendo Switch cognitive games for 40 min per session, simultaneously receiving either anodal or sham tDCS over the left dorsolateral prefrontal cortex for the first 20 min. Cognitive and gait assessments were performed before and after 15 training sessions.

RESULTS

The global cognition, executive function, and working-memory scores improved in both groups, but there were no significant interaction effects on cognitive outcomes. Additionally, the group × time interactions indicated that tDCS + ICCT significantly enhanced dual-task gait performance in terms of gait speed (p = 0.045), variability (p = 0.016), and dual-task cost (p = 0.039) compared to sham + ICCT.

CONCLUSION

The combined effect of tDCS and ICCT on cognition was not superior to that of ICCT alone; however, it had a significant impact on dual-task gait performance. Administering tDCS as an adjunct to ICCT may thus provide additional benefits for older adults with MCI.

TRIAL REGISTRATION

This trial was registered at http://www.

CLINICALTRIALS

in.th/ (TCTR 20,220,328,009).

A gait stability evaluation method based on wearable acceleration sensors

In this study, an accurate tool is provided for the evaluation of the effect of joint motion effect on gait stability. This quantitative gait evaluation method relies exclusively on the analysis of data acquired using acceleration sensors. First, the acceleration signal of lower limb motion is collected dynamically in real-time through the acceleration sensor. Second, an algorithm based on improved dynamic time warping (DTW) is proposed and used to calculate the gait stability index of the lower limbs. Finally, the effects of different joint braces on gait stability are analyzed. The experimental results show that the joint brace at the ankle and the knee reduces the range of motions of both ankle and knee joints, and a certain impact is exerted on the gait stability. In comparison to the ankle joint brace, the knee joint brace inflicts increased disturbance on the gait stability. Compared to the joint motion of the braced side, which showed a large deviation, the joint motion of the unbraced side was more similar to that of the normal walking process. In this paper, the quantitative evaluation algorithm based on DTW makes the results more intuitive and has potential application value in the evaluation of lower limb dysfunction, clinical training and rehabilitation.

DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units

In recent years, there has been a growing interest in developing and evaluating gait analysis algorithms based on inertial measurement unit (IMU) data, which has important implications, including sports, assessment of diseases, and rehabilitation. Multi-tasking and physical fatigue are two relevant aspects of daily life gait monitoring, but there is a lack of publicly available datasets to support the development and testing of methods using a mobile IMU setup. We present a dataset consisting of 6-minute walks under single- (only walking) and dual-task (walking while performing a cognitive task) conditions in unfatigued and fatigued states from sixteen healthy adults. Especially, nine IMUs were placed on the head, chest, lower back, wrists, legs, and feet to record under each of the above-mentioned conditions. The dataset also includes a rich set of spatio-temporal gait parameters that capture the aspects of pace, symmetry, and variability, as well as additional study-related information to support further analysis. This dataset can serve as a foundation for future research on gait monitoring in free-living environments.

Smartphone use affects gait performance, spinal kinematics and causes spinal musculoskeletal discomfort in young adults

BACKGROUND

Smartphone use may lead to alterations in spinal kinematics and musculoskeletal discomfort.

OBJECTIVES

The aim of this study was to evaluate the effect of smartphone use on spinal kinematics, and to examine the relationship between smartphone addiction, spinal discomfort, and gait parameters.

DESIGN

Cross-Sectional Study.

METHODS

The study included 42 healthy adults aged 18-30 years. A photographic method was used for spinal kinematic evaluation in sitting, standing and at the end of a 3-min walk. GAITRite electronic walkway was used for spatiotemporal gait parameters. Smartphone addiction was evaluated with the Smartphone Addiction Scale - Short Version (SAS-SV). The Cornell Musculoskeletal System Discomfort Questionnaire (CMDQ) was used to evaluate feelings of discomfort and pain.

RESULTS

There was an increase in head, cervical, and thoracic flexion angles while sitting, standing, and at the end of a 3-min walk. Similarly, an increase in thoracolumbar and lumbar flexion angles was observed only in the sitting position (p < 0.05). While using a smartphone during walking, cadence, walking speed, step length decreased, while step duration and double support duration increased (p < 0.05). A statistically significant correlation was determined between the SAS-SV and CMDQ scores (p < 0.05).

CONCLUSION

The study showed that smartphone use has an impact on spinal kinematics during sitting, standing and at the end of a 3 min-walk, as well as on the spatiotemporal parameters of gait. This study suggest that smartphone addiction should be taken into consideration due to its potential to cause musculoskeletal discomfort and there may be a need to raise public awareness on this matter.

Assessing falls in the elderly population using G-STRIDE foot-mounted inertial sensor

Falls are one of the main concerns in the elderly population due to their high prevalence and associated consequences. Guidelines for the management of the elder with falls are comprised of multidimensional assessments, especially gait and balance. Daily clinical practice needs for timely, effortless, and precise tools to assess gait. This work presents the clinical validation of the G-STRIDE system, a 6-axis inertial measurement unit (IMU) with onboard processing algorithms, that allows the calculation of walking-related metrics correlated with clinical markers of fall risk. A cross-sectional case-control study was conducted with 163 participants (falls and non-falls groups). All volunteers were assessed with clinical scales and conducted a 15-min walking test at a self-selected pace while wearing the G-STRIDE. G-STRIDE is a low-cost solution to facilitate the transfer to society and clinical evaluations. It is open hardware and flexible and, thus, has the advantage of providing runtime data processing. Walking descriptors were derived from the device, and a correlation analysis was conducted between walking and clinical variables. G-STRIDE allowed measuring walking parameters in non-restricted walking conditions (e.g. hallway). Walking parameters statistically discriminate between falls and non-falls groups. We found good/excellent estimation accuracy (ICC = 0.885; [Formula: see text]) for walking speed, showing good/excellent correlation between gait speed and several clinical variables. G-STRIDE can calculate walking-related metrics that allow for discrimination between falls and non-falls groups, which correlates with clinical indicators of fall risk. A preliminary fall-risk assessment based on the walking parameters was found to improve the Timed Up and Go test in the identification of fallers.

Test-retest reliability of kinematic and kinetic parameters during dual-task stair walking in the elderly

Objective: This study aims to evaluate the test-retest reliability of kinematics and kinetics during single and dual-task stair walking in the elderly. Methods: Fifteen healthy elderly adults were recruited. Kinematic and kinetic parameters were measured using an infrared motion analysis system (Vicon, Oxford Metrics Ltd., Oxford, United Kingdom) and force platforms (Switzerland, Kistler 9287BA and 9281CA). Participants were tested under single-task and dual-task (serial 3 subtractions or carrying a cup of water) conditions. Each participant completed two sessions on two separate days with a 1-week interval. Intraclass correlation coefficients (ICC), Pearson correlation coefficient (r), and Bland-Altman plot were used to assess the reliability of stair walking. Results: When ascending stairs, the ICC of kinematics and kinetics ranged from fair to excellent (ICC = 0.500-0.979) in the single and dual tasks, except for step length (ICC = 0.394) in the single task. The r value of kinematics and kinetics ranged from 0.704 to 0.999. When descending stairs, the ICC of kinematics and kinetics ranged from good to excellent (ICC = 0.661-0.963), except for min hip moment (ICC = 0.133) and min ankle moment (ICC = 0.057) in the manual task. The r value of kinematics and kinetics ranged from 0.773 to 0.960 in the single and dual tasks. In the Bland-Altman plots, all the zero values and most of the dots fell in the 95% confidence interval, and the mean difference was found to be close to zero for all the parameters during stair walking. Conclusion: These results obtained from this study show the good test-retest reliability of step cadence, step speed, and step width during single- and dual-task stair walking in the elderly, and the poor reliability of step length during ascending stairs. All the kinetic parameters, including min hip moment, max knee moment, and min ankle moment, had good test-retest reliability during single- and dual-task stair walking, but min hip moment and min ankle moment had poor reliability during manual-task descending stair. These results may help researchers in the assessment of biomechanics of dual-task stair walking in the elderly and to interpret the effect of interventions in this population.

Validity and reliability of the Apple Health app on iPhone for measuring gait parameters in children, adults, and seniors

This study assessed the concurrent validity and test-retest-reliability of the Apple Health app on iPhone for measuring gait parameters in different age groups. Twenty-seven children, 28 adults and 28 seniors equipped with an iPhone completed a 6-min walk test (6MWT). Gait speed (GS), step length (SL), and double support time (DST) were extracted from the gait recordings of the Health app. Gait parameters were simultaneously collected with an inertial sensors system (APDM Mobility Lab) to assess concurrent validity. Test-retest reliability was assessed via a second iPhone-instrumented 6MWT 1 week later. Agreement of the Health App with the APDM Mobility Lab was good for GS in all age groups and for SL in adults/seniors, but poor to moderate for DST in all age groups and for SL in children. Consistency between repeated measurements was good to excellent for all gait parameters in adults/seniors, and moderate to good for GS and DST but poor for SL in children. The Health app on iPhone is reliable and valid for measuring GS and SL in adults and seniors. Careful interpretation is required when using the Health app in children and when measuring DST in general, as both have shown limited validity and/or reliability.

Validation of Pressure-Sensing Insoles in Patients with Parkinson's Disease during Overground Walking in Single and Cognitive Dual-Task Conditions

There is a need for unobtrusive and valid tools to collect gait parameters in patients with Parkinson's disease (PD). The novel promising tools are pressure-sensing insoles connected to a smartphone app; however, few studies investigated their measurement properties during simple or challenging conditions in PD patients. This study aimed to examine the validity and reliability of gait parameters computed by pressure-sensing insoles (FeetMe^® insoles, Paris, France). Twenty-five PD patients (21 males, mean age: 69 (7) years) completed two walking assessment sessions. In each session, participants walked on an electronic pressure-sensitive walkway (GaitRite^®, CIR System Inc., Franklin, NJ, USA) without other additional instructions (i.e., single-task condition) and while performing a concurrent cognitive task (i.e., dual-task condition). Spatiotemporal gait parameters were measured simultaneously using the pressure-sensing insoles and the electronic walkway. Concurrent validity was assessed by correlation coefficients and Bland-Altman methodology. Test-retest reliability was examined by intraclass correlation coefficients (ICC) and minimal detectable changes (MDC). The validity results showed moderate to excellent correlations and good agreement between the two systems. Concerning test-retest reliability, moderate-to-excellent ICC values and acceptable MDC demonstrated the repeatability of the measured gait parameters. Our findings support the use of these insoles as complementary instruments to conventional tools during single and dual-task conditions.

Effects of different dual task training on dual task walking and responding brain activation in older adults with mild cognitive impairment

The concurrent additional tasking impacts the walking performance, and such impact is even greater in individuals with mild cognitive impairment (MCI) than in healthy elders. However, effective training program to improve dual task walking ability for the people with MCI is not immediately provided. Therefore, this study aimed to determine the effects of cognitive and motor dual task walking training on dual task walking performance and the responding brain changes in older people with MCI. Thirty older adults with MCI were randomly allocated to receive 24 sessions of 45-min cognitive dual task training (CDTT, n = 9), motor dual task training (MDTT, n = 11), or conventional physical therapy (CPT, n = 10). Gait performance and brain activation during single and dual task walking, and cognitive function assessed by trail-making test (TMT-A, B) and digit span test were measured at pre-, post-test, and 1-month follow-up. Both CDTT and MDTT improved dual task walking with responding activation changes in specific brain areas. The improvements in motor dual task walking performance after both dual task trainings were significantly better than after CPT in the older adults with MCI. Both cognitive and motor dual task training were feasible and beneficial to improve dual task walking ability in older adults with MCI.

Trial Registration: The trial was registered to Thai Clinical Trial Registry and the registration number is TCTR20180510002 (first registration date: 10/05/2018).

Comprehensive validation of a wearable foot sensor system for estimating spatiotemporal gait parameters by simultaneous three-dimensional optical motion analysis

BACKGROUND

Use of a wearable gait analysis system (WGAS) is becoming common when conducting gait analysis studies due to its versatility. At the same time, its versatility raises a concern about its accuracy, because its calculations rely on assumptions embedded in its algorithms. The purpose of the present study was to validate twenty spatiotemporal gait parameters calculated by the WGAS by comparison with simultaneous measurements taken with an optical motion capture system (OMCS).

METHODS

Ten young healthy volunteers wore two inertial sensors of the commercially available WGAS, Physilog®, on their feet and 23 markers for the OMCS on the lower part of the body. The participants performed at least three sets of 10-m walk tests at their self-paced speed in the laboratory equipped with 12 high-speed digital cameras with embedded force plates. To measure repeatability, all participants returned for a second day of testing within two weeks.

RESULTS

Twenty gait parameters calculated by the WGAS had a significant correlation with the ones determined by the OMCS. Bland and Altman analysis showed that the between-device agreement for twenty gait parameters was within clinically acceptable limits. The validity of the gait parameters generated by the WGAS was found to be excellent except for two parameters, swing width and maximal heel clearance. The repeatability of the WGAS was excellent when measured between sessions.

CONCLUSION

The present study showed that spatiotemporal gait parameters estimated by the WGAS were reasonably accurate and repeatable in healthy young adults, providing a scientific basis for applying this system to clinical studies.

Amputee Fall Risk Classification Using Machine Learning and Smartphone Sensor Data from 2-Minute and 6-Minute Walk Tests

The 6-min walk test (6MWT) is commonly used to assess a person’s physical mobility and aerobic capacity. However, richer knowledge can be extracted from movement assessments using artificial intelligence (AI) models, such as fall risk status. The 2-min walk test (2MWT) is an alternate assessment for people with reduced mobility who cannot complete the full 6MWT, including some people with lower limb amputations; therefore, this research investigated automated foot strike (FS) detection and fall risk classification using data from a 2MWT. A long short-term memory (LSTM) model was used for automated foot strike detection using retrospective data (n = 80) collected with the Ottawa Hospital Rehabilitation Centre (TOHRC) Walk Test app during a 6-min walk test (6MWT). To identify FS, an LSTM was trained on the entire six minutes of data, then re-trained on the first two minutes of data. The validation set for both models was ground truth FS labels from the first two minutes of data. FS identification with the 6-min model had 99.2% accuracy, 91.7% sensitivity, 99.4% specificity, and 82.7% precision. The 2-min model achieved 98.0% accuracy, 65.0% sensitivity, 99.1% specificity, and 68.6% precision. To classify fall risk, a random forest model was trained on step-based features calculated using manually labeled FS and automated FS identified from the first two minutes of data. Automated FS from the first two minutes of data correctly classified fall risk for 61 of 80 (76.3%) participants; however, <50% of participants who fell within the past six months were correctly classified. This research evaluated a novel method for automated foot strike identification in lower limb amputee populations that can be applied to both 6MWT and 2MWT data to calculate stride parameters. Features calculated using automated FS from two minutes of data could not sufficiently classify fall risk in lower limb amputees.

Validation of shoe-worn Gait Up Physilog®5 wearable inertial sensors in adolescents

BACKGROUND

Gait Up Physilog® wearable inertial sensors are a powerful alternative to traditional laboratory-based gait assessment for children with gait impairment. To build clinician trust in these devices and ultimately facilitate their use outside confined spaces, studies have examined performance of previous versions of Physilog® wearable inertial sensors but predominant focus has been on older adults. Despite their different gait patterns and behavioural/cognitive profiles, there are limited studies in children.

RESEARCH QUESTION

To determine whether key spatiotemporal gait parameters (stride length, time and velocity) collected by shoe-worn Physilog®5 sensors in a hallway assessment protocol are a valid method of gait assessment in typically developing adolescents aged 12-15 years.

METHODS

A total 30 typically developing participants (50 % female) median age 13.7 (interquartile range 2.34) were assessed in an exploratory study whilst walking at self-selected speed over the GAITRite® electronic walkway, concurrently wearing Physilog®5 sensors. Concurrent validity 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

Mean stride data demonstrated substantial agreement for stride length (CCC = 0.975) and stride velocity (CCC = 0.979) to almost perfect agreement for stride time (CCC > 0.996). Agreement between the technologies for individual stride-to-stride data remained high for stride time (CCC = 0.952); yet reduced for stride length (CCC = 0.868) and stride velocity (CCC = 0.877). Male/female differences in performance of the technology were observed for stride velocity, favouring females.

SIGNIFICANCE

Physilog®5 inertial sensors accurately measure walking in adolescents, with stride time the most accurately detected parameter. This demonstrates that wearables can be used by researchers and clinicians working with adolescent groups as an alternative to fixed systems. These findings will ultimately pave the way to using wearables for assessments with children outside of the laboratory environment.

Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations

Foot strike detection is important when evaluating a person’s gait characteristics. Accelerometer and gyroscope signals from smartphones have been used to train artificial intelligence (AI) models for automated foot strike detection in able-bodied and elderly populations. However, there is limited research on foot strike detection in lower limb amputees, who have a more variable and asymmetric gait. A novel method for automated foot strike detection in lower limb amputees was developed using raw accelerometer and gyroscope signals collected from a smartphone positioned at the posterior pelvis. Raw signals were used to train a decision tree model and long short-term memory (LSTM) model for automated foot strike detection. These models were developed using retrospective data (n = 72) collected with the TOHRC Walk Test app during a 6-min walk test (6MWT). An Android smartphone was placed on a posterior belt for each participant during the 6MWT to collect accelerometer and gyroscope signals at 50 Hz. The best model for foot strike identification was the LSTM with 100 hidden nodes in the LSTM layer, 50 hidden nodes in the dense layer, and a batch size of 64 (99.0% accuracy, 86.4% sensitivity, 99.4% specificity, and 83.7% precision). This research created a novel method for automated foot strike identification in lower extremity amputee populations that is equivalent to manual labelling and accessible for clinical use. Automated foot strike detection is required for stride analysis and to enable other AI applications, such as fall detection.

Association of gait with global cognitive function and cognitive domains detected by MoCA-J among community-dwelling older adults: a cross-sectional study

BACKGROUND

Gait was proved to be strongly associated with global cognitive function and multiple cognitive domains; however, previous research usually concentrated on individual gait parameters. This study used wearable sensors to measure gait parameters in different aspects and comprehensively explored the association of gait with global cognitive function and cognitive domains.

METHODS

The data of this cross-sectional study were obtained from 236 community-dwelling Japanese older adults (125 men and 111 women) aged 70-81 years. Gait was measured by asking participants to walk a 6-m course and back using the Physilog® sensors (GaiUp®, Switzerland). Global cognitive function and cognitive domains were evaluated by face-to-face interviews using the Japanese version of the Montreal Cognitive Assessment. Twenty gait parameters were summarized as independent gait factors using factor analysis. A generalized linear model and linear regression model were used to explore the relationship of gait with global cognitive function and cognitive domains adjusted for several confounding factors.

RESULTS

Factor analysis yielded four gait factors: general cycle, initial contact, propulsion, and mid-swing. Among them, general cycle factor was significantly associated with global cognitive function (β = - 0.487, [- 0.890, - 0.085]) and executive function (P = 0.049); initial contact was associated with executive function (P = 0.017).

CONCLUSION

General cycle of gait might be the better marker of global cognitive function and gait is most strongly associated with executive function. The longitudinal relationships should be examined in future cohort studies.