Background: Walking relies on synchronized joint actions guided by the central nervous system and sensorimotor circuits. Physical fatigue can alter these functions and affect walking performance, but its impact on inter-joint coordination is poorly understood. Research question: How does physical fatigue affect inter-joint coordination under different walking conditions? Methods: Twelve young adults performed a progressively inclined treadmill walking to induce physical fatigue, monitored by respiratory exchange ratio, heart rate, and perceived exertion. Sagittal plane joint kinematics of the right leg was analyzed pre- and post-fatigue during overground walking under preferred and fast speeds and treadmill walking. Inter-joint coordination was assessed using continuous relative phase metrics: mean absolute relative phase (MARP), deviation phase (DP), and cross-correlation coefficient. A two-way repeated measures ANOVA evaluated the effects of fatigue and walking conditions. Results: For hip–knee coordination, MARP and DP differed significantly across walking conditions during the stance phase ([Formula: see text]), with fatigue-related changes in MARP observed in fast walking. Cross-correlation coefficients exceeded 0.79 in both the stance and swing phases. For knee–ankle coordination, MARP varied significantly across walking conditions ([Formula: see text]) and fatigue states ([Formula: see text]), with cross-correlation coefficients also differing across conditions ([Formula: see text]). Significance: Physical fatigue reduces inter-joint coordination variability during walking, potentially increasing the risk of lower limb injuries but may be serving as an adaptive strategy for complex tasks like fast walking. Variability differs across gait phases and joints due to their functional roles. Treadmill walking may mask natural variability, warranting careful data interpretation.

Altered plantar pressure distribution may be associated with patellofemoral pain syndrome (PFP), particularly during dynamic activities; however, findings on task-specific adaptations remain inconsistent. A systematic search was conducted in major databases for articles published up to January 2025 using key terms on patellar/knee pain, plantar pressure, and foot loading. Studies were included if they compared plantar pressure variables between individuals with PFP and controls during walking, running, stair descent, squatting, or jumping. Eleven studies met the eligibility criteria. Across studies, findings varied in definition and magnitude. Six walking studies reported reduced medial forefoot pressure and increased medial heel contact in individuals with PFP. One prospective running study reported increased lateral heel and forefoot loading in those who developed PFP. A stair descent study reported greater midfoot and medial heel contact during step down, while squatting and jumping tasks reported asymmetrical loading patterns and shifts in centre of pressure. Overall, this review suggests task-specific plantar pressure alterations in individuals with PFP. However, the evidence remains limited, and heterogeneity in activity types, pressure systems, analysis methods, and study designs contributed to inconsistent findings. While some patterns may reflect changes in loading, causal relationships cannot be established, underscoring the need for prospective research.

This narrative review examines the use of inertial measurement units (IMUs) for assessing gait balance control. Impaired gait balance control is associated with an increased risk of falls and reduced mobility, particularly in older adults. Traditional methods of assessing gait balance control, such as clinical balance assessments and camera-based motion analysis, have limitations in terms of reliability, cost, and practicality. Wearable sensor technology, including IMUs, offers a more accessible and cost-effective alternative for assessing gait and balance performance in real-world settings. IMUs, equipped with tri-axial accelerometers, gyroscopes, and magnetometers, can directly measure body movement and provide quantifiable data. This review explores the advantages and limitations of using IMUs for assessing gait balance control, including the measurement of anticipatory postural adjustments (APAs) for gait initiation, spatiotemporal gait parameters, center of mass (COM) motion during walking, and data-driven machine learning models. IMUs have shown promise in quantifying APAs, estimating gait spatiotemporal parameters, assessing COM motion, and using machine learning algorithms to classify and predict balance-related outcomes. However, further research is needed to establish standardized protocols, validate IMU-based measurements, and determine the specific IMU parameters that correlate with balance control ability. Overall, IMUs have the potential to be a valuable tool for assessing gait balance control, monitoring changes over time, and tracking interventions to improve balance control in both clinical and research settings.

BACKGROUND:Obstacle-crossing (OC) requires dynamic balance and cognitive attention, which may decline with age and fatigue. While age-related gait changes are known, the combined effects of fatigue and cognitive demand on OC remain unclear. RESEARCH QUESTION:How does performance fatigability affect balance and crossing performance during single- and dual-task OC in older versus younger adults? METHODS:Seventeen older and 17 young adults (9 females in each group) with post-fatigue Rating of Perceived Exertion (RPE) > 15/20 were included. In Visit 1, participants were familiarized with OC and a working memory task. In Visit 2, they performed single- and dual-task OC trials before and after a fatiguing sit-to-stand protocol. Motion capture and force plates recorded toe-obstacle clearance, foot placement, crossing velocity, and balance (measured as center of mass-center of pressure inclination angle, IA). Fatigue was assessed via RPE and knee extension torque. Cognitive performance was evaluated by accuracy and reaction time. Data were analyzed using repeated measures ANOVAs and mixed-effects models (α =.05). RESULTS:Both age groups experienced similar exertion and strength loss post-fatigue. Increased mediolateral IA indicated reduced balance control, though crossing velocity and foot placement remained stable. Post-fatigue, the typical increase in toe-obstacle clearance during dual-tasking was reduced. Surprisingly, cognitive performance improved post-fatigue, with higher accuracy and faster reaction times. SIGNIFICANCE:Fatigue impaired gait balance control in both age groups, shown by increased IA. The reduced toe-clearance during dual-tasking post-fatigue suggests decreased emphasis on safe obstacle navigation. Improved cognitive performance without slowing suggests a shift in attentional priorities.

Relying on patients to make clean contacts with multiple force plates during clinical gait analysis can be time-consuming and dissuade clinicians from collecting biomechanical data. Several methods have been proposed to estimate ground reaction forces (GRFs) without force plates, though many can be computationally intensive. As an alternative to measuring GRFs, we evaluated the whole cycle and time frame accuracy of a built-in, GUI-based GRF estimation tool. Twenty-seven healthy adult participants walked over two consecutive force plates. We evaluated the accuracy of estimated GRFs against force plates as the gold standard using correlation, residual error analysis, and statistical parametric mapping (SPM). Estimated GRF magnitudes were accurate in the anterior-posterior (R2=0.86, %RMSE=7.28), and vertical directions (R2=0.86, %RMSE=6.09), but not in the medio-lateral direction (R2=0.30, %RMSE=12.46). SPM revealed errors in the sagittal plane in mid- and late-stance, with no specific errors in the medio-lateral direction. Estimated centers of pressure (CoPs) showed good accuracy (R2≥0.99, %RMSE≤4.46). However, SPM showed estimated CoPs were consistently anterior to the true CoPs. Sagittal GRFs are predicted with good accuracy, showing whole cycle errors comparable to more complex methods, while also displaying little early stance error and offering user-friendly implementation.

Falls are the leading cause of injury related morbidity and mortality in older adults. Primary and secondary prevention strategies that address modifiable risk factors are critically important to reduce the number of falls and fall related injuries. A number of evidence-based fall prevention programs are available, but few offer potential for broad dissemination and public health impact due to implementation barriers, such as a need for trained program leaders and clinicians. The study will use a randomized controlled trial design to evaluate incorporating physical therapy exercises (primary prevention strategy) within an existing intervention called Walk with Ease. While Walk with Ease has an established evidence-base related to the management of arthritis pain and symptoms, the present study will determine the potential to also reduce falls and fall risk in community-dwelling older adults. The integrated process and outcome evaluation will determine the relative effectiveness of individually-prescribed exercises (compared to standardized exercises) as well as the potential of ‘habit training’ resources (relative to generic behavior prompts) to improve compliance with exercises in this population. The study, conducted through a local clinical-community partnership will advance both the science and practice of community-based fall prevention programming, while also informing implementation strategies needed to promote broader dissemination. ClinicalTrials.gov, NCT05693025, Registered January 20, 2023, Updated March 1, 2023.

Placing an inertial measurement unit (IMU) at the 5th lumbar vertebra (L5) is a frequently employed method to assess the whole-body center of mass (CoM) motion during walking. However, such a fixed position approach does not account for instantaneous changes in body segment positions that change the CoM. Therefore, this study aimed to assess the congruence between CoM accelerations obtained from these two methods. The CoM positions were calculated based on trajectory data from 49 markers placed on bony landmarks, and its accelerations were computed using the finite-difference algorithm. Concurrently, accelerations were obtained with an IMU placed at L5, a proxy CoM position. Data were collected from 16 participants. Bland-Altman Limits of Agreement and Statistical Parametric Mapping approaches were used to examine the similarity and differences between accelerations directly obtained from the IMU and those derived from position data of the L5 marker (ML5) and whole-body CoM during a gait cycle. The correlation was moderate between IMU and CoM accelerations (r = 0.58) and was strong between IMU and ML5 or between CoM and ML5 accelerations (r = 0.76). There were significant differences in magnitudes between CoM and ML5 and between CoM and IMU accelerations along the anteroposterior and mediolateral directions during the early loading response, mid-stance, and terminal stance to pre-swing. Such comprehensive understanding of the similarity or discrepancy between CoM accelerations acquired by a single IMU and a camera- based motion capture system could further improve the development of wearable sensor technology for human movement analysis.

Background: Gait imbalance has been reported in overweight individuals and could further impair their mobility and quality of life. As the feet are the most distal part of the body and sensitively interface with external surroundings, evaluating the plantar pressure distribution can provide critical insights into their roles in regulating gait balance control. Therefore, the purpose of this study was to evaluate the effect of body weight and different gait speeds on the plantar pressure distribution and whole-body center of mass (COM) motion during walking. Methods: Eleven overweight individuals (OB) and 13 non-overweight individuals (NB) walked on a 10-meter walkway at three speed conditions (preferred, 80% and 120% of preferred speed). Gait balance was quantified by the mediolateral COM sway. Plantar pressure data were obtained using wireless pressure-sensing insoles that were inserted into a pair of running shoes. Analysis of variance models were used to examine the effect of body size, gait speeds, or their interactions on peak mediolateral COM and peak plantar pressure during walking. Results: Significant group effects of peak plantar pressure under the lateral forefoot (P = 0.03), lateral midfoot (P = 0.02), and medial heel (P = 0.02) were observed. However, the mediolateral COM motion and spatiotemporal gait parameters only revealed significant speed effects. Significance: Findings from this study indicated that overweight individuals exhibited increased plantar pressure under the lateral aspect of the foot, particularly during the late stance phase of walking, in an effort to maintain a comparable mediolateral COM motion to that of non-overweight individuals. Such elevated pressure in overweight individuals may potentially increase the risk of musculoskeletal pathology in the long term. The identified patterns are noteworthy as they have practical implications for designing targeted interventions and improving the overall health of individuals with a high BMI.

Objective: To assess the feasibility of using biomechanical gait balance measures, the frontal and sagittal plane center of mass (COM)-Ankle angles, to prospectively predict recurrent falls in community-dwelling older adults.Design: A cohort study with a one-year longitudinal follow-up. Logistic regression was used to test the ability of the COM-Ankle angles to predict prospective falls.Setting: General communityParticipants: Sixty older adults over the age of 70 years were recruited using a volunteer sample.Interventions: Not applicable.Main Outcome Measure(s): Biomechanical balance parameters: the sagittal and frontal plane COM-Ankle angles during the sit-to-walk and turning phases of the Timed Up and Go test. The COM-Ankle angles are the inclination angles of the line formed by the COM and lateral ankle (malleolus) marker of the stance foot in the sagittal and frontal planes. We also included the following clinical balance tests in the analysis: Activity-Specific Balance Confidence, Berg Balance Scale, Fullerton Advanced Balance scale, and Timed Up and Go test. Their abilities to predict falls served as a reference for the biomechanical balance parameters.Results: When the biomechanical gait balance measures were added to all the confounders, the explained variance was increased from 25.3% to 50.2%. Older adults who have a smaller sagittal plane COM-Ankle angle at seat-off, a greater frontal plane COM range of motion during STW and a smaller frontal plane angle during turning were more likely to become recurrent fallers.Conclusion(s): Our results indicated that dynamic biomechanical balance parameters could provide valuable information about a participant's future fall risks beyond what can be explained by demographics, cognition, depression, strength, and past fall history. Among all biomechanical parameters investigated, frontal plane COM motion measures during STW and turning appear to be the most significant predictors for future falls.

Inertial measurement units (IMUs) have proven to be valuable tools in measuring the range of motion (RoM) of human upper limb joints. Although several studies have reported on the validity of IMUs compared to the gold standard (optical motion capture system, OMC), a quantitative summary of the accuracy of IMUs in measuring RoM of upper limb joints is still lacking. Thus, the primary objective of this systematic review and meta-analysis was to determine the concurrent validity of IMUs for measuring RoM of the upper extremity in adults. Fifty-one articles were included in the systematic review, and data from 16 were pooled for meta-analysis. Concurrent validity is excellent for shoulder flexion-extension (Pearson's r = 0.969 [0.935, 0.986], ICC = 0.935 [0.749, 0.984], mean difference = -3.19 (p = 0.55)), elbow flexion-extension (Pearson's r = 0.954 [0.929, 0.970], ICC = 0.929 [0.814, 0.974], mean difference = 10.61 (p = 0.36)), wrist flexion-extension (Pearson's r = 0.974 [0.945, 0.988], mean difference = -4.20 (p = 0.58)), good to excellent for shoulder abduction-adduction (Pearson's r = 0.919 [0.848, 0.957], ICC = 0.840 [0.430, 0.963], mean difference = -7.10 (p = 0.50)), and elbow pronation-supination (Pearson's r = 0.966 [0.939, 0.981], ICC = 0.821 [0.696, 0.900]). There are some inconsistent results for shoulder internal-external rotation (Pearson's r = 0.939 [0.894, 0.965], mean difference = -9.13 (p < 0.0001)). In conclusion, the results support IMU as a viable instrument for measuring RoM of upper extremity, but for some specific joint movements, such as shoulder rotation and wrist ulnar-radial deviation, IMU measurements need to be used with caution.

IntroductionFalls present a significant public health concern in the United States as a primary cause of unintentional injury-related deaths among older adults. A fall risk assessment toolkit STEADI developed by the CDC has been shown to predict future falls. However, STEADI has issues with accurate evaluations due to the disagreement on cut-off scores in functional assessments and history-taking questionnaires. Wearable sensor technology offers a practical and quantifiable alternative for assessing an individual's movement performance in real-world environments. The use of Inertial Measurement Units (IMUs) offers considerable potential to enhance fall risk screening.PurposeThe primary aim of this study is to test the agreement of STEADI functional assessment performance measured by the IMUs in comparison to human-based measurements.Method27 participants (Age: 74.37 ± 7.21) performed STEADI, including the Four-Stage Balance Test (4SBT), Timed Up & Go Test (TUG), 30-second Chair Stand (30sCS) with IMU placed at the fifth lumbar vertebra which is the proxy location of whole-body Center of Mass. By adopting an equivalent test, the STEADI agreement was tested between the human rater and IMU measurements, giving α = 0.05.ResultBetween the results from evaluators and IMU, the difference in TUG is -0.23 seconds, and the difference in 30sCS is 0.37, which is equivalent to within 4% and 8% for TUG and 30sCS, respectively. The difference in single-leg stance during the 4SBT is 0.59s; however, the calculated equivalence zone is larger (22.7%).ConclusionThis study demonstrates the feasibility of using IMU sensors to enhance fall risk screening protocols based on the STEADI. Future refinement may still need to enable broader application and effective screening practices on a larger scale of the population.

Background: Persistent concussion symptoms (PCS) negatively affects common activities of daily living including deficits in both single and dual-task (DT) gait. DT gait deficits are present post-concussion; however, task prioritization and the effects of differing cognitive challenge remain unexplored in the PCS population. Research question: The purpose of this study was to investigate single and dual-task gait performance in individuals with persistent concussion symptoms and to identify task priorization strategies during DT trials. Methods: Fifteen adults with PCS (age: 43.9+11.7 y.o.) and 23 healthy control participants (age: 42.1+10.3 y.o.) completed five trials of single task gait followed by fifteen trials of dual task gait along a 10-m walkway. The cognitive challenges consisted of five trials each of visual stroop, verbal fluency, and working memory cognitive challenges. Groups were compared on DT cost stepping characteristics with independent samples t-test or MannWhitney U tests. Results: There were significant overall gait Dual Task Cost (DTC)difference between groups for gait speed (p = 0.009, d=0.92) and step length (p = 0.023, d=0.76). Specific to each DT challenge, PCS participants were slower during Verbal Fluency (0.98 + 0.15 m/s and 1.12 + 0.12 m/s, p = 0.008; d=1.03), Visual Stroop (1.06 + 0.19 m/ s and 1.20 + 0.12 m/s, p = 0.012, d=0.88), and Working Memory (1.02 + 0.15 m/s and 1.16 + 0.14 m/s, p = 0.006, d=0.96). There were significant cognitive DTC differences between groups for WM accuracy (p = 0.008, d=0.96), but not for VS accuracy (p = 0.841, d=0.061) or VF total words (p = 0.112, d=0.56). Significance: The PCS participants displayed a posture-second strategy whereby gait performance generally decreased in the absence of cognitive changes. However, during the Working Memory DT, PCS participants had a mutual interference response whereby both motor and cognitive performance decreased suggesting the cognitive task plays a key role in the DT gait performance of PCS patients.

Overweight or obesity is known to be associated with altered activations of lower extremity muscles. Such changes in muscular function may lead to the development of mobility impairments or joint diseases. However, little is known about how individual lower extremity muscles contribute to the whole-body center of mass (COM) control during walking and the effect of body weight. This study examined the contribution of individual lower extremity muscle force to the COM accelerations during walking in overweight and non-overweight individuals. Musculoskeletal simulations were performed for the stance phase of walking with data collected from 11 overweight and 13 non-overweight adults to estimate lower extremity muscle forces and their contributions to the COM acceleration. Mean time-series data from each parameter were compared between body size groups using Statistical Parametric Mapping. Compared to the non-overweight group, the overweight group revealed a greater gastrocnemius contribution to the mediolateral (p = 0.006) and vertical (p < 0.001) COM accelerations during mid-stance, and had a lower vastus contribution to the anteroposterior COM acceleration (p < 0.001) during pre-swing. Increased contributions from the large posterior calf muscles to the mediolateral COM ac-celeration may be related to efforts to alleviate COM sway in overweight individuals.

World Scientific Annual Review of BiomechanicsOnline Ready No AccessEditorial: Charting a Path Forward in Biomechanics Research DisseminationTung-Wu Lu, John K.-J. Li, and Li-Shan ChouTung-Wu LuDepartment of Biomedical Engineering, National Taiwan University, Taipei, Taiwan, R. O. China, John K.-J. LiDepartment of Biomedical Engineering, Rutgers — The State University of New Jersey, Piscataway, NJ, USA, and Li-Shan ChouDepartment of Kinesiology, Iowa State University, Ames, IA, USAhttps://doi.org/10.1142/S281095892301001XCited by:0 (Source: Crossref) Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail FiguresReferencesRelatedDetails Recommended Online Ready Metrics History Published: 20 November 2023 PDF download

Background: Declines in muscular function may hinder our ability to properly respond balance perturbations during walking. Examining age-related differences in muscle activation during balance-perturbed walking could be an important summary of literature to guide future clinical or scientific research. Research question: Are there differences in lower limb muscle activation between young and older adults when responding to balance perturbations during walking? Methods: A literature search was conducted in October 2020 to identify relevant articles using Pubmed, Scopus, Web of Science, Ovid EMBASE, and CINAHL. Inclusion criteria were defined to identify studies investigating lower limb muscle activation in healthy older adults during balance-perturbed walking. Data extraction was independently performed by both authors. Outcome measures included key findings of lower limb muscle activations during walking and balance-related tasks (e.g. multidirectional perturbations, different speeds, cognitive tasks, slippery/slopes, and obstacles). Results: This article reviewed fourteen studies including 230 older adults (age: 70 +/- 4.5, females: 124 [53.9%]) and 230 young adults (age: 23 +/- 2.0, females: 113 [49.1%]). The overall quality of included studies was fair, with a mean score of 76%. Twelve lower limb muscles were assessed during balance-perturbed walking. All studies reported electromyographic measurements, including magnitude, timing, co-contraction indices, and variability of activation. Significance: Compared to young adults, older adults demonstrated different adaptations in lower limb muscle activation during balance-perturbed walking. Co-contraction of ankle and knee joint muscles had more conclusive results, with the majority reporting an increased co-contraction in older adults, especially when balance is perturbed by a physical task. These data suggest that coordination between agonist and antagonist muscles is important to provide necessary stabilization during balance-perturbed walking.