Three decades of gait index development: A comparative review of clinical and research gait indices

Two sides of the same runner! The association between biomechanical and physiological markers of endurance performance in distance runners

BACKGROUND

The number of people who run to achieve competitive performance has increased, encouraging the scientific community to analyze the association of factors that can affect a runner performance.

RESEARCH QUESTION

Is there association between running spatiotemporal and angular kinematics with the physiological markers of endurance performance during a cardiorespiratory exercise test?

METHODS

This was an observational cross-sectional study with 40 distance runners simultaneously submitted to a running biomechanical analysis and cardiorespiratory exercise test on a treadmill. Mixed models were developed to verify the association between angular kinematic data obtained by the Movement Deviation Profile and the running spatiotemporal data with oxygen consumption and ventilatory thresholds.

RESULTS

Spatiotemporal variables [.e., step frequency Odds Ratio 0.09 [0.06-0.12 95 % Confidence Interval], center of mass vertical displacement Odds Ratio 0.10 [0.07-0.14 95 % Confidence Interval], and step length [Odds Ratio -0.01 [-0.01 to -0.00 95 % Confidence Interval]] were associated with VO2. Also, step frequency Odds Ratio 1.03 [1.01-1.05 95 % Confidence Interval] was associated with the first ventilatory threshold, and angular running kinematics [Movement Deviation Profile analysis] Odds Ratio 1.47 [1.13-1.91 95 % Confidence Interval] was associated with peak of exercise during the cardiorespiratory exercise test.

SIGNIFICANCE

Our findings demonstrated that: both higher step frequency and center of mass vertical displacement are associated with the increase of oxygen demand; step frequency is associated with the first ventilatory threshold, due to the entrainment mechanism and angular kinematic parameters are associated with peak aerobic speed. Future studies could also compare the biomechanical and physiological characteristics of different groups of distance runners. This could help identify the factors that contribute to oxygen demands during running and performance across different ages, genders, and levels of competition.

Position paper on how technology for human motion analysis and relevant clinical applications have evolved over the past decades: Striking a balance between accuracy and convenience

BACKGROUND

Over the past decades, tremendous technological advances have emerged in human motion analysis (HMA).

RESEARCH QUESTION

How has technology for analysing human motion evolved over the past decades, and what clinical applications has it enabled?

METHODS

The literature on HMA has been extensively reviewed, focusing on three main approaches: Fully-Instrumented Gait Analysis (FGA), Wearable Sensor Analysis (WSA), and Deep-Learning Video Analysis (DVA), considering both technical and clinical aspects.

RESULTS

FGA techniques relying on data collected using stereophotogrammetric systems, force plates, and electromyographic sensors have been dramatically improved providing highly accurate estimates of the biomechanics of motion. WSA techniques have been developed with the advances in data collection at home and in community settings. DVA techniques have emerged through artificial intelligence, which has marked the last decade. Some authors have considered WSA and DVA techniques as alternatives to "traditional" HMA techniques. They have suggested that WSA and DVA techniques are destined to replace FGA.

SIGNIFICANCE

We argue that FGA, WSA, and DVA complement each other and hence should be accounted as "synergistic" in the context of modern HMA and its clinical applications. We point out that DVA techniques are especially attractive as screening techniques, WSA methods enable data collection in the home and community for extensive periods of time, and FGA does maintain superior accuracy and should be the preferred technique when a complete and highly accurate biomechanical data is required. Accordingly, we envision that future clinical applications of HMA would favour screening patients using DVA in the outpatient setting. If deemed clinically appropriate, then WSA would be used to collect data in the home and community to derive relevant information. If accurate kinetic data is needed, then patients should be referred to specialized centres where an FGA system is available, together with medical imaging and thorough clinical assessments.

Intraoperative Angle Measurement of Anatomical Structures: A Systematic Review

Ensuring precise angle measurement during surgical correction of orientation-related deformities is crucial for optimal postoperative outcomes, yet there is a lack of an ideal commercial solution. Current measurement sensors and instrumentation have limitations that make their use context-specific, demanding a methodical evaluation of the field. A systematic review was carried out in March 2023. Studies reporting technologies and validation methods for intraoperative angular measurement of anatomical structures were analyzed. A total of 32 studies were included, 17 focused on image-based technologies (6 fluoroscopy, 4 camera-based tracking, and 7 CT-based), while 15 explored non-image-based technologies (6 manual instruments and 9 inertial sensor-based instruments). Image-based technologies offer better accuracy and 3D capabilities but pose challenges like additional equipment, increased radiation exposure, time, and cost. Non-image-based technologies are cost-effective but may be influenced by the surgeon's perception and require careful calibration. Nevertheless, the choice of the proper technology should take into consideration the influence of the expected error in the surgery, surgery type, and radiation dose limit. This comprehensive review serves as a valuable guide for surgeons seeking precise angle measurements intraoperatively. It not only explores the performance and application of existing technologies but also aids in the future development of innovative solutions.

Gait analysis using digital biomarkers including smart shoes in lumbar spinal canal stenosis: a scoping review

Lumbar spinal canal stenosis (LSS) is characterized by gait abnormalities, and objective quantitative gait analysis is useful for diagnosis and treatment. This review aimed to provide a review of objective quantitative gait analysis in LSS and note the current status and potential of smart shoes in diagnosing and treating LSS. The characteristics of gait deterioration in LSS include decreased gait velocity and asymmetry due to neuropathy (muscle weakness and pain) in the lower extremities. Previous laboratory objective and quantitative gait analyses mainly comprised marker-based three-dimensional motion analysis and ground reaction force. However, workforce, time, and costs pose some challenges. Recent developments in wearable sensor technology and markerless motion analysis systems have made gait analysis faster, easier, and less expensive outside the laboratory. Smart shoes can provide more accurate gait information than other wearable sensors. As only a few reports exist on gait disorders in patients with LSS, future studies should focus on the accuracy and cost-effectiveness of gait analysis using smart shoes.

The Movement Deviation Profile Can Differentiate Faller and Non-Faller Older Adults

BACKGROUND

The World Health Organization considers falls the second leading cause of death by accidental injury worldwide and one of the most frequent complications in older adults during activities of daily living. Several tasks related to fall risk have been individually assessed describing kinematic changes in older adults. The study proposal was to identify which functional task differentiates faller and non-faller older adults using the movement deviation profile (MDP).

METHODS

This cross-sectional study recruited 68 older adults aged ≥60 years by convenience sampling. Older adults were divided into 2 groups: with and without a history of falls (34 older adults in each group). The MDP analyzed the 3-dimensional angular kinematics data of tasks (ie, gait, walking turn, stair ascent and descent, sit-to-stand, and stand-to-sit), and the Z score of the mean MDP identified which task presented the greatest difference between fallers and non-fallers. A multivariate analysis with Bonferroni post hoc verified the interaction between groups considering angular kinematic data and the cycle time of the task. Statistical significance was set at 5% (p < .05).

RESULTS

Z score of the MDPmean showed an interaction between groups (λ = 0.67, F = 5.085, p < .0001). Fallers differed significantly from non-fallers in all tasks and the greatest difference was in stair descent (Z score = 0.89). The time to complete each task was not different between groups.

CONCLUSIONS

The MDP distinguished older adult fallers from non-fallers. The stair descent task should be highlighted because it presented the greatest difference between groups.

Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition

Stroke is a significant cause of disability worldwide, and stroke survivors often experience severe motor impairments. Lower limb rehabilitation exoskeleton robots provide support and balance for stroke survivors and assist them in performing rehabilitation training tasks, which can effectively improve their quality of life during the later stages of stroke recovery. Lower limb rehabilitation exoskeleton robots have become a hot topic in rehabilitation therapy research. This review introduces traditional rehabilitation assessment methods, explores the possibility of lower limb exoskeleton robots combining sensors and electrophysiological signals to assess stroke survivors' rehabilitation objectively, summarizes standard human-robot coupling models of lower limb rehabilitation exoskeleton robots in recent years, and critically introduces adaptive control models based on motion intent recognition for lower limb exoskeleton robots. This provides new design ideas for the future combination of lower limb rehabilitation exoskeleton robots with rehabilitation assessment, motion assistance, rehabilitation treatment, and adaptive control, making the rehabilitation assessment process more objective and addressing the shortage of rehabilitation therapists to some extent. Finally, the article discusses the current limitations of adaptive control of lower limb rehabilitation exoskeleton robots for stroke survivors and proposes new research directions.

How many observations in the reference dataset are required to compute a consistent Gait Deviation Index & Gait Profile Score?

BACKGROUND

The Gait Deviation Index (GDI) and the Gait Profile Score (GPS) are the most used scores to sum up gait deviations and are used as primary outcomes in many clinical studies. They are considered as equivalent scores. The computation of these scores is based on a reference dataset but often no description is provided. Among other characteristics, the number of observations needed and its possible influence on the computation of the scores remains unknown.

RESEARCH QUESTION

Define the number of observations needed in the reference dataset to compute consistent and reliable GDI and GPS.

METHODS

Fifty individuals with cerebral palsy (CP) were randomly selected from our laboratory database. Both scores were computed based on the reference dataset of Schwartz et al. (2008). A bootstrap analysis was performed, for every individual, to assess the effect of the number of observations on both scores. N number of observations were randomly selected, with replacement, from the reference dataset. This procedure was repeated 2000 times for every individual and every N and performed from N = 5 to N = 165 with an increment of 5. The 95 % of the absolute error distribution was considered for every individual and every N. The smallest detectable change (SDC) for both scores was considered as a threshold (GDI: 10.8; GPS:1.3°) to determine the minimum N required.

RESULTS AND SIGNIFICANCE

A minimum of 90 and 20 observations are required to compute consistent GDI and GPS, respectively. The number of observations has a higher impact on the GDI than the GPS, mainly because the GPS calculation does not rely on the standard deviation (SD). Furthermore, the GDI absolute error seems to be higher in individuals with greater gait deviations, i.e. lower GDI value. This effect was not observed on the GPS. In the case of a small reference dataset, the GPS should therefore be preferred.