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Due to impaired mobility caused by aging, it is very important to employ early detection and monitoring of gait parameters to prevent the inevitable huge amount of medical cost at a later age. For gait training and potential tele-monitoring application outside clinical settings, low-cost yet highly reliable gait analysis systems are needed. This research proposes using a single LiDAR system to perform automatic gait analysis with polynomial fitting. The experimental setup for this study consists of two different walking speeds, fast walk and normal walk, along a 5-m straight line. There were ten test subjects (mean age 28, SD 5.2) who voluntarily participated in the study. We performed polynomial fitting to estimate the step length from the heel projection cloud point laser data as the subject walks forwards and compared the values with the visual inspection method. The results showed that the visual inspection method is accurate up to 6 cm while the polynomial method achieves 8 cm in the worst case (fast walking). With the accuracy difference estimated to be at most 2 cm, the polynomial method provides reliability of heel location estimation as compared with the observational gait analysis. The proposed method in this study presents an improvement accuracy of 4% as opposed to the proposed dual-laser range sensor method that reported 57.87 cm ± 10.48, an error of 10%. Meanwhile, our proposed method reported ±0.0633 m, a 6% error for normal walking.

Approximate entropy (ApEn) and sample entropy (SampEn) are mathematical algorithms created to measure the repeatability or predictability within a time series. Both algorithms are extremely sensitive to their input parameters: m (length of the data segment being compared), r (similarity criterion), and N (length of data). There is no established consensus on parameter selection in short data sets, especially for biological data. Therefore, the purpose of this research was to examine the robustness of these two entropy algorithms by exploring the effect of changing parameter values on short data sets. Data with known theoretical entropy qualities as well as experimental data from both healthy young and older adults was utilized. Our results demonstrate that both ApEn and SampEn are extremely sensitive to parameter choices, especially for very short data sets, N  ≤ 200. We suggest using N larger than 200, an m of 2 and examine several r values before selecting your parameters. Extreme caution should be used when choosing parameters for experimental studies with both algorithms. Based on our current findings, it appears that SampEn is more reliable for short data sets. SampEn was less sensitive to changes in data length and demonstrated fewer problems with relative consistency.

The approach run is a fundamental precondition for successful vault performance, as it enables the gymnast to develop maximum controlled horizontal velocity. The purpose of this study was to investigate the length, frequency, and velocity of steps during the run-up phase (approach run) in the execution of the handspring vault on the vaulting table. Nine high-level male artistic gymnasts, who performed the handspring vault under training conditions, volunteered to participate in the study. Five video cameras—four stationary and one scanning—were used to record the run-up phase, the hurdle step, and the take-off from the springboard. The gymnasts performed six trials of the handspring vault with a three-minute rest between each trial. Results showed that the final step was shorter than the penultimate step, and the penultimate step was longer than the preceding step. Additionally, the gymnasts demonstrated a gradual increase in their run-up velocity, a key requirement for a successful jump, up to the penultimate step. The average step frequency among gymnasts ranged from 3.20 to 4.88 steps per second, while the average step velocity across the six attempts was between 4.03 and 7.37 m/sec. Finally, a gradual increase in the gymnast’s velocity was observed up until the last step, with the final step being shorter than the penultimate step and the penultimate step being longer than the one before it. Zalet je temeljni predpogoj za uspešno izvedbo preskoka, saj omogoča telovadcu, da razvije največjo nadzorovano vodoravno hitrost. Namen te raziskave je bil ugotoviti dolžino, pogostost in hitrost korakov med zaletom (približevanjem mizi za preskok) pri izvedbi premeta naprej čez mizo za preskok. Devet vrhunskih orodnih telovadcev, ki so izvajali premet čez mizo za preskok med vadbo, se je prostovoljno udeležilo raziskave. S petimi videokamerami – štirimi nepremičnimi in eno premično – so posneli zalet, naskok in odriv z odrivne deske. Telovadci so izvedli šest poskusov premeta naprej čez mizo za preskok s triminutnim odmorom med vsakim poskusom. Rezultati so pokazali, da je bil zadnji korak krajši od predzadnjega, predzadnji pa daljši od predhodnega. Poleg tega so telovadci pokazali postopno povečevanje zaletne hitrosti, ki je ključni pogoj za uspešen skok, do predzadnjega koraka. Povprečna frekvenca korakov med telovadci je bila od 3,20 do 4,88 korakov na sekundo, medtem ko je bila povprečna hitrost korakov v šestih poskusih med 4,03 in 7,37 m/s. Končno je bilo opaženo postopno povečevanje telovadčeve hitrosti do zadnjega koraka, pri čemer je bil zadnji korak krajši od predzadnjega koraka in predzadnji korak daljši od tistega pred njim.

We propose a walking distance estimation method based on an adaptive step-length estimator at various walking speeds using a smartphone. First, we apply a fast Fourier transform (FFT)-based smoother on the acceleration data collected by the smartphone to remove the interference signals. Then, we analyze these data using a set of step-detection rules in order to detect walking steps. Using an adaptive estimator, which is based on a model of average step speed, we accurately obtain the walking step length. To evaluate the accuracy of the proposed method, we examine the distance estimation for four different distances and three speed levels. The experimental results show that the proposed method significantly outperforms conventional estimation methods in terms of accuracy.

People rarely walk in straight lines. Instead, we make frequent turns or other maneuvers. Spatiotemporal parameters fundamentally characterize gait. For straight walking, these parameters are well-defined for the task of walking on a straight path. Generalizing these concepts to non-straight walking, however, is not straightforward. People follow non-straight paths imposed by their environment (sidewalk, windy hiking trail, etc.) or choose readily-predictable, stereotypical paths of their own. People actively maintain lateral position to stay on their path and readily adapt their stepping when their path changes. We therefore propose a conceptually coherent convention that defines step lengths and widths relative to predefined walking paths. Our convention simply re-aligns lab-based coordinates to be tangent to a walker’s path at the mid-point between the two footsteps that define each step. We hypothesized this would yield results both more correct and more consistent with notions from straight walking. We defined several common non-straight walking tasks: single turns, lateral lane changes, walking on circular paths, and walking on arbitrary curvilinear paths. For each, we simulated idealized step sequences denoting “perfect” performance with known constant step lengths and widths. We compared results to path-independent alternatives. For each, we directly quantified accuracy relative to known true values. Results strongly confirmed our hypothesis. Our convention returned vastly smaller errors and introduced no artificial stepping asymmetries across all tasks. All results for our convention rationally generalized concepts from straight walking. Taking walking paths explicitly into account as important task goals themselves thus resolves conceptual ambiguities of prior approaches.

Background: Normal aging is characterized by functional changes in the sensory, neurological and musculoskeletal systems. These changes affect several motor tasks including postural balance and gait. Gait variability has been suggested to be an important predictor of the risk of falling: the age-related increased variability may result of errors in the control of foot placement and/or center of mass displacement. Falls occur most frequently in elderly populations who scored poorly during transfer of quasi-static to dynamic situations, turning and reaching tasks in clinical tests. This suggests that gait initiation, which is a transient phase between standing and walking, could contribute to an increase in variability because, for elderly, muscular synergies associated with gait initiation occur less frequently than for young adults. Objective: To examine if gait initiation and more particularly the variability of the first step length and the duration of the first double support period are more important for elderly fallers than for eldery non-fallers and young adults. Methods: Elderly fallers, elderly non-fallers, and young adults were asked to initiate gait and walk at least 3 strides. Spatio-temporal characteristics of the first step and following strides were collected and across-trials variability analysed. Results: Elderly fallers showed a much smaller first step length and a longer duration of the double support period. The first step length variability of elderly fallers was more than twice greater than that observed for elderly non-fallers. Conclusion: Considering the importance of proper initial foot placement for gait initiation and for stepping recovery responses, the first step length variability observed for the elderly fallers may be an important predictor of postural problems.

When discrepancies between planned and actual movements arise due to environmental changes, humans adjust movement parameters to achieve task goals. While motor adaptation has been extensively studied, the mechanisms involved in redundant movement parameters remain unclear. Split-belt treadmill adaptation, where each belt moves at a different speed, is an example of this phenomenon. Such adaptation initially induces gait asymmetry, which diminishes over time. Previous studies have postulated step length asymmetry as the target function; however, recent evidence challenges this assumption, leaving the target function undefined. This study investigates the target function by analyzing step parameter asymmetry using the goal-equivalent manifold and generalization predictability. The goal-equivalent manifold assesses whether adaptation is close to optimal in minimizing step parameter asymmetry, while generalization predictability reflects adaptation effects across different contexts, indicating potential target functions. We propose that step velocity asymmetry, rather than step length asymmetry, serves as the target function in split-belt treadmill adaptation. This framework facilitates the prediction and interpretation of both the learning process and the transfer of learning effects from trained to untrained conditions. In addition, it explains the overadaptation of step length asymmetry and the achievement of energy-efficient gait after adaptation. Therefore, we propose that step velocity asymmetry is the primary target function in split-belt treadmill adaptation.

The Fruit Fly Optimization Algorithm (FOA) is a recent algorithm inspired by the foraging behavior of fruit fly populations. However, the original FOA easily falls into the local optimum in the process of solving practical problems, and has a high probability of escaping from the optimal solution. In order to improve the global search capability and the quality of solutions, a dynamic step length mechanism, abandonment mechanism and Gaussian bare-bones mechanism are introduced into FOA, termed as BareFOA. Firstly, the random and ambiguous behavior of fruit flies during the olfactory phase is described using the abandonment mechanism. The search range of fruit fly populations is automatically adjusted using an update strategy with dynamic step length. As a result, the convergence speed and convergence accuracy of FOA have been greatly improved. Secondly, the Gaussian bare-bones mechanism that overcomes local optimal constraints is introduced, which greatly improves the global search capability of the FOA. Finally, 30 benchmark functions for CEC2017 and seven engineering optimization problems are experimented with and compared to the best-known solutions reported in the literature. The computational results show that the BareFOA not only significantly achieved the superior results on the benchmark problems than other competitive counterparts, but also can offer better results on the engineering optimization design problems.

The finite step length (FSL) method is extensively used for structural reliability analysis due to its robustness and efficiency compared with traditional Hasofer–Lind and Rackwitz–Fiessler (HL-RF) method. However, it may generate a large computational effort when it faces some complex nonlinear limit state functions. This study explains the basic reason of inefficiency of the FSL method and proposes an enhanced finite step length (EFSL) method to improve the ability for solving complex nonlinear problems, and then apply it to reliability-based design optimization (RBDO). The tactic is to present an iterative control criterion to compensate for the deficiency of the FSL method in the oscillation amplitude criterion, which solves the problem of large computational effort caused by unchanged step length during the iterative process. Then, a comprehensive step length adjustment formula is presented, which can adaptively adjust the step length to achieve fast convergence for limit state functions with different degrees of nonlinearity. Following that, the proposed method is combined with the double loop method (DLM) to improve the efficiency and robustness for solving complex RBDO problems. The robustness and efficiency of the proposed method compared to other commonly used first-order reliability analysis methods are demonstrated by five numerical examples. In addition, four design problems are used to validate the proposed EFSL-based DLM which is effective for solving complex nonlinear RBDO problems.

In this paper a novel step length model using a handheld Micro Electrical Mechanical System (MEMS) is presented. It combines the user’s step frequency and height with a set of three parameters for estimating step length. The model has been developed and trained using 12 different subjects: six men and six women. For reliable estimation of the step frequency with a handheld device, the frequency content of the handheld sensor’s signal is extracted by applying the Short Time Fourier Transform (STFT) independently from the step detection process. The relationship between step and hand frequencies is analyzed for different hand’s motions and sensor carrying modes. For this purpose, the frequency content of synchronized signals collected with two sensors placed in the hand and on the foot of a pedestrian has been extracted. Performance of the proposed step length model is assessed with several field tests involving 10 test subjects different from the above 12. The percentages of error over the travelled distance using universal parameters and a set of parameters calibrated for each subject are compared. The fitted solutions show an error between 2.5 and 5% of the travelled distance, which is comparable with that achieved by models proposed in the literature for body fixed sensors only.