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Spatiotemporal parameters can characterize the gait patterns of individuals, allowing assessment of their health status and detection of clinically meaningful changes in their gait. Video-based markerless motion capture is a user-friendly, inexpensive, and widely applicable technology that could reduce the barriers to measuring spatiotemporal gait parameters in clinical and more diverse settings. Two studies were performed to determine whether gait parameters measured using markerless motion capture demonstrate concurrent validity with those measured using marker-based motion capture and a pressure-sensitive gait mat. For the first study, thirty healthy young adults performed treadmill gait at self-selected speeds while marker-based motion capture and synchronized video data were recorded simultaneously. For the second study, twenty-five healthy young adults performed over-ground gait at self-selected speeds while footfalls were recorded using a gait mat and synchronized video data were recorded simultaneously. Kinematic heel-strike and toe-off gait events were used to identify the same gait cycles between systems. Nine spatiotemporal gait parameters were measured by each system and directly compared between systems. Measurements were compared using Bland-Altman methods, mean differences, Pearson correlation coefficients, and intraclass correlation coefficients. The results indicate that markerless measurements of spatiotemporal gait parameters have good to excellent agreement with marker-based motion capture and gait mat systems, except for stance time and double limb support time relative to both systems and stride width relative to the gait mat. These findings indicate that markerless motion capture can adequately measure spatiotemporal gait parameters of healthy young adults during treadmill and over-ground gait.

Motor function impairments have the greatest impact on independence in daily life and the working capacity of patients after a stroke [1]. To identify the structure of motor impairments in paresis and, accordingly, to enable precise, targeted selection of rehabilitation methods after stroke, objective diagnostics of the quantitative and qualitative characteristics of the altered movement pattern are necessary [2]. As walking is the primary locomotor act in humans, gait patterns are specifically analyzed in the diagnosis of motor disorders. Aim of this study was the analyses of spatiotemporal and kinematic parameters of the movement pattern during the early recovery period of ischemic stroke (IS) with lesion localization in the middle cerebral artery (MCA) territory and the vertebrobasilar system (VBS) [3, 4]. A total of 30 patients were examined (17 women, 13 men; mean age 60.6 ± 6.45 years) at 4–6 weeks post-IS. Fifteen patients had ischemic lesions in the MCA, and fifteen in the VBS. The total score on the FIM scale was 118 ± 5.2 / 121.8 ± 4.5, on the Modified Ashworth Scale — 0.5 ± 0.6 / 0.3 ± 0.7, proximal leg paresis — 4 ± 0.4 / 4.3 ± 0.5 points, distal leg paresis — 3.9 ± 0.4 / 4.1 ± 1.0 points, respectively. Spatiotemporal (gait cycle, stance phase, swing phase, step length, speed, step width, and step frequency) and kinematic (pelvic motion in three planes during walking) parameters of the movement pattern were studied in a 3D video analysis laboratory equipped with the high-resolution SMART-DX Motion Analysis System, Serial SDX–0313–0071 (Italy), for analyzing all types of movement according to the international Davis protocol. In patients with mild paresis after IS, the structure of the gait cycle (percentage distribution of stance and swing phases) was preserved, but the spatiotemporal and kinematic movement parameters deviated from normal in both groups. Patients with ischemic lesions in the MCA were characterized by excessive pelvic elevation in the frontal plane on the paretic side, with insufficient pelvic drop on the healthy side during walking. In patients with IS in the VBS, the leading feature was a pathological forward shift of the pelvis in the sagittal plane during gait. No differences between the groups were found in the changes of spatiotemporal parameters. Importantly, in cases of mild paresis, alterations in the movement pattern developed on the healthy side simultaneously with those on the paretic side from the first month after stroke onset. In conclusion., patients with IS in the MCA and VBS territories with mild paresis and absence of spasticity are similar in their alterations of spatiotemporal gait parameters but show statistically significant differences in the kinematic characteristics of pelvic motion during walking. The findings highlight the importance of differentiating rehabilitation strategies based on whether the stroke is localized in the MCA or VBS.

Three-dimensional (3D) kinematic analysis of gait holds potential as a digital biomarker to identify neuropathologies, monitor disease progression, and provide a high-resolution outcome measure to monitor neurorehabilitation efficacy by characterizing the mechanisms underlying gait impairments. There is a need for 3D motion capture technologies accessible to community, clinical, and rehabilitation settings. Image-based markerless motion capture (MLMC) using neural network-based deep learning algorithms shows promise as an accessible technology in these settings. In this study, we assessed the feasibility of implementing 3D MLMC technology outside the traditional laboratory environment to evaluate its potential as a tool for outcomes assessment in neurorehabilitation. A sample population of 166 individuals aged 9 to 87 years (mean 43.7, S.D. 20.4) of varied health history were evaluated at six different locations in the community over a three-month period. Participants walked overground at self-selected (SS) and fastest comfortable (FC) speeds. Feasibility measures considered the expansion, implementation, and practicality of this MLMC system. A subset of the sample population (46 individuals) walked over a pressure-sensitive walkway (PSW) concurrently with MLMC to assess agreement of the spatiotemporal gait parameters measured between the two systems. Twelve spatiotemporal parameters were compared using mean differences, Bland-Altman analysis, and intraclass correlation coefficients for agreement (ICC2,1) and consistency (ICC3,1). All measures showed good to excellent agreement between MLMC and the PSW system with cadence, speed, step length, step time, stride length and stride time showing strong similarity. Furthermore, this information can inform the development of rehabilitation strategies targeting gait dysfunction. These first experiments provide evidence for feasibility of using MLMC in community and clinical practice environments to acquire robust 3D kinematic data from a diverse population. This foundational work enables future investigation with MLMC especially its use as a digital biomarker of disease progression and rehabilitation outcome.

Wearable inertial devices have recently been used to evaluate spatiotemporal parameters of gait in daily life situations. Given the heterogeneity of gait patterns in children with cerebral palsy (CP), the sensor placement and analysis algorithm may influence the validity of the results. This study aimed at comparing the spatiotemporal measurement performances of three wearable configurations defined by different sensor positioning on the lower limbs: (1) shanks and thighs, (2) shanks, and (3) feet. The three configurations were selected based on their potential to be used in daily life for children with CP and typically developing (TD) controls. For each configuration, dedicated gait analysis algorithms were used to detect gait events and compute spatiotemporal parameters. Fifteen children with CP and 11 TD controls were included. Accuracy, precision, and agreement of the three configurations were determined in comparison with an optoelectronic system as a reference. The three configurations were comparable for the evaluation of TD children and children with a low level of disability (CP-GMFCS I) whereas the shank-and-thigh-based configuration was more robust regarding children with a higher level of disability (CP-GMFCS II–III).

Arm swinging is a typical feature of human walking: Continuous and rhythmic movement of the upper limbs is important to ensure postural stability and walking efficiency. However, several factors can interfere with arm swings, making walking more risky and unstable: These include aging, neurological diseases, hemiplegia, and other comorbidities that affect motor control and coordination. Objective assessment of arm swings during walking could play a role in preventing adverse consequences, allowing appropriate treatments and rehabilitation protocols to be activated for recovery and improvement. This paper presents a system for gait analysis based on Microsoft Azure Kinect DK sensor and its body-tracking algorithm: It allows noninvasive full-body tracking, thus enabling simultaneous analysis of different aspects of walking, including arm swing characteristics. Sixteen subjects with Parkinson’s disease and 13 healthy controls were recruited with the aim of evaluating differences in arm swing features and correlating them with traditional gait parameters. Preliminary results show significant differences between the two groups and a strong correlation between the parameters. The study thus highlights the ability of the proposed system to quantify arm swing features, thus offering a simple tool to provide a more comprehensive gait assessment.

Spatiotemporal gait parameters are commonly used to quantify physical functioning including in populations with atypical foot strike patterns. The current gold standard measurement system for gait analysis is marker-based motion capture and floor-mounted force plates, but it can be expensive and cumbersome to set up. Pressure sensitive walkways are more affordable, quicker to set up, and more portable. Currently available walkways have not been compared to marker-based motion capture when measuring atypical foot strike patterns. The recently developed Tekscan® Strideway pressure sensitive walkway system has not been compared to any gold standard. This study compared measurements of step width, step length, and step time from the Strideway™ system against a gold standard marker-based motion capture (Vicon® Vantage™) and floor-mounted force plate (AMTI®) system. Ten typically ambulating adults, free of injury, emulated five different foot strike patterns recording two-hundred footsteps for each. Results indicate that the Strideway™ compares well to the gold standard especially under typical foot strike patterns. The errors were highest for step width and near-zero for step time. However, the user needs to be aware that errors can substantially increase with certain foot strike patterns such as when the heel does not make contact with the walkway. The results of this study will help inform users about potential limitations when using a pressure sensitive walkway like the Strideway™ for testing populations with typical and atypical strike patterns.

This study aims to measure and compare spatiotemporal gait parameters in nineteen subjects using a full wearable inertial mocap system Xsens (MVN Awinda, Netherlands) and a photoelectronic system one-meter OptoGaitTM (Microgait, Italy) on a treadmill imposing a walking speed of 5 km/h. A total of eleven steps were considered for each subject constituting a dataset of 209 samples from which spatiotemporal parameters (SPT) were calculated. The step length measurement was determined using two methods. The first one considers the calculation of step length based on the inverted pendulum model, while the second considers an anthropometric approach that correlates the stature with an anthropometric coefficient. Although the absolute agreement and consistency were found for the calculation of the stance phase, cadence and gait cycle, from our study, differences in SPT were found between the two systems. Mean square error (MSE) calculation of their speed (m/s) with respect to the imposed speed on a treadmill reveals a smaller error (MSE = 0.0008) using the OptoGaitTM. Overall, our results indicate that the accurate detection of heel strike and toe-off have an influence on phases and sub-phases for the entire acquisition. Future study in this domain should investigate how to design and integrate better products and algorithms aiming to solve the problematic issues already identified in this study without limiting the user’s need and performance in a different environment.

Split-belt treadmill walking has contributed to our understanding of human locomotor adaptation and motor learning. On the split-belt treadmill, each limb walks on a belt moving at a different speed. An obvious question is: how fast is a person walking on a split-belt treadmill? This question has implications for metabolic and mechanical energy consumption, stability, and neuromotor adaptation. There are two interpretations of this question that provide different answers and implications. First, how fast a person is walking can be interpreted as the center of mass velocity, which is a vector of the rate of displacement in the three directions of an inertial reference frame. The center of mass velocity is zero on average in a global reference frame and oscillates throughout the gait cycle, providing information about mechanical and stability demands. Second, how fast a person is walking can be interpreted as stride speed, which is a scalar defined as stride length divided by stride time. Stride speed is constrained by the split-belt treadmill to the average belt speed, subtracted by the interaction between belt speed difference and step time asymmetry. Thus, stride speed is slower than the average speed of the belts due to modifications in step time asymmetry. Here, we provide a framework for calculating and contextualizing these two quantities, highlighting their implications for recent work in split-belt adaptation. We suggest that the design and interpretation of future split-belt adaptation studies can benefit from careful consideration of the analyses presented here.

Background: A single lumbar-mounted inertial sensor offers a practical alternative to optoelectronic systems for gait analysis, simplifying measurements and improving usability in the clinical field. However, its validity can be influenced by sensor placement and signal choice. This study aimed to develop and validate a novel algorithm for estimating spatiotemporal parameters using anteroposterior linear acceleration and angular velocity around the sagittal axis using a single inertial measurement unit (IMU) placed on the lumbar region. The proposed algorithm was validated comparing the parameters computed by the algorithm with the ones computed using a commercial wearable system based on a two-foot-mounted IMU configuration. Thirty healthy subjects underwent a 2 min walk test, and five spatiotemporal parameters were computed using the two methodologies. Study results showed that cadence and gait cycle time exhibited very high agreement, with only a small, statistically significant bias in cadence negligible for practical purposes. In contrast, swing, stance, and double-support parameters showed disagreement due to the presence of systematic proportional errors. This work introduces a novel algorithm for gait event detection and spatiotemporal parameter estimation, addressing uncertainties related to sensor placement, metric models, processing techniques, and signal selection, while avoiding synchronization issues associated with using multiple sensors.

Freezing of Gait (FOG) is a prevalent and debilitating symptom in idiopathic Parkinson's disease (PD). This study evaluated spatiotemporal and kinematic gait parameters in individuals with PD with a history of FOG and explored the effects of dopaminergic therapy on FOG subtypes. One hundred and nine individuals with PD underwent clinical assessments and quantitative biomechanical measures during walking cycles before and after dopaminergic therapy. Individuals with FOG were classified into levodopa-responsive and levodopa-unresponsive groups. Individuals with FOG displayed longer disease duration and higher Unified Parkinson's Disease Rating Scale (UPDRS) II, III, IV scores, and total scores and levodopa equivalent dose, than those without FOG (all  < 0.0001). Following propensity score matching of 15 pairs based on UPDRS total score and disease duration during the off-medication state, the analysis comparing the FOG and non-FOG groups revealed no significant differences in spatiotemporal and kinematic parameters. In 39 cases of FOG, dopaminergic therapy improved gait performance in individuals with PD, enhancing spatiotemporal parameters (speed, stride length, step length, step variability) and kinematic parameters (shoulder and elbow flexion/extension range of motion (ROM), pelvic rotation, and hip abduction/adduction ROM) regardless of FOG responsiveness to dopaminergic therapy. A significant difference in trunk sway ROM (  = 0.029) remained before and after dopaminergic therapy, even after adjusting for disease duration and clinical severity. Dopaminergic therapy had varying effects on PD with FOG, improving several spatiotemporal and kinematic gait parameters but being less effective in levodopa-unresponsive cases. Quantitative biomechanical measures offer detailed insights into gait performance, aiding personalized fall risk assessment and guiding individualized rehabilitation programs.