Ambient floor vibration sensing advances the accessibility of functional gait assessments for children with muscular dystrophies.

Muscular dystrophies (MD) are a group of genetic neuromuscular disorders that cause progressive weakness and loss of muscles over time, influencing 1 in 3500-5000 children worldwide. New and exciting treatment options have led to a critical need for a clinical post-marketing surveillance tool to confirm the efficacy and safety of these treatments after individuals receive them in a commercial setting. For MDs, functional gait assessment is a common approach to evaluate the efficacy of the treatments because muscle weakness is reflected in individuals' walking patterns. However, there is little incentive for the family to continue to travel for such assessments due to the lack of access to specialty centers. While various existing sensing devices, such as cameras, force plates, and wearables can assess gait at home, they are limited by privacy concerns, area of coverage, and discomfort in carrying devices, which is not practical for long-term, continuous monitoring in daily settings. In this study, we introduce a novel functional gait assessment system using ambient floor vibrations, which is non-invasive and scalable, requiring only low-cost and sparsely deployed geophone sensors attached to the floor surface, suitable for in-home usage. Our system captures floor vibrations generated by footsteps from patients while they walk around and analyzes such vibrations to extract essential gait health information. To enhance interpretability and reliability under various sensing scenarios, we translate the signal patterns of floor vibration to pathological gait patterns related to MD, and develop a hierarchical learning algorithm that aggregates insights from individual footsteps to estimate a person's overall gait performance. When evaluated through real-world experiments with 36 subjects (including 15 patients with MD), our floor vibration sensing system achieves a 94.8% accuracy in predicting functional gait stages for patients with MD. Our approach enables accurate, accessible, and scalable functional gait assessment, bringing MD progressive tracking into real life.

Effects of robot-assisted gait training using the Welwalk on gait independence for individuals with hemiparetic stroke: an assessor-blinded, multicenter randomized controlled trial.

BACKGROUND: Gait disorder remains a major challenge for individuals with stroke, affecting their quality of life and increasing the risk of secondary complications. Robot-assisted gait training (RAGT) has emerged as a promising approach for improving gait independence in individuals with stroke. This study aimed to evaluate the effect of RAGT in individuals with subacute hemiparetic stroke using a one-leg assisted gait robot called Welwalk WW-1000. METHODS: An assessor-blinded, multicenter randomized controlled trial was conducted in the convalescent rehabilitation wards of eight hospitals in Japan. Participants with first-ever hemiparetic stroke who could not walk at pre-intervention assessment were randomized to either the Welwalk group, which underwent RAGT with conventional physical therapy, or the control group, which underwent conventional physical therapy alone. Both groups received 80 min of physical therapy per day, 7 days per week, while the Welwalk group received 40 min of RAGT per day, 6 days per week, as part of their physical therapy. The primary outcome was gait independence, as assessed using the Functional Independence Measure Walk Score. RESULTS: A total of 91 participants were enrolled, 85 of whom completed the intervention. As a result, 91 participants, as a full analysis set, and 85, as a per-protocol set, were analyzed. The primary outcome, the cumulative incidence of gait-independent events, was not significantly different between the groups. Subgroup analysis revealed that the interaction between the intervention group and stroke type did not yield significant differences in either the full analysis or per-protocol set. However, although not statistically significant, a discernible trend toward improvement with Welwalk was observed in cases of cerebral infarction for the full analysis and per-protocol sets (HR 4.167 [95%CI 0.914-18.995], p = 0.065, HR 4.443 [95%CI 0.973-20.279], p = 0.054, respectively). CONCLUSIONS: The combination of RAGT using Welwalk and conventional physical therapy was not significantly more effective than conventional physical therapy alone in promoting gait independence in individuals with subacute hemiparetic stroke, although a trend toward earlier gait independence was observed in individuals with cerebral infarction. TRIAL REGISTRATION: This study was registered with the Japan Registry of Clinical Trials ( https://jrct.niph.go.jp ; jRCT 042180078) on March 3, 2019.

Stroke walking and balance characteristics via principal component analysis.

Balance impairment is associated gait dysfunction with several quantitative spatiotemporal gait parameters in patients with stroke. However, the link between balance impairments and joint kinematics during walking remains unclear. Clinical assessments and gait measurements using motion analysis system was conducted in 44 stroke patients. This study utilised principal component analysis to identify key joint kinematics characteristics of patients with stroke during walking using average joint angles of pelvis and bilateral lower limbs in every gait-cycle percentile related to balance impairments. Reconstructed kinematics showed the differences in joint kinematics in both paretic and nonparetic lower limbs that can be distinguished by balance impairment, particularly in the sagittal planes during swing phase. The impaired balance group exhibited greater joint variability in both the paretic and nonparetic limbs in the sagittal plane during entire gait phase and during terminal swing phase respectively compared with those with high balance scores. This study provides a more comprehensive understanding of stroke hemiparesis gait patterns and suggests considering both nonparetic and paretic limb function, as well as bilateral coordination in clinical practice. Principal component analysis can be a useful assessment tool to distinguish differences in balance impairment and dynamic symmetry during gait in patients with stroke.

Characteristic Changes of the Stance-Phase Plantar Pressure Curve When Walking Uphill and Downhill: Cross-Sectional Study.

BACKGROUND: Monitoring of gait patterns by insoles is popular to study behavior and activity in the daily life of people and throughout the rehabilitation process of patients. Live data analyses may improve personalized prevention and treatment regimens, as well as rehabilitation. The M-shaped plantar pressure curve during the stance phase is mainly defined by the loading and unloading slope, 2 maxima, 1 minimum, as well as the force during defined periods. When monitoring gait continuously, walking uphill or downhill could affect this curve in characteristic ways. OBJECTIVE: For walking on a slope, typical changes in the stance phase curve measured by insoles were hypothesized. METHODS: In total, 40 healthy participants of both sexes were fitted with individually calibrated insoles with 16 pressure sensors each and a recording frequency of 100 Hz. Participants walked on a treadmill at 4 km/h for 1 minute in each of the following slopes: -20%, -15%, -10%, -5%, 0%, 5%, 10%, 15%, and 20%. Raw data were exported for analyses. A custom-developed data platform was used for data processing and parameter calculation, including step detection, data transformation, and normalization for time by natural cubic spline interpolation and force (proportion of body weight). To identify the time-axis positions of the desired maxima and minimum among the available extremum candidates in each step, a Gaussian filter was applied (σ=3, kernel size 7). Inconclusive extremum candidates were further processed by screening for time plausibility, maximum or minimum pool filtering, and monotony. Several parameters that describe the curve trajectory were computed for each step. The normal distribution of data was tested by the Kolmogorov-Smirnov and Shapiro-Wilk tests. RESULTS: Data were normally distributed. An analysis of variance with the gait parameters as dependent and slope as independent variables revealed significant changes related to the slope for the following parameters of the stance phase curve: the mean force during loading and unloading, the 2 maxima and the minimum, as well as the loading and unloading slope (all P<.001). A simultaneous increase in the loading slope, the first maximum and the mean loading force combined with a decrease in the mean unloading force, the second maximum, and the unloading slope is characteristic for downhill walking. The opposite represents uphill walking. The minimum had its peak at horizontal walking and values dropped when walking uphill and downhill alike. It is therefore not a suitable parameter to distinguish between uphill and downhill walking. CONCLUSIONS: While patient-related factors, such as anthropometrics, injury, or disease shape the stance phase curve on a longer-term scale, walking on slopes leads to temporary and characteristic short-term changes in the curve trajectory.

Finite element analysis of kangaroo astragali: A new angle on the ankle.

Using finite element analysis on the astragali of five macropodine kangaroos (extant and extinct hoppers) and three sthenurine kangaroos (extinct proposed bipedal striders) we investigate how the stresses experienced by the ankle in similarly sized kangaroos of different hypothesized/known locomotor strategy compare under different simulation scenarios, intended to represent the moment of midstance at different gaits. These tests showed a clear difference between the performance of sthenurines and macropodines with the former group experiencing lower stress in simulated bipedal strides in all species compared with hopping simulations, supporting the hypothesis that sthenurines may have utilized this gait. The Pleistocene macropodine Protemnodon also performed differently from all other species studied, showing high stresses in all simulations except for bounding. This may support the hypothesis of Protemnodon being a quadrupedal bounder.

A comprehensive dataset on biomechanics and motor control during human walking with discrete mechanical perturbations.

Background: Humans have a remarkable capability to maintain balance while walking. There is, however, a lack of publicly available research data on reactive responses to destabilizing perturbations during gait. Methods: Here, we share a comprehensive dataset collected from 10 participants who experienced random perturbations while walking on an instrumented treadmill. Each participant performed six 5-min walking trials at a rate of 1.2 m/s, during which rapid belt speed perturbations could occur during the participant's stance phase. Each gait cycle had a 17% probability of being perturbed. The perturbations consisted of an increase of belt speed by 0.75 m/s, delivered with equal probability at 10%, 20%, 30%, 40%, 50%, 60%, 70%, or 80% of the stance phase. Data were recorded using motion capture with 25 markers, eight inertial measurement units (IMUs), and electromyography (EMG) from the tibialis anterior (TA), soleus (SOL), lateral gastrocnemius (LG), rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), biceps femoris (BF), and gluteus maximus (GM). The full protocol is described in detail. Results: We provide marker trajectories, force plate data, EMG data, and belt speed information for all trials and participants. IMU data is provided for most participants. This data can be useful for identifying neural feedback control in human gait, biologically inspired control systems for robots, and the development of clinical applications.

Exoskeleton-based exercises for overground gait and balance rehabilitation in spinal cord injury: a systematic review of dose and dosage parameters.

BACKGROUND: Exoskeletons are increasingly applied during overground gait and balance rehabilitation following neurological impairment, although optimal parameters for specific indications are yet to be established. OBJECTIVE: This systematic review aimed to identify dose and dosage of exoskeleton-based therapy protocols for overground locomotor training in spinal cord injury/disease. METHODS: A systematic review was conducted in accordance with the Preferred Reporting Items Systematic Reviews and Meta-Analyses guidelines. A literature search was performed using the CINAHL Complete, Embase, Emcare Nursing, Medline ALL, and Web of Science databases. Studies in adults with subacute and/or chronic spinal cord injury/disease were included if they reported (1) dose (e.g., single session duration and total number of sessions) and dosage (e.g., frequency of sessions/week and total duration of intervention) parameters, and (2) at least one gait and/or balance outcome measure. RESULTS: Of 2,108 studies identified, after removing duplicates and filtering for inclusion, 19 were selected and dose, dosage and efficacy were abstracted. Data revealed a great heterogeneity in dose, dosage, and indications, with overall recommendation of 60-min sessions delivered 3 times a week, for 9 weeks in 27 sessions. Specific protocols were also identified for functional restoration (60-min, 3 times a week, for 8 weeks/24 sessions) and cardiorespiratory rehabilitation (60-min, 3 times a week, for 12 weeks/36 sessions). CONCLUSION: This review provides evidence-based best practice recommendations for overground exoskeleton training among individuals with spinal cord injury/disease based on individual therapeutic goals - functional restoration or cardiorespiratory rehabilitation. There is a need for structured exoskeleton clinical translation studies based on standardized methods and common therapeutic outcomes.

Effects of walking speeds on lower extremity kinematic synergy in toe vertical position control: An experimental study.

BACKGROUND: This study aimed to investigate whether lower limb joints mutually compensate for each other, resulting in motor synergy that suppresses toe vertical position fluctuation, and whether walking speeds affect lower limb synergy. METHODS: Seventeen male university students walked at slow (0.85 ±â€…0.04 m/s), medium (1.43 ±â€…0.05 m/s) and fast (1.99 ±â€…0.06 m/s) speeds on a 15-m walkway while lower limb kinematic data were collected. Uncontrolled manifold analysis was used to quantify the strength of synergy. Two-way (speed × phase) repeated-measures analysis of variance was used to analyze all dependent variables. RESULTS: A significant speed-by-phase interaction was observed in the synergy index (SI) (P  < .001). At slow walking speeds, subjects had greater SI during mid-swing (P  < .001), while at fast walking speeds, they had greater SI during early-swing (P  < .001). During the entire swing phase, fast walking exhibited lower SI values than medium (P  = .005) and slow walking (P  = .027). CONCLUSION: Kinematic synergy plays a crucial role in controlling toe vertical position during the swing phase, and fast walking exhibits less synergy than medium and slow walking. These findings contribute to a better understanding of the role of kinematic synergy in gait stability and have implications for the development of interventions aimed at improving gait stability and reducing the risk of falls.

Gait classification for growing children with Duchenne muscular dystrophy.

Classifying gait patterns into homogeneous groups could enhance communication among healthcare providers, clinical decision making and clinical trial designs in boys with Duchenne muscular dystrophy (DMD). Sutherland's classification has been developed 40 years ago. Ever since, the state-of-the-art medical care has improved and boys with DMD are now longer ambulatory. Therefore, the gait classification requires an update. The overall aim was to develop an up-to-date, valid DMD gait classification. A total of 137 three-dimensional gait analysis sessions were collected in 30 boys with DMD, aged 4.6-17 years. Three classes were distinguished, which only partly aligned with increasing severity of gait deviations. Apart from the mildly affected pattern, two more severely affected gait patterns were found, namely the tiptoeing pattern and the flexion pattern with distinct anterior pelvic tilt and posterior trunk leaning, which showed most severe deviations at the ankle or at the proximal segments/joints, respectively. The agreement between Sutherland's and the current classification was low, suggesting that gait pathology with the current state-of-the-art medical care has changed. However, overlap between classes, especially between the two more affected classes, highlights the complexity of the continuous gait changes. Therefore, caution is required when classifying individual boys with DMD into classes.

Effect of different constraining boundary conditions on simulated femoral stresses and strains during gait.

Finite element analysis (FEA) is commonly used in orthopaedic research to estimate localised tissue stresses and strains. A variety of boundary conditions have been proposed for isolated femur analysis, but it remains unclear how these assumed constraints influence FEA predictions of bone biomechanics. This study compared the femoral head deflection (FHD), stresses, and strains elicited under four commonly used boundary conditions (fixed knee, mid-shaft constraint, springs, and isostatic methods) and benchmarked these mechanics against the gold standard inertia relief method for normal and pathological femurs (extreme anteversion and retroversion, coxa vara, and coxa valga). Simulations were performed for the stance phase of walking with the applied femoral loading determined from patient-specific neuromusculoskeletal models. Due to unrealistic biomechanics observed for the commonly used boundary conditions, we propose a novel biomechanical constraint method to generate physiological femur biomechanics. The biomechanical method yielded FHD (< 1 mm), strains (approaching 1000 µÎµ), and stresses (< 60 MPa), which were consistent with physiological observations and similar to predictions from the inertia relief method (average coefficient of determination = 0.97, average normalized root mean square error = 0.17). Our results highlight the superior performance of the biomechanical method compared to current methods of constraint for  both healthy and pathological femurs.

The Impact of Dual-Tasks and Disease Severity on Posture, Gait, and Functional Mobility among People Living with Dementia in Residential Care Facilities: A Pilot Study.

Gait speed and timed-up-and-go (TUG) predict cognitive decline, falls, and mortality. Dual-tasks may be useful in cognitive screening among people living with dementia (PWD), but more evidence is needed. This cross-sectional study aimed to compare single- and dual-task performance and determine the influence of dementia severity on dual-task performance and interference. Thirty PWD in two residential care facilities (Age: 81.3 ± 7.1 years; Montreal Cognitive Assessment: 10.4 ± 6.0 points) completed two trials of single- (feet apart) and dual-task posture (feet apart while counting backward), single- (walk 4 m) and dual-task gait (walk 4m while naming words), and single- (timed-up-and-go (TUG)), and dual-task functional mobility (TUG while completing a category task) with APDM inertial sensors. Dual-tasks resulted in greater sway frequency, jerk, and sway area; slower gait speed; greater double limb support; shorter stride length; reduced mid-swing elevation; longer TUG duration; reduced turn angle; and slower turn velocity than single-tasks (ps < 0.05). Dual-task performance was impacted (reduced double limb support, greater mid-swing elevation), and dual-task interference (greater jerk, faster gait speed) was related to moderate-to-severe compared to mild PWD. Moderate-to-severe PWD had poorer dynamic stability and a reduced ability to appropriately select a cautious gait during dual-tasks than those with mild PWD, indicating the usefulness of dual-tasks for cognitive screening.

Brain Networks Modulation during Simple and Complex Gait: A "Mobile Brain/Body Imaging" Study.

Walking encompasses a complex interplay of neuromuscular coordination and cognitive processes. Disruptions in gait can impact personal independence and quality of life, especially among the elderly and neurodegenerative patients. While traditional biomechanical analyses and neuroimaging techniques have contributed to understanding gait control, they often lack the temporal resolution needed for rapid neural dynamics. This study employs a mobile brain/body imaging (MoBI) platform with high-density electroencephalography (hd-EEG) to explore event-related desynchronization and synchronization (ERD/ERS) during overground walking. Simultaneous to hdEEG, we recorded gait spatiotemporal parameters. Participants were asked to walk under usual walking and dual-task walking conditions. For data analysis, we extracted ERD/ERS in α, ß, and γ bands from 17 selected regions of interest encompassing not only the sensorimotor cerebral network but also the cognitive and affective networks. A correlation analysis was performed between gait parameters and ERD/ERS intensities in different networks in the different phases of gait. Results showed that ERD/ERS modulations across gait phases in the α and ß bands extended beyond the sensorimotor network, over the cognitive and limbic networks, and were more prominent in all networks during dual tasks with respect to usual walking. Correlation analyses showed that a stronger α ERS in the initial double-support phases correlates with shorter step length, emphasizing the role of attention in motor control. Additionally, ß ERD/ERS in affective and cognitive networks during dual-task walking correlated with dual-task gait performance, suggesting compensatory mechanisms in complex tasks. This study advances our understanding of neural dynamics during overground walking, emphasizing the multidimensional nature of gait control involving cognitive and affective networks.

Are Gait Patterns during In-Lab Running Representative of Gait Patterns during Real-World Training? An Experimental Study.

Biomechanical assessments of running typically take place inside motion capture laboratories. However, it is unclear whether data from these in-lab gait assessments are representative of gait during real-world running. This study sought to test how well real-world gait patterns are represented by in-lab gait data in two cohorts of runners equipped with consumer-grade wearable sensors measuring speed, step length, vertical oscillation, stance time, and leg stiffness. Cohort 1 (N = 49) completed an in-lab treadmill run plus five real-world runs of self-selected distances on self-selected courses. Cohort 2 (N = 19) completed a 2.4 km outdoor run on a known course plus five real-world runs of self-selected distances on self-selected courses. The degree to which in-lab gait reflected real-world gait was quantified using univariate overlap and multivariate depth overlap statistics, both for all real-world running and for real-world running on flat, straight segments only. When comparing in-lab and real-world data from the same subject, univariate overlap ranged from 65.7% (leg stiffness) to 95.2% (speed). When considering all gait metrics together, only 32.5% of real-world data were well-represented by in-lab data from the same subject. Pooling in-lab gait data across multiple subjects led to greater distributional overlap between in-lab and real-world data (depth overlap 89.3-90.3%) due to the broader variability in gait seen across (as opposed to within) subjects. Stratifying real-world running to only include flat, straight segments did not meaningfully increase the overlap between in-lab and real-world running (changes of <1%). Individual gait patterns during real-world running, as characterized by consumer-grade wearable sensors, are not well-represented by the same runner's in-lab data. Researchers and clinicians should consider "borrowing" information from a pool of many runners to predict individual gait behavior when using biomechanical data to make clinical or sports performance decisions.

Pedestrian Pose Recognition Based on Frequency-Modulated Continuous-Wave Radar with Meta-Learning.

With the continuous advancement of autonomous driving and monitoring technologies, there is increasing attention on non-intrusive target monitoring and recognition. This paper proposes an ArcFace SE-attention model-agnostic meta-learning approach (AS-MAML) by integrating attention mechanisms into residual networks for pedestrian gait recognition using frequency-modulated continuous-wave (FMCW) millimeter-wave radar through meta-learning. We enhance the feature extraction capability of the base network using channel attention mechanisms and integrate the additive angular margin loss function (ArcFace loss) into the inner loop of MAML to constrain inner loop optimization and improve radar discrimination. Then, this network is used to classify small-sample micro-Doppler images obtained from millimeter-wave radar as the data source for pose recognition. Experimental tests were conducted on pose estimation and image classification tasks. The results demonstrate significant detection and recognition performance, with an accuracy of 94.5%, accompanied by a 95% confidence interval. Additionally, on the open-source dataset DIAT-µRadHAR, which is specially processed to increase classification difficulty, the network achieves a classification accuracy of 85.9%.

Gait Pattern Analysis: Integration of a Highly Sensitive Flexible Pressure Sensor on a Wireless Instrumented Insole.

Gait phase monitoring wearable sensors play a crucial role in assessing both health and athletic performance, offering valuable insights into an individual's gait pattern. In this study, we introduced a simple and cost-effective capacitive gait sensor manufacturing approach, utilizing a micropatterned polydimethylsiloxane dielectric layer placed between screen-printed silver electrodes. The sensor demonstrated inherent stretchability and durability, even when the electrode was bent at a 45-degree angle, it maintained an electrode resistance of approximately 3 Ω. This feature is particularly advantageous for gait monitoring applications. Furthermore, the fabricated flexible capacitive pressure sensor exhibited higher sensitivity and linearity at both low and high pressure and displayed very good stability. Notably, the sensors demonstrated rapid response and recovery times for both under low and high pressure. To further explore the capabilities of these new sensors, they were successfully tested as insole-type pressure sensors for real-time gait signal monitoring. The sensors displayed a well-balanced combination of sensitivity and response time, making them well-suited for gait analysis. Beyond gait analysis, the proposed sensor holds the potential for a wide range of applications within biomedical, sports, and commercial systems where soft and conformable sensors are preferred.

Back geometry and mobility function changes in cerebral palsy children after backward walking training: arandomized controlled trial.

AIM: To compare the effects of backward (BW) and forward (FW) walking training on back geometry and mobility function in children with hemiparetic cerebral palsy (CP). METHODS: Fifty-five children with hemiparetic CP participated in this study. They were randomly assigned into two groups. For 12 weeks, both groups got a conventional physical therapy program three days/week. Groups A and B got a specifically developed FW walking training (25 minutes/session) and a specially designed BW walking training (25 minutes/session), respectively. RESULTS: The trunk imbalance, lateral deviation, pelvic tilting, pelvic torsion, surface motion, and dynamic gait index of group B improved significantly more than group A (p < .05). Both groups showed significant improvements in all measured variables (p < .05). CONCLUSION: BW walking training might be considered as an effective therapy modality for improving back geometry and mobility function in hemiparetic CP children compared with FW walking training combined with a typical program.

Developing excellent plantar pressure sensors for monitoring human motions by using highly compressible and resilient PMMA conductive iongels.

Based on real-time detection of plantar pressure, gait recognition could provide important health information for rehabilitation administration, fatigue prevention, and sports training assessment. So far, such researches are extremely limited due to lacking of reliable, stable and comfortable plantar pressure sensors. Herein, a strategy for preparing high compression strength and resilience conductive iongels has been proposed by implanting physically entangled polymer chains with covalently cross-linked networks. The resulting iongels have excellent mechanical properties including nice compliance (young's modulus < 300 kPa), high compression strength (>10 MPa at a strain of 90 %), and good resilience (self-recovery within seconds). And capacitive pressure sensor composed by them possesses excellent sensitivity, good linear response even under very small stress (∼kPa), and long-term durability (cycles > 100,000) under high-stress conditions (133 kPa). Then, capacitive pressure sensor arrays have been prepared for high-precision detection of plantar pressure spatial distribution, which also exhibit excellent sensing performances and long-term stability. Further, an extremely sensitive and fast response plantar pressure monitoring system has been designed for monitoring plantar pressure of foot at different postures including upright, forward and backward. The system achieves real-time tracking and monitoring of changes of plantar pressure during different static and dynamic posture processes. And the characteristics of plantar pressure information can be digitally and photography displayed. Finally, we propose an intelligent framework for real-time detection of plantar pressure by combining electronic insoles with data analysis system, which presents excellent applications in sport trainings and safety precautions.

SensAA-Design and Verification of a Cloud-Based Wearable Biomechanical Data Acquisition System.

Exoskeletons designed to assist patients with activities of daily living are becoming increasingly popular, but still are subject to research. In order to gather requirements for the design of such systems, long-term gait observation of the patients over the course of multiple days in an environment of daily living are required. In this paper a wearable all-in-one data acquisition system for collecting and storing biomechanical data in everyday life is proposed. The system is designed to be cost efficient and easy to use, using off-the-shelf components and a cloud server system for centralized data storage. The measurement accuracy of the system was verified, by measuring the angle of the human knee joint at walking speeds between 3 and 12 km/h in reference to an optical motion analysis system. The acquired data were uploaded to a cloud database via a smartphone application. Verification results showed that the proposed toolchain works as desired. The system reached an RMSE from 2.9° to 8°, which is below that of most comparable systems. The system provides a powerful, scalable platform for collecting and processing biomechanical data, which can help to automize the generation of an extensive database for human kinematics.

The Analytical Validity of Stride Detection and Gait Parameters Reconstruction Using the Ankle-Mounted Inertial Measurement Unit Syde®.

The increasing use of inertial measurement units (IMU) in biomedical sciences brings new possibilities for clinical research. The aim of this paper is to demonstrate the accuracy of the IMU-based wearable Syde® device, which allows day-long and remote continuous gait recording in comparison to a reference motion capture system. Twelve healthy subjects (age: 23.17 ± 2.04, height: 174.17 ± 6.46 cm) participated in a controlled environment data collection and performed a series of gait tasks with both systems attached to each ankle. A total of 2820 strides were analyzed. The results show a median absolute stride length error of 1.86 cm between the IMU-based wearable device reconstruction and the motion capture ground truth, with the 75th percentile at 3.24 cm. The median absolute stride horizontal velocity error was 1.56 cm/s, with the 75th percentile at 2.63 cm/s. With a measurement error to the reference system of less than 3 cm, we conclude that there is a valid physical recovery of stride length and horizontal velocity from data collected with the IMU-based wearable Syde® device.

Dirt Track Surface Preparation and Associated Differences in Speed, Stride Length, and Stride Frequency in Galloping Horses.

In racehorses, the risk of musculoskeletal injury is linked to a decrease in speed and stride length (SL) over consecutive races prior to injury. Surface characteristics influence stride parameters. We hypothesized that large changes in stride parameters are found during galloping in response to dirt racetrack preparation. Harrowing of the back stretch of a half-mile dirt racetrack was altered in three individual lanes with decreasing depth from the inside to the outside. Track underlay compaction and water content were changed between days. Twelve horses (six on day 2) were sequentially galloped at a target speed of 16 ms-1 across the three lanes. Speed, stride frequency (SF), and SL were quantified with a GPS/GNSS logger. Mixed linear models with speed as covariate analyzed SF and SL, with track hardness and moisture content as fixed factors (p < 0.05). At the average speed of 16.48 ms-1, hardness (both p < 0.001) and moisture content (both p < 0.001) had significant effects on SF and SL. The largest difference in SL of 0.186 m between hardness and moisture conditions exceeded the 0.10 m longitudinal decrease over consecutive race starts previously identified as injury predictor. This suggests that detailed measurements of track conditions might be useful for refining injury prediction models.