Exploring single-line walking in immersive virtual reality

BackgroundGait disturbance is a major contributor to clinical disability in multiple sclerosis (MS). A sensor was developed to assess walking speed at home for people with MS using infrared technology in real-time without the use of wearables.ObjectiveTo develop continuous in-home outcome measures to assess gait in adults with MS.MethodsMovement measurements were collected continuously for 8 months from six people with MS. Average walking speed and peak walking speed were calculated from movement data, then analyzed for variability over time, by room (location), and over the course of the day. In-home continuous gait outcomes and variability were correlated with standard in-clinic gait outcomes.ResultsMeasured in-home average walking speed of participants ranged from 0.33 m/s to 0.96 m/s and peak walking speed ranged from 0.89 m/s to 1.51 m/s. Mean total within-participant coefficient of variation for daily average walking speed and peak walking speed were 10.75% and 10.93%, respectively. Average walking speed demonstrated a moderately strong correlation with baseline Timed 25-Foot Walk (rs = 0.714, P = 0.111).ConclusionNew non-wearable technology provides reliable and continuous in-home assessment of walking speed.

Objective To investigate gait characteristics in children with spastic cerebral palsy during inclined treadmill walking under a virtual reality environment. Methods Ten spastic cerebral palsy (CP) children and ten typically developing (TD) children were asked to walk at their comfortable speed on a treadmill at a ground level and 10° inclined. Three-dimensional kinematic data and ground reaction force data were captured in a computer-assisted rehabilitation environment system. Kinetic parameters and dynamic balance parameters were calculated using a standard biomechanical approach. Results During uphill walking, both groups decreased walking speed and stride length and increased peak pelvis tilt, ankle dorsiflexion, and hip flexion. Compared with TD children, CP children had decreased walking speed and stride length, decreased peak hip abduction moment, increased stance phase percentage, increased peak ankle dorsiflexion and knee flexion, and increased peak hip extension moment. The peak trunk rotation angle, ankle angle at initial contact, and stride length showed a significant group∗walking condition interaction effect. Conclusions CP children showed similar adjustments for most gait parameters during uphill walking as TD children. With a lower walking speed, CP children could maintain similar dynamic balance as TD children. Uphill walking magnifies the existing abnormal gait patterns of the cerebral palsy children. We suggest that during a treadmill training with an inclination, the walking speed should be carefully controlled in the case of improving peak joint loading too much.

Movement is a fundamental characteristic of life that has been defined as a change across many spatial and temporal scales. It is also a ubiquitous ecological process which influences the structure and dynamics of populations, communities, and ecosystems. Movement has always been at the center of many observations, investigations and studies since the very first attempt to understand where a species may go. Naturalists have investigated the mysteries of movement since the writings of Aristotle (4th century B.C.) searched for common features unifying animal movements. He tried to explain movement such as flight through air and motion of animals in water in general terms on the basis of casual observations. Since then, researchers have traditionally used direct observation as a method to monitor wildlife. The direct observation has also been used to elucidate and describe movement phenomena. Not losing sight of an animal is the most challenging part of this traditional type of research, but this has now been solved by deploying telemetry techniques radar, radio, satellite and Global Positioning System (GPS) tracking. Recent advances in telemetry techniques, such as extensive use of bio-loggers with GPS, have enabled spatiotemporal data to be collected on animals with ever-increasing accuracy. Also, recent advances in movement research have inspired a shift in the study at species-level or population-level patterns to individual-level patterns. This dissertation aims to contribute to the understanding of the movement phenomenon at individual-level pattern. The availability of movement data and recent advances in movement research are key factors to improve our understanding of animals' movement at individual levels. Although the techniques for studying animal movement have been advanced and flourished since earliest attempts, the incorporation of movement studies in other methodological advancements, particularly in species distribution modelling (SDM) has lagged behind. In the context of SDMs, the accessibility of habitats by species or populations has been considered rather than underlying the process of individuals' movement. Part of my dissertation is dedicated to providing evidence that including the individuals' movement of a species and accounting for their potential dispersal, along with environmental dynamics improves the accuracy and credibility of models to predict the potential distribution of species over time under climate change. Also, I illustrate the capacity of individuals’ movement data which can be considered as a reliable source for species distribution modelling. A common hypothesis is that when including the vertical movement of individuals improves the accuracy and credibility of the individual’s range maps. Another part of this dissertation provides evidence for this hypothesis and puts it to use in the utilization distribution model. I incorporated the vertical movement data to the 2D space use analysis and proved that the 3D volumetric analysis is a realistic depiction of species occurrence. To develop efficient nature conservation practices, it is necessary to know where and when wildlife may occur. This approach would give a better information to conservationists and wildlife managers while developing or practising conservation plans. A complex phenomenon like movement involving uncertainties may create confusion for managers. In this dissertation, I also presented the use of movement data to classify different types of movement (i.e. flight). I am confident that application of movement data in wildlife management and conservation practices will continue to expand and improve.

BackgroundChronic Obstructive Pulmonary Disease (COPD) currently ranks as the third leading cause of mortality worldwide, imposing substantial burdens on societal and individual health. Amongst health research tools, walking pace (WP) and hand grip strength (HGS) are cornerstones, extensively associated with diverse health conditions. However, the intricate interplay between these factors and COPD risk remains ambiguous. This study aims to elucidate the causal association of WP, HGS, with COPD risk through a bidirectional Mendelian randomization (MR) approach.MethodsBidirectional MR analysis was performed using Genome-wide association study (GWAS) data of European individuals for WP, HGS, and COPD. Inverse Variance Weighted (IVW) served as the primary MR analysis approach. To supplement the IVW findings, four additional MR methods [MR-Egger, weighted median, maximum likelihood, simple median] were used. To assess heterogeneity and pleiotropy, sensitivity analyses were performed. In addition, multivariate MR (MVMR) analysis was used to assess causality after adjustment for potential confounders.ResultsIVW method results show a significant negative association between WP and COPD risk in both initial (genome-wide threshold, odds ratio (OR) = 0.21, 95% confidence interval (CI) 0.09–0.51, P = 5.06 × 10− 4) and secondary (locus-wide threshold, OR = 0.27, 95%CI: 0.18–0.41, P = 4.88 × 10− 10) MR analysis. The reverse MR analysis suggested that COPD also diminishes WP. Additionally, a causal risk reduction for COPD with right HGS (OR = 0.74, 95% CI: 0.58–0.94, P = 1.44 × 10− 2) was only found in secondary MR analysis. The outcomes of the four additional MR methods also suggested similar causal relationships, and sensitivity analyses endorsed their robustness. Lastly, the MVMR analysis demonstrated that the WP’s effect on reducing COPD risk persisted independently of potential confounding variables.ConclusionA bidirectional causal relationship exists between typical WP and COPD risk. Conversely, a decrease in right HGS is unidirectionally associated with an increased risk of COPD. The study suggests that WP may serve as a predictive factor for COPD or as a simple evaluative indicator for prognosis.

Compared with elderly people who have not experienced falls, those who have were reported to have a shortened step length, large fluctuations in their pace, and a slow walking speed. The purpose of this study was to elucidate the step length required to maintain a walking speed of 1.0 m/s in patients aged 75 years or older. We measured the 10 m maximum walking speed in patients aged 75 years or older and divided them into the following two groups: Those who could walk 1.0 m/s or faster (fast group) and those who could not (slow group). Step length was determined from the number of steps taken during the 10 m-maximum walking speed test, and the step length-to-height ratio was calculated. Isometric knee extension muscle force (kgf), modified functional reach (cm), and one-leg standing time (s) were also measured. We included 261 patients (average age: 82.1 years, 50.6% men) in this study. The fast group included 119 participants, and the slow group included 142 participants. In a regression logistic analysis, knee extension muscle force (p = 0.03) and step length-to-height ratio (p < 0.01) were determined as factors significantly related to the fast group. As a result of ROC curve analysis, a step length-to-height ratio of 31.0% could discriminate between the two walking speed groups. The results suggest that the step length-to-height ratio required to maintain a walking speed of 1.0 m/s is 31.0% in patients aged 75 years or older.

Lower extremity powered exoskeletons (LEPE) are an emerging technology that assists people with lower-limb paralysis. LEPE for people with complete spinal cord injury walk at very slow speeds, below 0.5m/s. For the able-bodied population, very slow walking uses different neuromuscular, locomotor, postural, and dynamic balance control. Speed dependent kinetic and kinematic regression equations in the literature could be used for very slow walking LEPE trajectory scaling; however, kinematic and kinetic information at walking speeds below 0.5 m/s is lacking. Scaling LEPE trajectories using current reference equations may be inaccurate because these equations were produced from faster than real-world LEPE walking speeds. An improved understanding of how able-bodied people biomechanically adapt to very slow walking will provide LEPE developers with more accurate models to predict and scale LEPE gait trajectories. Full body motion capture data were collected from 30 healthy adults while walking on an instrumented self-paced treadmill, within a CAREN-Extended virtual reality environment. Kinematic and kinetic data were collected for 0.2 m/s—0.8 m/s, and self-selected walking speed. Thirty-three common sagittal kinematic and kinetic gait parameters were identified from motion capture data and inverse dynamics. Gait parameter relationships to walking speed, cadence, and stride length were determined with linear and quadratic (second and third order) regression. For parameters with a non-linear relationship with speed, cadence, or stride-length, linear regressions were used to determine if a consistent inflection occurred for faster and slower walking speeds. Group mean equations were applied to each participant’s data to determine the best performing equations for calculating important peak sagittal kinematic and kinetic gait parameters. Quadratic models based on walking speed had the strongest correlations with sagittal kinematic and kinetic gait parameters, with kinetic parameters having the better results. The lack of a consistent inflection point indicated that the kinematic and kinetic gait strategies did not change at very slow gait speeds. This research showed stronger associations with speed and gait parameters then previous studies, and provided more accurate regression equations for gait parameters at very slow walking speeds that can be used for LEPE joint trajectory development.

Introduction: Stroke commonly causes low aerobic fitness and walking speed. Objective: To compare the estimated aerobic fitness of the community and non-community elderly individuals after chronic stroke. Investigate the association between walking speed and aerobic fitness. Methods: Elderly people after stroke classified as community walkers or limited community walkers, according to their walking speed (10-m walk test), in m/s, were assessed with The Human Activity Profile (HAP). The Lifestyle Energy Consumption (LEC) score of the HAP was used to estimate aerobic fitness through the peak oxygen uptake (V̇o2peak). Results: Aerobic fitness of elderly individuals after stroke classified as community walkers was on average 17.3 (6.0) mL kg−1 min−1, while for non-community walkers, it was on average 10.2 (5.6) mL kg−1 min−1. There was a significant difference between groups (−7.05, 95% CI −10.98 to −3.12; P &lt; .001). A statistically significant (P &lt; .001) association with moderate magnitude (r = 0.587) was observed between the LEC and the habitual walking speed. Conclusions: Individuals with lower walking speed have lower aerobic fitness.

Appropriateness of gait analysis for biometrics: Initial study using FDA method

In this article, gait data of typically developing (TD) children (24 boys/31 girls, mean (95% confidence interval) age 9.38 (8.51 – 10.25) years, body mass 35.67 (31.40 – 39.94) kg, leg length 0.73 (0.70 – 0.76) m, and height 1.41 (1.35 – 1.46) m) walking at different walking speeds is shared publicly.Raw and processed data is presented for each child separately and includes data of each single step of both legs. Beside, the subject demographics and the results from the physical examination are presented allowing to select TD children from the database to create a matched group, based on specific parameters (e.g. sex and body weight).For clinical application, gait data is also presented per age group, which provides quick insight into the normal gait pattern of TD children of varying age.Gait analysis was performed during treadmill walking in a virtual environment using the Computer Assisted Rehabilitation Environment (CAREN). The human body lower limb model with trunk markers (HBM2) was used as biomechanical model. Children walked at comfortable walking speed, 30% slower and 30% faster (random sequence) while wearing gymnastic shoes and a safety harness to prevent falling. For each speed condition, 250 steps were recorded. Data quality check, step detection and the calculation of gait parameters was done by custom made Matlab algorithms.Raw data files are provided per walking speed, for each child separately. The raw data is exported from the CAREN software (D-flow) and is provided in .mox and .txt files. It includes the output from the models such as subject data, marker and force data, kinematic data (joint angles), kinetic data (joint moments, GRFs, joint powers), as well as CoM data and EMG data (the last two are not described in this manuscript), for each speed condition and each child. Unfiltered and filtered data are included. C3D files with raw marker and GRF data were recorded in Nexus (Vicon software) and are available upon request.After analyzing the raw data into Matlab (R2016a, Mathworks) using custom made Matlab algorithms, processed data is obtained. The processed data is provided in .xls files and is also presented for each child separately. It contains spatiotemporal parameters, 3D joint angles, anterior-posterior and vertical ground reaction forces (GRF), 3D joint moments and sagittal joint power of each step of the left and right leg.In addition to each individual's data, overview files (.xls) are created per walking speed condition. These overviews present the averaged gait parameter (e.g. joint angle), calculated over all valid steps, of each child.

This study aimed to verify whether working in cultivated land as a daily-life task contributes to the maintenance and improvement of physical and cognitive functions. The participants were 91 elderly people of ≥65 years of age who owned cultivated land in the mountainous Koyadaira district in Tokushima Prefecture. Sex, age, body mass index (BMI), walking speed as a physical function, and the mini-mental status examination (MMSE) score as a cognitive function were measured and analyzed in addition to the total working hours per week (WH) in cultivated land. The participants were 31 males and 60 females (mean age 78.5±6.6 years). The average values of the evaluated variables were as follows: WH, 18.0±13.2; BMI, 23.4±3.0 kg/m2; walking speed, 0.95±0.28 m/s; and MMSE score, 26.6±3.1 points. In addition, the Mann-Whitney U test and the Chi-square test showed no significant differences between sexes for each item. A logistic regression analysis showed that WH was significantly associated with MMSE (1, ≥28 points; 0, <28 points), and the odds ratio was 1.054 (p=0.010) in the model adjusted for age and BMI, while it was not significantly associated with walking speed (1, ≥1 m/s; 0, <1 m/s). Working on small-scale cultivated land was significantly associated with the cognitive function but not the physical function. Routine work on small-scale cultivated land as a daily-life task would contribute to the suppression of cognitive decline in older people living in hilly and mountainous areas.

Characteristics of personal space during obstacle circumvention in physical and virtual environments

Obesity is characterized by excessive body mass relative to height and is a predictor of knee osteoarthritis (OA). Despite the assumption that reduced body mass reduces knee joint loads and knee OA risk, these loads have not been identified in obese people or in formerly obese people after weight loss. PURPOSE: To identify the effects of weight loss on knee joint and muscle forces in obese adults. METHODS: Ground reaction forces and 3D kinematics were measured during walking prior to (baseline, BMI = 43.2) and 6 (BMI = 31.6) and 12 months (BMI = 28.6) after bariatric surgery in 10 adults. Musculoskeletal modeling was applied to these data to predict knee joint and muscle forces which were analyzed with 1-way ANOVA and post-hoc tests comparing baseline vs 6 months and 6 vs 12 months. All subjects gave written informed consent. RESULTS: Body mass decreased 27% from baseline to 6 months and 9% more at 12 months (126 vs 92 vs 84 kg, both p<0.05). Self-selected walking speed was statistically unchanged at 6 months (1.30 vs 1.35 m/s, N.S.) but increased 7% from 6 to 12 months (1.45 m/s, p<0.05). Maximum knee compressive force decreased 18% (2,943 vs 2,417, p<0.05) at 6 months then was statistically unchanged at 12 months (2,557 N). Maximum hamstrings (661 vs 550 N, p<0.05) and gastrocnemius (1,055 vs 805 N, p<0.05) forces were reduced at 6 months compared to baseline. Quadriceps force was statistically unchanged. CONCLUSIONS: Reduced body weight was the primary mechanism in changing knee joint compressive force. Indeed, knee force and body weight were correlated at r=0.82. Knee force most likely did not change from 6 to 12 months because increased walking speed probably counteracted the effect of reduced weight. Speed was correlated to knee force at r=0.49. Quadriceps force went unchanged despite weight loss most likely because step length (r=0.72 with quadriceps force) and knee flexion (r=0.44 with quadriceps force) both increased through the trial (data not shown) increasing quadriceps demand. Our data suggest 1) reduced weight led to reduced hamstrings and gastrocnemius forces which then led to reduced knee force and 2) reduced body weight may serve as a protective mechanism against knee OA incidence by reducing knee joint forces.

This paper reports on our efforts to collect daily COVID-19-related symptoms for a large public university population, as well as study relationship between reported symptoms and individual movements. We developed a set of tools to collect and integrate individual-level data. COVID-19-related symptoms are collected using a self-reporting tool initially implemented in Qualtrics survey system and consequently moved to .NET framework. Individual movement data are collected using off-the-shelf tracking apps available for iPhone and Android phones. Data integration and analysis are done in PostgreSQL, Python, and R. As of September 2020, we collected about 184,000 daily symptom responses for 20,000 individuals, as well as over 15,000 days of GPS movement data for 175 individuals. The analysis of the data indicates that headache is the most frequently reported symptom, present almost always when any other symptoms are reported as indicated by derived association rules. It is followed by cough, sore throat, and aches. The study participants traveled on average 223.61 km every week with a large standard deviation of 254.53 and visited on average 5.77 ± 4.75 locations each week for at least 10 min. However, there is no evidence that reported symptoms or prior COVID-19 contact affects movements (p > 0.3 in most models). The evidence suggests that although some individuals limit their movements during pandemics, the overall study population do not change their movements as suggested by guidelines.

To ensure that elderly members of society are able to maintain a quality of life that allows them to live independently, it is important that impending frailty, which occurs between the state of begin healthy and needing nursing care, be detectable at an early stage. The purpose of this study was to establish a system to continuously measure of elderly people as they go about their daily lives at home and to detect early signs of pre-frailty. This study focused on the decrease in walking speed of elderly people in pre-frailty, designing a means of measuring walking speed using a non-wearable passive infrared (PIR) sensor that performs equivalent accuracy with conventional manual measurement. The walking speed measured by this system with an average error of 1.7% at five meters from the walking speed measured by the gating method. Further testing confirmed that walking speed could be measured with an average error of 1.5%, even at one meter. The system is feasible, cost-effective, and can be easily installed in homes for continuous measurement of the walking speed of elderly people.

In order to study the movement characteristics of three rotational freedoms of pelvic motion during walking on the ground for middle-aged and elderly people, a mathematical model of pelvis motion was established according to the data measured. Pelvic motion parameters were studied by the Assessment of Biomechanics Functional (FAB) for 21 participants when walking at slow, normal, and fast speeds. In the experiment, multiple trajectories of the pelvis in three directions were measured, and the data were statistically analyzed with respect to the different conditions of height and walking speed. A mathematical model was established to fit the curve associated with walking speed and height. This mathematical model has practical significance for rehabilitation equipment and evaluation.