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Background: Achieving community ambulation is a common goal for many patients seeking to improve their quality of life. Rehabilitation professionals are tasked with preparing patients to meet the demands of their environment. In Singapore, rapid infrastructural development over the past two decades could alter these environmental challenges and affect functional community ambulation.

Objectives: This study aims to provide current recommendations of distance, step count and speed requirements for therapists to determine their patients’ suitability for community ambulation in Singapore, as well as to deepen understanding of current environmental barriers and enablers.

Methods: A quantitative surveillance data collection was conducted on 510 public housing blocks sampled across the country (North, North-East, Central, East and West sectors) to determine ambulation requirements for accessing five essential amenities of daily living (eatery, clinic, grocery, automated teller machine and public transport). Measurements of distance, step count and number of obstacles encountered en route to each amenity were collected. Walking speed was calculated by measuring the length and time allocated for traffic junction crossings.

Results: The average distance to amenities was found to be 294.3m, with 441 steps taken. Public transport was the nearest amenity (190.8m), while grocery stores were the furthest (382.7m). There was a significant variation in the distance to reach amenities (ranging from 10.0m to 1611.2m). The most common obstacles encountered were curbs and stairs. The average walking speed to cross traffic junctions safely was 0.74m/s.

Conclusion: This study aids rehabilitation professionals in better understanding Singapore’s urban landscape and planning realistic rehabilitation goals with their clients to achieve functional community ambulation.

Urban flooding events have emerged as increasingly prevalent and severe natural hazards, owing to the influence of climate change and human-induced activities. However, pedestrian locomotion in floodwater diverges significantly from level ambulation due to the complex interaction between pedestrians and water. To systematically investigate this mechanism of interaction, we propose the integration of a drag force into the social force model to simulate pedestrian movement behavior under floodwater conditions. The modified social force model was conducted for sensitivity parameters analysis and calibrated by three controllable experiments. Based on this calibrated model, an in-depth investigation has been conducted to analyze the influence of water depth and water flow velocity on pedestrians’ movement speed. Simulation results suggest that as water depth gradually incrementally rises, the drop rate of speed in running conditions is notably faster than that of walking conditions no matter what water flow speed and direction was. In addition, we propose a mathematical model capable of predicting pedestrians’ movement speed under floodwater conditions. These findings will offer valuable insights into the risk assessment of pedestrian evacuation in flooding scenarios.

In this research, we investigated the effect of changes in walking speed on variations of the complexity of electromyogram (EMG) signals recorded from the right and left legs of subjects. We specifically employed fractal theory and approximate entropy to analyze the changes in the complexity of EMG signals recorded from 13 subjects walked at 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, and 4.0 km/h on a flat surface. The results showed that by increasing of walking speed, the complexity of EMG signals decreases. The statistical analysis also indicated the significant effect of variations in walking speed on the variations of the complexity of EMG signals. This method analysis can be applied to other physiological signals of humans (e.g. electroencephalogram (EEG) signals) to investigate the effect of walking speed on other organs’ activations (e.g. brain).

Background: The cut-off values of walking velocity and classification of functional mobility both have a role in clinical settings for assessing the walking function of stroke patients and setting rehabilitation goals and treatment plans.

Objective: The present study investigated whether the cut-off values of the modified Rivermead Mobility Index (mRMI) and walking velocity accurately differentiated the walking ability of stroke patients according to the modified Functional Ambulation Category (mFAC).

Methods: Eighty two chronic stroke patients were included in the study. The comfortable/maximum walking velocities and mRMI were used to measure the mobility outcomes of these patients. To compare the walking velocities and mRMI scores for each mFAC point, one-way analysis of variance and the post-hoc test using Scheffe’s method were performed. The patients were categorized according to gait ability into either $\mathrm{\text{mFAC}}=\mathrm{\text{VII}}$ or mFAC$\le$VI group. The cut-off values for mRMI and walking velocities were calculated using a receiver-operating characteristic curve. The odds ratios of logistic regression analysis (Wald Forward) were analyzed to examine whether the cut-off values of walking velocity and mRMI can be utilized to differentiate functional walking levels.

Results: Except for mFACs III and IV, maximum walking velocity differed between mFAC IV and mFAC V $(p<0.01)$, between mFAC V and mFAC VI $(p<0.001)$, and between mFAC VI and mFAC VII $(p<0.05)$. The cut-off value of mRMI is $>26.5$ and the area under the curve is 0.87, respectively; the cut-off value for comfortable walking velocity is $>0.77$m/s and the area under the curve is 0.92, respectively; also, the cut-off value for maximum walking velocity is $>0.92$m/s and the area under the curve is 0.97, respectively. In the logistic regression analysis, the maximum walking velocity $(>0.92$m/s, $\mathrm{\text{OR}}=22.027)$ and mRMI $(>26.5$ scores, $\mathrm{\text{OR}}=10.283)$ are able to distinguish $\mathrm{\text{mFAC}}=\mathrm{\text{VII}}$ from mFAC$\le$VI.

Conclusion: The cut-off values of maximum walking velocity and mRMI are recommended as useful outcome measures for assessing ambulation levels in chronic stroke patients during rehabilitation.

The main objective of this paper is to evaluate experimentally the influence of different structural parameters on passive dynamic gaits. For this purpose a passive dynamic walker with knees and arc-shaped feet has been designed and built. Incremental encoders have been added on the hip and knee joints and force sensors have been installed not only in the sole of the feet but also the thighs of the prototype. Furthermore, each leg segment is also equipped with an inertial measurement unit. The acquired information is then used to determine how the changes in the ramp angle affect the step period, the step length and the walking speed. Conclusions could play an important role on the generation of gaits for actuated biped robots.

Coupled elastic actuation has been proven to be a simple and effective method to realize natural biped walking. However, former researches found it hard to achieve high speed merely by adjusting control parameters. For the purpose of realizing fast walking in this method, we put forward a simple approach: moving CoM(center of mass) forward. Through numerical simulation, we found that a positive lateral offset of CoM could effectively increase walking speed by enlarging step length and shortening step period, both of which result from the reduction of swing leg retraction. To testify the simulation results, experiments based on a prototype were conducted. The dimensionless speed of the robot changes from 0.264 to 0.424 as CoM moves forward, which confirms our speed-increasing strategy.