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Despite evidence suggesting that excess weight is linked to gait alterations and foot disorders, its effect on peak plantar pressure (PPP) variability and complexity during walking remains poorly understood.This study aimed to examine the influence of overweight (BMI ≥ 25) on the dynamic PPP distribution during gait using traditional and nonlinear dynamic measures in young college students.Fifty-two overweight (BMI25, average 29.3 ± 4.02) and sixty-four control college students (BMI25, 21.7 ± 1.76) aged 18-25 years, walked across a Tekscan gait assessment system at their preferred speed. A t-test or a Mann Whitney U test was used for analysis, subject to data normality. Kinematic, kinetic, spatiotemporal, and GaitEn (sample entropy of 2D spatial PPP maps) for window lengths (m=2) at various filtering levels (r) were used to explore the impact of BMI on PPP alterations.The overweight group exhibited significantly higher mean PPP. The PPP under the forefoot region was also significantly higher for the overweight group as compared to the heel. The mean GaitEn values of overweight and control groups were found significantly different at r = (0.7-0.8) x STD, where GaitEn of the control group was relatively higher, which indicates better gait performance as compared to the overweight group in alignment with previous studies. A significant correlation of GaitEn with STD of PPP was revealed for the overweight group only, suggesting that overweight could significantly change the regularity or the complexity of the PPP series. Although no spatiotemporal parameters (stride length, step length, step width) were significantly affected by the increased BMI, GaitEn dynamic measure, along with spatiotemporal (decrease in gait velocity and cadence with increased BMI), and kinetic measures (increased maximum forces and plantar pressure with increased BMI), were significantly affected by overweight, indicating the feasibility of assessing the impact of increased BMI using pressure platforms in clinical settings.

Background: Gait dysfunction or impairment is considered one of the most common and devastating physiological consequences of stroke, and achieving optimal gait is a key goal for stroke victims with gait disability along with their clinical teams. Many researchers have explored post stroke gait, including assessment tools and techniques, key gait parameters and significance on functional recovery, as well as data mining, modeling and analyses methods.Research Question: This study aimed to review and summarize research efforts applicable to quantification and analyses of post-stroke gait with focus on recent technology-driven gait characterization and analysis approaches, including the integration of smart low cost wearables and Artificial Intelligence (AI), as well as feasibility and potential value in clinical settings.Methods: A comprehensive literature search was conducted within Google Scholar, PubMed, and ScienceDirect using a set of keywords, including lower extremity, walking, post-stroke, and kinematics. Original articles that met the selection criteria were included.Results and Significance: This scoping review aimed to shed light on tools and technologies employed in post stroke gait assessment toward bridging the existing gap between the research and clinical communities. Conventional qualitative gait analysis, typically used in clinics is mainly based on observational gait and is hence subjective and largely impacted by the observer's experience. Quantitative gait analysis, however, provides measured parameters, with good accuracy and repeatability for the diagnosis and comparative assessment throughout rehabilitation. Rapidly emerging smart wearable technology and AI, including Machine Learning, Support Vector Machine, and Neural Network approaches, are increasingly commanding greater attention in gait research. Although their use in clinical settings are not yet well leveraged, these tools promise a paradigm shift in stroke gait quantification, as they provide means for acquiring, storing and analyzing multifactorial complex gait data, while capturing its non-linear dynamic variability and offering the invaluable benefits of predictive analytics.

Precise finishing operations such as chamfering and filleting are characterized by relatively low contact forces and low material removal. For such processes, conventional automation approaches like pre-programmed position or force control without adaptations are not suitable to obtain fine surface finishing with high profile accuracy. As a result, polishing tasks are still mainly carried out manually by skilled operators. In this paper, we propose an adaptive framework capable of polishing a wide range of materials including hard metals like titanium using a collaborative robot. We propose an iterative learning controller based on impedance control that adapts both position and forces simultaneously in each iteration to regulate the polishing process. The proposed controller can track the desired profile without any a priori knowledge of the forces required to polish different materials. In addition, we introduce a novel mathematical model to generate the complex filleting toolpath based on Lissajous curves. Trials are carried out in finishing tasks such as chamfering and filleting using a collaborative industrial robot to validate the novel framework. Surface roughness and profile measurements show that our adaptive controller can obtain fine polishing output in various materials such as titanium, aluminum, and wood. Ministry of Education (MOE) National Research Foundation (NRF) This project was conducted within the Rolls-Royce@NTU Corporate Lab with support from the National Research Foundation (NRF), Singapore under the Corp Lab@University Scheme. This grant was partly supported by the MOE Tier1 grant (RG48/17).

Surface finishing, as the final stage in the manufacturing pipeline, is a key process in determining the quality and life span of a product. Such a task is characterized by low contact forces and minimal material removal from the object surface. Despite the advancements in machine learning and artificial intelligence, human workforce is still irreplaceable in performing such tasks due to superior dexterity and adaptability, but this is often prone to risks such as hand-arm vibration syndrome due to hand-held tools. Therefore, we propose a collaborative approach to assist the human in carrying out such tasks with the help of two case studies: Human–Robot-Collaborative edge chamfering and polishing tasks, based on an impedance controlled collaborative curve tracing technique. We propose a collaborative framework, where the robot assists an operator to guide the end-effector/tool along a pre-defined parametric curve. The algorithm is demonstrated in two scenarios. In the first case, we address a collaborative chamfering task whereas the second case focuses on a polishing application (for straight edges). For these kinds of tasks, the curve to be traced assumes the shape of a straight line along the edge. We make use of the compliant feature of a cobot, which allows the user to physically guide the robot in the task space, to generate a mathematical model for the tool path. From the end-user perspective, this is more intuitive than the classical programming-based path planning approaches. In the process of machining, to enhance the path tracking accuracy and to ensure constant tool-surface contact, we implement guidance virtual fixtures through impedance control. As a result, the machining error is reduced. Nanyang Technological University National Research Foundation (NRF) This project was conducted within the Rolls-Royce@NTU Corporate Lab with support from the National Research Foundation (NRF) Singapore under the Corp Lab@University Scheme.

In this paper, we devise a robot-to-sensor calibration technique for a drawing robot working simultaneously with a low-cost 3D sensor. The framework allows the robot to create a calibration object and exploits the geometrical and the texture properties of its 3D mesh representation to identify the transformation between the robot and the sensor coordinates systems. The calibration is followed by experimental analysis validating the approach. The proposed technique is a simple and efficient way to calibrate the robot-vision system in the context of drawing/writing tasks.

Human Robot Collaboration (HRC) has shown great promise across a wide range of disciplines. The role of robots often ranges from being an assistant to even a co-worker. This paper discusses a collaborative framework, whereby a robot assists the human in achieving smooth tool pose transitions during the course of a tooling task. The human operator manually teaches the robot a set of orientations at specific key locations on the workpiece, prior to the commencement of the task. Subsequently, a spatial field of virtual frames is generated through Inverse Distance Weighted interpolation, which in turn governs the tool orientation at each instance of robot motion. Afterward, a guided curve tracing task on a three dimensional surface is performed to analyze the effectiveness of the approach.

Human robot collaboration (HRC) is a cogent need in many industrial applications nowadays. This paper proposes a framework specifically targeting curve-following tasks whereby a compliant robot, in impedance-control mode, can be manually guided by an operator. Preliminary experiments are described to test the performance of approach. In particular, data relative to curve following in the case of vertical tool positioner versus vertical tool positioner plus impedance-control mode are performed under manual guidance, showing better performance in the former case.

Human robot collaboration (HRC) is a hot research topic nowadays and achieves great success and development from the laboratory to the daily life service and industrial applications. This paper briefly introduces the background of HRC and its application in industries as well as technical characteristics. Finally, an intelligent industrial work assistant (iiwa) robot is setup as a HRC experiment platform to perform collaborative tasks. The demo gives an outlook that human and robot working alongside is viable and successful.

The purpose of the study is to examine the effect of subliminal priming in terms of the perception of images influenced by words with positive, negative, and neutral emotional content, through electroencephalograms (EEGs). Participants were instructed to rate how much they like the stimuli images, on a 7-point Likert scale, after being subliminally exposed to masked lexical prime words that exhibit positive, negative, and neutral connotations with respect to the images. Simultaneously, the EEGs were recorded. Statistical tests such as repeated measures ANOVAs and two-tailed paired-samples t-tests were performed to measure significant differences in the likability ratings among the three prime affect types; the results showed a strong shift in the likeness judgment for the images in the positively primed condition compared to the other two. The acquired EEGs were examined to assess the difference in brain activity associated with the three different conditions. The consistent results obtained confirmed the overall priming effect on participants’ explicit ratings. In addition, machine learning algorithms such as support vector machines (SVMs), and AdaBoost classifiers were applied to infer the prime affect type from the ERPs. The highest classification rates of 95.0% and 70.0% obtained respectively for average-trial binary classifier and average-trial multi-class further emphasize that the ERPs encode information about the different kinds of primes.

Good quality of surface finishing requires accurate control of tool positioning, as well as of contact forces. Various position and force control strategies have been applied to robotized finishing processes in industries. This paper reviews the control strategies applicable to robotic finishing operations, highlighting the benefits and limitations. Finally, adaptive force/impedance control architecture and its potential implementation on new generation compliant robots are briefed.

The research presented in this article investigates the influence of subliminal prime words on peoples' judgment about images, through electroencephalograms (EEGs). In this cross domain priming paradigm, the participants are asked to rate how much they like the stimulus images, on a 7-point Likert scale, after being subliminally exposed to masked lexical prime words, with EEG recorded simultaneously. Statistical analysis tools are used to analyze the effect of priming on behavior, and machine learning techniques to infer the primes from EEGs. The experiment reveals strong effects of subliminal priming on the participants' explicit rating of images. The subjective judgment affected by the priming makes visible change in event-related potentials (ERPs); results show larger ERP amplitude for the negative primes compared with positive and neutral primes. In addition, Support Vector Machine (SVM) based classifiers are proposed to infer the prime types from the average ERPs, which yields a classification rate of 70%.

The technological advancements in Intelligent Transport Systems have made it possible to acquire large amounts of traffic data in real-time. As a result, various data-mining techniques are being used to extract useful traffic patterns. The research presented in this article focuses on the detection of disruptive traffic events such as congestion. In most transportation studies, traffic parameters are typically modeled as time series. However, these techniques fail to incorporate the spatial dependencies between different traffic variables. In this work, the traffic quantities such as speeds are considered as the signals defined at the vertices of a network line graph. Furthermore, the graph wavelet operators are applied to the spatial signals to generate the wavelet coefficients at different wavelet scales. By analyzing these wavelet coefficients, useful information such as origin, propagation, and the span of traffic congestion are inferred. For analysis, we consider two major expressways in Singapore. The analysis shows that the abrupt changes in the speed can be captured by using the wavelet coefficients at the higher scales. On the other hand, the high magnitude coefficients at the lower wavelet scales reflect the smooth flow of the traffic across the network. I. INTRODUCTION Intelligent Transport Systems (ITS) can play a vital role in developing sophisticated control strategies for optimal usage of the road infrastructure of a land-scarce city like Singapore. The technological advancements in ITS and sensor developments enabled the availability of extensive data related to the on ground traffic conditions. Consequently, data driven approaches are being widely used for applications such as traffic sensing, congestion control, traffic forecasting, and route guidance (1)-(5). In this work, we focus on detecting disruptive traffic events such as unexpected traffic speed fluctuations, traffic slowdown, and congestion that hinder normal traffic flow. The early detection of such traffic events can be useful in issuing early warnings that will eventually help the drivers to plan alternate routes. Previous studies have proposed various methods to detect congestion and other disruptive events. These methods include Principal Component Analysis (PCA), Robust Principal Component Analysis (RPCA), Fourier transform, and wavelets. However, such approaches typically model

The ability to accurately predict traffic speed in a large and heterogeneous road network has many useful applications, such as route guidance and congestion avoidance. In principle, data-driven methods, such as support vector regression (SVR), can predict traffic with high accuracy because traffic tends to exhibit regular patterns over time. However, in practice, the prediction performance can significantly vary across the network and during different time periods. Insight into those spatiotemporal trends can improve the performance of intelligent transportation systems. Traditional prediction error measures, such as the mean absolute percentage error, provide information about the individual links in the network but do not capture global trends. We propose unsupervised learning methods, such as k-means clustering, principal component analysis, and self-organizing maps, to mine spatiotemporal performance trends at the network level and for individual links. We perform prediction for a large interconnected road network and for multiple prediction horizons with an SVR-based algorithm. We show the effectiveness of the proposed performance analysis methods by applying them to the prediction data of the SVR., Singapore. National Research Foundation (Singapore-MIT Alliance for Research and Technology Center. Future Urban Mobility Program)