Estimating Human Wrist Stiffness during a Tooling Task.

In this work, we propose a practical approach to estimate human joint stiffness during tooling tasks for the purpose of programming a robot by demonstration. More specifically, we estimate the stiffness along the wrist radial-ulnar deviation while a human operator performs flexion-extension movements during a polishing task. The joint stiffness information allows to transfer skills from expert human operators to industrial robots. A typical hand-held, abrasive tool used by humans during finishing tasks was instrumented at the handle (through which both robots and humans are attached to the tool) to assess the 3D force/torque interactions between operator and tool during finishing task, as well as the 3D kinematics of the tool itself. Building upon stochastic methods for human arm impedance estimation, the novelty of our approach is that we rely on the natural variability taking place during the multi-passes task itself to estimate (neuro-)mechanical impedance during motion. Our apparatus (hand-held, finishing tool instrumented with motion capture and multi-axis force/torque sensors) and algorithms (for filtering and impedance estimation) were first tested on an impedance-controlled industrial robot carrying out the finishing task of interest, where the impedance could be pre-programmed. We were able to accurately estimate impedance in this case. The same apparatus and algorithms were then applied to the same task performed by a human operators. The stiffness values of the human operator, at different force level, correlated positively with the muscular activity, measured during the same task.

Plantar pressure alterations associated with increased BMI in young adults.

BACKGROUND: 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. RESEARCH QUESTION: 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. METHODS: Fifty-two overweight (BMI >25, average 29.3 ± 4.02) and sixty-four control college students (BMI<25, 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. RESULTS AND SIGNIFICANCE: 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.

Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis.

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.

Effect of Subliminal Lexical Priming on the Subjective Perception of Images: A Machine Learning Approach.

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.

On the effect of subliminal priming on subjective perception of images: a machine learning approach.

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%.