Reduce User Effort Research Articles

Estimating human joint moments unifies exoskeleton control, reducing user effort.

Robotic lower-limb exoskeletons can augment human mobility, but current systems require extensive, context-specific considerations, limiting their real-world viability. Here, we present a unified exoskeleton control framework that autonomously adapts assistance on the basis of instantaneous user joint moment estimates from a temporal convolutional network (TCN). When deployed on our hip exoskeleton, the TCN achieved an average root mean square error of 0.142 newton-meters per kilogram across 35 ambulatory conditions without any user-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level-ground and incline walking compared with walking without wearing the exoskeleton. This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community.

Tdsa: An R package for time‐dependent sensitivity analysis

Abstract Sensitivity analysis of ecological and epidemiological models is often used to identify the best targets of opportunity for control or management, for example, which subpopulations should be prioritised for vaccination or protection. However, effectiveness of a management action depends not just on which system component is targeted but also on when an action is taken. Traditional methods of model sensitivity analysis apply to time‐invariant parameter perturbations, which limits their ability to address questions about the timing of management interventions. A semianalytic method for performing time‐dependent sensitivity analysis (TDSA) has been recently introduced to address this need (Ng et al., in press). However, some of the steps typically require substantial time and effort. We have developed an R package, tdsa, that automates all steps required for TDSA by using numerical rather than analytic differentiation of model terms to evaluate the sensitivity equations. By avoiding analytic differentiation, the package substantially reduces user effort and risk of human error and increases the generality of TDSA by making it possible to analyse nonparametric or equation‐free models. The results of TDSA may be sensitive to assumptions of the system model. By reducing the effort needed to perform TDSA, the tdsa package facilitates comparison of results under different model assumptions, hence allowing for more robust conclusions. We illustrate this using an example involving the transmission of Morogoro virus in the Natal multimammate mouse, under different assumptions about how the contact rate depends on mouse population density. By automating most of the mathematical and programming steps of the semianalytical approach, the tdsa package makes TDSA accessible to a wide range of users with varying quantitative expertise.

PIMedSeg: Progressive interactive medical image segmentation

Background and Objective: Accurate object segmentation in medical images is a crucial step in medical diagnosis and other applications. Despite years of research on automatic segmentation approaches, achieving clinically acceptable image quality remains challenging. Interactive segmentation is seen as a promising alternative; thus, we propose a new interactive segmentation framework based on a progressive workflow to reduce user effort and provide high-quality results. Method: First, our approach encodes user-provided region clicks and edge scribbles using our proposed disk and curve transform. Then, it is followed by refinement with a transformer-based module that extracts effective features from the outputs of the convolutional neural network (CNN) and the extra input maps. Result: Extensive experiments conducted on various medical images, including ultrasound (US), computerized tomography (CT), and magnetic resonance images (MRI), have demonstrated the effectiveness of our new approach over the state-of-the-art alternatives. Conclusion: The proposed framework can achieve high-quality segmentation using minimal interactions without the substantial cost of manual segmentation.

An Integrated Co-Design Optimization Toolchain Applied to a Conjugate Cam-Follower Drivetrain System

Due to ever increasing performance requirements, model-based optimization and control strategies are increasingly being adopted by machine builders and automotive companies. However, this demands an increase in modelling effort and a growing knowledge of optimization techniques, as a sufficient level of detail is required in order to evaluate certain performance characteristics. Modelling tools such as MATLAB Simscape have been created to reduce this modelling effort, allowing for greater model complexity and fidelity. Unfortunately, this tool cannot be used with high-performance gradient-based optimization algorithms due to obfuscation of the underlying model equations. In this work, an optimization toolchain is presented that efficiently interfaces with MATLAB Simscape to reduce user effort and the necessary skill and computation time required for the optimization of high-fidelity drivetrain models. The toolchain is illustrated on an industrially relevant conjugate cam-follower system, which is modelled in the Simscape environment and validated with respect to a higher-fidelity modeling technique, namely, the finite element method (FEM).

MISARA: Matlab Interface for Seismo-Acoustic aRray Analysis

AbstractVolcanic activity produces a broad spectrum of seismic and acoustic signals whose characteristics provide important clues on the underlying magmatic processes. Networks and arrays of seismic and acoustic sensors are the backbone of most modern volcano monitoring programs. Investigation of the signals gathered by these instruments requires efficient workflows and specialist software. The high sampling rates, typically 50 Hz or greater, at which seismic and acoustic waveforms are recorded by multistation networks and dense arrays leads to the rapid accumulation of large volumes of data, making the implementation of efficient data analysis workflows for volcano surveillance a challenging task. Here, we present an open-source MATLAB graphical user interface, MISARA (Matlab Interface for Seismo-Acoustic aRray Analysis), designed to provide a user-friendly workflow for the analysis of seismoacoustic data in volcanic environments. MISARA includes efficient algorithm implementations of well-established techniques for seismic and acoustic data analysis. It is designed to support visualization, characterization, detection, and location of volcano seismoacoustic signals. Its intuitive, modular, structure facilitates rapid, semiautomated, inspection of data and results, thus reducing user effort. MISARA was tested using seismoacoustic data recorded at Etna Volcano (Italy) in 2010, 2011, and 2019, and is intended for use in education and research, and to support routine data analysis at volcano observatories.

IoT BASED WHEELCHAIR FALL DETECTION SYSTEM

It is important to take care of our elderly people and keep an eye on their well-being and security. It is likely that they have a high risk of falling because of their fragile bones and joints, which brings in the usage of wheelchair for most of the old people. A wheelchair-bound individual who is physically impaired needs to be watched out for falls as well. There are currently monitoring systems using CCTV cameras, but they are very expensive. The average person cannot afford such a system. As a result, we suggest an affordable fall detection system using the newest technology such as Internet of Things (IoT). The system uses gyroscope and accelerometer sensors to detect individual movements. Both collaborate to detect the fall events improving the detection’s accuracy. It might be mounted on a person's hand or wheelchair for finding. The sensor is connected to a microcontroller so that it can continuously monitor the data about the increasing speed. What if the individual did not fall and the warning given was false? The framework allows the alarm to be turned off if the individual presses the snooze button within 5 seconds. Therefore, IoT will reduce user effort while also generating fertile work that makes accessing the system simple. Keywords- Arduino UNO R3, w h e e l c h a i r f a l l d e t e c t i o n , a c c u r a c y , S e n s o r s

Team IHMC at the 2020 Cybathlon: a user-centered approach towards personal mobility exoskeletons

BackgroundThe past few decades have seen rapid advancements in exoskeleton technology, with a considerable shift towards applications involving users with gait pathologies. Commercial devices from ReWalk, Ekso Bionics, and Indego, mainly designed for rehabilitation purposes, have inspired the development of many research platforms aimed at extending capabilities for use as safe and effective personal mobility devices. The 2016 Cybathlon featured an impressive demonstration of exoskeletons designed to enable mobility for individuals with spinal cord injury, however, not a single team completed every task and only two completed the stairs. Major improvements were showcased at the 2020 Cybathlon, with seven of the nine teams completing a similar set of tasks. Team IHMC built upon its silver-medal success from 2016 with an upgraded device, Quix.MethodsQuix features several notable improvements including an additional powered degree of freedom for hip ab/adduction to laterally shift the device and reduce user effort while walking, custom-tailored cuffs and soft goods based on 3D body scans to optimize user comfort, and a streamlined testing pipeline for online tuning of gait parameters.ResultsTeam IHMC finished in fourth place behind the teams from EPFL and Angel Robotics. Although we suffered from a considerably slower flat-ground walking speed, our pilot reported marked improvements in overall effort, comfort, and ease-of-use compared to our previous device.ConclusionsClear progress in exoskeleton development has been exhibited since the inaugural Cybathlon, with tasks involving rough terrain, stairs, and ramps now posing little threat to most of the competitors. As a result, the layout of the powered exoskeleton course will likely undergo significant modifications to further push the devices towards suitability for personal everyday use. The current tasks do not address the issue of donning and doffing, nor do they simulate a scenario similar to maneuvering a kitchen to prepare a meal, for example. An additional limitation that may be more difficult to test in a competition setting is the required upper-body effort to manipulate the device in an effective manner.

An Enhanced Triadic Color Scheme for Content-Based Image Retrieval

The complexity of multimedia content, particularly images, has risen dramatically in recent years, and millions of images are shared on social media every day. Finding or retrieving an appropriate image is becoming more difficult due to the increase in the volume of shared and archived multimedia data. Any image retrieval model must, at a bare minimum, locate and classify images that are visually related to the user’s query. The vast majority of Internet search engines employ text algorithms that fetch images using captions as input. Even though there is a lot of study being done to increase the effectiveness of automatic image annotation, retrieval errors can occur due to differences in visual perception. Content-based image retrieval (CBIR) addresses the aforementioned issue because visual analysis of the content is included in the query image. On the other hand, feature extraction is significantly challenging because of semantic gap. This work proposes a strategy for effective retrieval in similarity images using the triadic color scheme RGB, YCbCr, and L ∗ a ∗ b ∗ based on reranking. We want to increase image similarity and encourage more relevant reranking. As a result of the findings, it can be concluded that a triadic color scheme improves precision by 5% more dramatically than existing schemes and also efficiently improves retrieved results while reducing user effort.

Assisting Forearm Function in Children With Movement Disorders via A Soft Wearable Robot With Equilibrium-Point Control.

Wearable robots are envisioned to amplify the independence of people with movement impairments by providing daily physical assistance. For portable, comfortable, and safe devices, soft pneumatic-based robots are emerging as a potential solution. However, due to the inherent complexities, including compliance and nonlinear mechanical behavior, feedback control for facilitating human–robot interaction remains a challenge. Herein, we present the design, fabrication, and control architecture of a soft wearable robot that assists in supination and pronation of the forearm. The soft wearable robot integrates an antagonistic pair of pneumatic-based helical actuators to provide active pronation and supination torques. Our main contribution is a bio-inspired equilibrium-point control scheme for integrating proprioceptive feedback and exteroceptive input (e.g., the user’s muscle activation signals) directly with the on/off valve behavior of the soft pneumatic actuators. The proposed human–robot controller is directly inspired by the equilibrium-point hypothesis of motor control, which suggests that voluntary movements arise through shifts in the equilibrium state of the antagonistic muscle pair spanning a joint. We hypothesized that the proposed method would reduce the required effort during dynamic manipulation without affecting the error. In order to evaluate our proposed method, we recruited seven pediatric participants with movement disorders to perform two dynamic interaction tasks with a haptic manipulandum. Each task required the participant to track a sinusoidal trajectory while the haptic manipulandum behaved as a Spring-Dominate system or Inertia-Dominate system. Our results reveal that the soft wearable robot, when active, reduced user effort on average by 14%. This work demonstrates the practical implementation of an equilibrium-point volitional controller for wearable robots and provides a foundational path toward versatile, low-cost, and soft wearable robots.

I Know What You Need: Investigating Document Retrieval Effectiveness with Partial Session Contexts

Reducing user effort in finding relevant information is one of the key objectives of search systems. Existing approaches have been shown to effectively exploit the context from the current search session of users for automatically suggesting queries to reduce their search efforts. However, these approaches do not accomplish the end goal of a search system—that of retrieving a set of potentially relevant documents for the evolving information need during a search session. This article takes the problem of query prediction one step further by investigating the problem of contextual recommendation within a search session. More specifically, given the partial context information of a session in the form of a small number of queries, we investigate how a search system can effectively predict the documents that a user would have been presented with had he continued the search session by submitting subsequent queries. To address the problem, we propose a model of contextual recommendation that seeks to capture the underlying semantics of information need transitions of a current user’s search context. This model leverages information from a number of past interactions of other users with similar interactions from an existing search log. To identify similar interactions, as a novel contribution, we propose an embedding approach that jointly learns representations of both individual query terms and also those of queries (in their entirety) from a search log data by leveraging session-level containment relationships. Our experiments conducted on a large query log, namely the AOL, demonstrate that using a joint embedding of queries and their terms within our proposed framework of document retrieval outperforms a number of text-only and sequence modeling based baselines.

Design of a low-cost, reconfigurable, standing wheelchair with easy and stable sit-stand-sit transition capability

Purpose Assistive devices like Standing Wheelchairs (SWC) have remained out of reach of the economically underprivileged even before the pandemic-induced financial downturn, and more so now. This paper describes the mechanical design of a manual user-actuated SWC that is cost-effective (equivalent of USD 210 in India, ex-factory) and has special features that minimise user effort and accommodates varying body weights (50–110 kg) and dimensions (1.52–1.83 m height). Methods The design includes a six-bar mechanism and spring balancing to optimise user effort during operation. The optimised gas spring incorporates adjustability to minimise each user’s force for sit-stand-sit transitions. The handle shape is ergonomically designed using kinematic analysis to provide convenient gripping positions for actuation. The design has been customised based on parametric studies to suit varying body weights. Results Overall, the SWC design provides standing functionality with ease of operation, safety locks, customisability, affordability, outdoor mobility and is aesthetically pleasing. Conclusions Customisability and the low cost of the device would enhance the accessibility of the SWC to a larger group of eligible users. Implications for rehabilitation Manual user-operated standing wheelchair design using a six-bar mechanism Spring balancing used to reduce user effort to self-lift to the standing position Kinematic analysis used to determine convenient handle location for user ease Customisability for wide range of users to ensure correct posture, optimal effort Design refined through multiple iterations using inputs from users and clinicians Design commercialised at an affordable cost, making it accessible to a larger population

Variable Damping Control for pHRI: Considering Stability, Agility, and Human Effort in Controlling Human Interactive Robots

This article presents a multi-degree-of-freedom variable damping controller to manage the trade-off between stability and agility and to reduce user effort in physical human-robot interaction. The controller accounts for the human body's inherent impedance properties and applies a range of robotic damping from negative (energy injection) to positive (energy dissipation) values based on the user's intent of motion. To evaluate the effectiveness of the proposed controller in balancing the trade-off between stability/agility and reducing user effort, two studies are performed on both the human upper-extremity and lower-extremity to represent both industrial and rehabilitation applications of the proposed controller. These studies required subjects to perform a series of multidimensional target reaching tasks while the human user interacted with either the end-effector of a robotic arm for the upper-extremity study or a wearable ankle robot for the lower-extremity study. Stability, agility, and user effort are quantified by a variety of performance metrics. Stability is quantified by both overshoot and stabilization time. Mean and maximum speed are used to quantify agility. To quantify the user effort, both overall and maximum muscle activation, and mean and maximum root-mean-squared interaction force are calculated. The results of both the upper- and lower-extremity studies demonstrate that the controller is able to reduce user effort while increasing agility at a negligible cost to stability.

Open sesame: Lessons in password-based user authentication

The cost of unusable password policies in the wild is well documented. These costs impinge both business and security. The alternative is to move to multi-factor and risk-based authentication, which include software authenticators, hardware tokens, and biometrics. This paper provides an overview of the research in this area and concludes with guidance on how to best leverage password-based authentication. We recommend that designers should only implement efforts backed by empirical evidence, offer solutions to reduce user effort, and use compensating controls to address the underlying limitations of passwords.

Evaluation of Hybrid Control and Palpation Assistance for Situational Awareness in Telemanipulated Task Execution

The use of intelligent feedback modalities to control and react to interaction forces during surgical procedures is an important factor in enabling safe and precise surgery. We explore the use of a model-mediated telemanipulation framework to enhance a user’s situational awareness using assistive virtual fixtures and semi-automated task execution for safe and intuitive environment interaction during robotic laparoscopic surgery. The framework allows stiffness mapping with semi-autonomous excitation, hybrid position-force control, and model updates during soft geometry contact. A 24-person study was carried out at 3 sites in simulated ablation and palpation of phantom anatomy. Compared to methods lacking intelligent feedback and guidance, the proposed framework improved task execution metrics (force regulation, completion time, path-following error) and reduced user effort.

Active neural learners for text with dual supervision

Dual supervision for text classification and information retrieval, which involves training the machine with class labels augmented with text annotations that are indicative of the class, has been shown to provide significant improvements, both in and beyond active learning (AL) settings. Annotations in the simplest form are highlighted portions of the text that are indicative of the class. In this work, we aim to identify and realize the full potential of unsupervised pretrained word embeddings for text-related tasks in AL settings by training neural nets—specifically, convolutional and recurrent neural nets—through dual supervision. We propose an architecture-independent algorithm for training neural networks with human rationales for class assignments and show how unsupervised embeddings can be better leveraged in active learning settings using the said algorithm. The proposed solution involves the use of gradient-based feature attributions for constraining the machine to follow the user annotations; further, we discuss methods for overcoming the architecture-specific challenges in the optimization. Our results on the sentiment classification task show that one annotated and labeled document can be worth up to seven labeled documents, giving accuracies of up to 70% for as few as ten labeled and annotated documents, and shows promise in significantly reducing user effort for total-recall information retrieval task in systematic literature reviews.

Recommendation Framework Combining User Interests with Fashion Trends in Apparel Online Shopping

Although fashion-related products account for most of the online shopping categories, it becomes more difficult for users to search and find products matching their taste and needs as the number of items available online increases explosively. Personalized recommendation of items is the best method for both reducing user effort on searching for items and expanding sales opportunity for sellers. Unfortunately, experimental studies and research on fashion item recommendation for online shopping users are lacking. In this paper, we propose a novel recommendation framework suitable for online apparel items. To overcome the rating sparsity problem of online apparel datasets, we derive implicit ratings from user log data and generate predicted ratings for item clusters by user-based collaborative filtering. The ratings are combined with a network constructed by an item click trend, which serves as a personalized recommendation through a random walk. An empirical evaluation on a large-scale real-world dataset obtained from an apparel retailer demonstrates the effectiveness of our method.

Data-driven weight optimization for real-time mesh deformation

3D model deformation has been an active research topic in geometric processing. Due to its efficiency, linear blend skinning (LBS) and its follow-up methods are widely used in practical applications as an efficient method for deforming vector images, geometric models and animated characters. LBS needs to determine the control handles and specify their influence weights, which requires expertise and is time-consuming. Further studies have proposed a method for efficiently calculating bounded biharmonic weights of given control handles which reduces user effort and produces smooth deformation results. The algorithm defines a high-order shape-aware smoothness function which tends to produce smooth deformation results, but fails to generate locally rigid deformations.To address this, we propose a novel data-driven approach to producing improved weights for handles that makes full use of available 3D model data by optimizing an energy consisting of data-driven, rigidity and sparsity terms, while maintaining its advantage of allowing handles of various forms. We further devise an efficient iterative optimization scheme. Through contrast experiments, it clearly shows that linear blend skinning based on our optimized weights better reflects the deformation characteristics of the model, leading to more accurate deformation results, outperforming existing methods. The method also retains real-time performance even with a large number of deformation examples. Our ablation experiments also show that each energy term is essential.

Assistive Arm-Exoskeleton Control Based on Human Muscular Manipulability.

This paper introduces a novel control framework for an arm exoskeleton that takes into account force of the human arm. In contrast to the conventional exoskeleton controllers where the assistance is provided without considering the human arm biomechanical force manipulability properties, we propose a control approach based on the arm muscular manipulability. The proposed control framework essentially reshapes the anisotropic force manipulability into the endpoint force manipulability that is invariant with respect to the direction in the entire workspace of the arm. This allows users of the exoskeleton to perform tasks effectively in the whole range of the workspace, even in areas that are normally unsuitable due to the low force manipulability of the human arm. We evaluated the proposed control framework with real robot experiments where subjects wearing an arm exoskeleton were asked to move a weight between several locations. The results show that the proposed control framework does not affect the normal movement behavior of the users while effectively reduces user effort in the area of low manipulability. Particularly, the proposed approach augments the human arm force manipulability to execute tasks equally well in the entire workspace of the arm.

A smart harvester for capturing energy from human ankle dorsiflexion with reduced user effort

Scavenging energy from human motion is a potential way to meet the increasing requirement of electrical power supply for portable electronics. However, since the conventional energy harvesters may collect both positive and negative work, the users have to pay extra efforts. This work aims at developing a smart energy harvester to accurately identify and capture the negative work of human ankle motion. During normal walking, only the dorsiflexion at stance phase performs negative work. Thus, one-way clutch is employed to filter ankle plantarflexion and mechanical contact switch array is used to disconnect electrical load, avoiding capturing positive work when ankle dorsiflexion is at swing phase. With the one-way clutch and mechanical contact switch array, the energy harvester can effectively target negative work as energy scavenged without consuming electrical energy. A wearable and light prototype is built to test its power output and users’ metabolic expenditure during walking. The energy harvesting system is also modeled and analyzed. The experimental results show that the energy harvester produces an average power of 0.35 W at 4.9 km h−1 while reducing metabolic expenditure by 0.84 W, so as to achieve lower cost of harvesting compared with the previous work.

Rapid time-stamped analysis of filament motility.

The in vitro motility assay is a valuable tool to understand motor protein mechanics, but existing algorithms are not optimized for accurate time resolution. We propose an algorithm that combines trace detection with a time-stamped analysis. By tracking filament ends, we minimize data loss from overlapping and crossing filaments. A movement trace formed by each filament end is created by time-stamping when the filament either first (filament tip) or last (filament tail) occupies a pixel. A frame number vs. distance curve is generated from this trace, which is segmented into regions by slope to detect stop-and-go movement. We show, using generated mock motility videos, accurate detection of velocity and motile fraction changes for velocities < 0.05 pixels per frame, without manual trace dropping and regardless of filament crossings. Compared with established algorithms we show greatly improved accuracy in velocity and motile fraction estimation, with greatly reduced user effort. We tested two actual motility experiments: (1) adenosine triphosphate (ATP) added to skeletal myosin in rigor; (2) myosin light chain phosphatase (MLCP) added to phasic smooth muscle myosin. Our algorithm revealed previously undetectable features: (1) rapid increase in motile fraction paralleled by a slow increase in velocity as ATP concentration increases; (2) simultaneous reductions in velocity and motile fraction as MLCP diffuses into the motility chamber at very low velocities. Our algorithm surpasses existing algorithms in the resolution of time dependent changes in motile fraction and velocity at a wide range of filament lengths and velocities, with minimal user input and CPU time.