Manual Tasks Research Articles

Test-retest reliability of kinematic and kinetic parameters during dual-task stair walking in the elderly.

Objective: This study aims to evaluate the test-retest reliability of kinematics and kinetics during single and dual-task stair walking in the elderly. Methods: Fifteen healthy elderly adults were recruited. Kinematic and kinetic parameters were measured using an infrared motion analysis system (Vicon, Oxford Metrics Ltd., Oxford, United Kingdom) and force platforms (Switzerland, Kistler 9287BA and 9281CA). Participants were tested under single-task and dual-task (serial 3 subtractions or carrying a cup of water) conditions. Each participant completed two sessions on two separate days with a 1-week interval. Intraclass correlation coefficients (ICC), Pearson correlation coefficient (r), and Bland-Altman plot were used to assess the reliability of stair walking. Results: When ascending stairs, the ICC of kinematics and kinetics ranged from fair to excellent (ICC = 0.500-0.979) in the single and dual tasks, except for step length (ICC = 0.394) in the single task. The r value of kinematics and kinetics ranged from 0.704 to 0.999. When descending stairs, the ICC of kinematics and kinetics ranged from good to excellent (ICC = 0.661-0.963), except for min hip moment (ICC = 0.133) and min ankle moment (ICC = 0.057) in the manual task. The r value of kinematics and kinetics ranged from 0.773 to 0.960 in the single and dual tasks. In the Bland-Altman plots, all the zero values and most of the dots fell in the 95% confidence interval, and the mean difference was found to be close to zero for all the parameters during stair walking. Conclusion: These results obtained from this study show the good test-retest reliability of step cadence, step speed, and step width during single- and dual-task stair walking in the elderly, and the poor reliability of step length during ascending stairs. All the kinetic parameters, including min hip moment, max knee moment, and min ankle moment, had good test-retest reliability during single- and dual-task stair walking, but min hip moment and min ankle moment had poor reliability during manual-task descending stair. These results may help researchers in the assessment of biomechanics of dual-task stair walking in the elderly and to interpret the effect of interventions in this population.

Hospital-wide natural language processing summarising the health data of 1 million patients.

Electronic health records (EHRs) represent a major repository of real world clinical trajectories, interventions and outcomes. While modern enterprise EHR's try to capture data in structured standardised formats, a significant bulk of the available information captured in the EHR is still recorded only in unstructured text format and can only be transformed into structured codes by manual processes. Recently, Natural Language Processing (NLP) algorithms have reached a level of performance suitable for large scale and accurate information extraction from clinical text. Here we describe the application of open-source named-entity-recognition and linkage (NER+L) methods (CogStack, MedCAT) to the entire text content of a large UK hospital trust (King's College Hospital, London). The resulting dataset contains 157M SNOMED concepts generated from 9.5M documents for 1.07M patients over a period of 9 years. We present a summary of prevalence and disease onset as well as a patient embedding that captures major comorbidity patterns at scale. NLP has the potential to transform the health data lifecycle, through large-scale automation of a traditionally manual task.

Leveraging Demonstrations for Learning the Structure and Parameters of Hierarchical Task Networks


 Hierarchical Task Networks (HTNs) are a common formalism for automated planning, allowing to leverage the hierarchical structure of many activities.
 While HTNs have been used in many practical applications, building a complete and efficient HTN model remains a difficult and mostly manual task.
 
 In this paper, we present an algorithm for learning such hierarchical models from a set of demonstrations.
 Given an initial vocabulary of tasks and accompanying demonstrations of possible ways to achieve them, we present how each task can be associated with a set of methods capturing the knowledge of how to achieve it.
 We focus on the algorithms used to learn the structure of the model and to efficiently parameterize it, as well as an evaluation in terms of planning performance.

Enhancing Web Applications Observability through Instrumented Automated Browsers

In software engineering, observability is the ability to determine the current state of a software system based on its external outputs or signals such as metrics, logs, or traces. Web engineers rely on the web browser console as the primary tool to monitor the client-side of web applications during end-to-end tests. However, this is a manual and time-consuming task due to the different browsers available. This paper presents BrowserWatcher, an open-source browser extension providing cross-browser capabilities to observe web applications and automatically gather browser console logs in different browsers (e.g., Chrome, Firefox, or Edge). We have leveraged this extension to conduct an empirical study analyzing the browser console of the top-50 public websites manually and automatically. The results show that BrowserWatcher gathers all the well-known log categories such as console or error traces. It also reveals that each web browser additionally includes other types of logs, which differ among browsers, thus providing distinct pieces of information for the same website.

Dual-task decrements in mono-, bi- and multilingual participants: Evidence for multilingual advantage

ABSTRACT Evidence suggests that language processing in bilinguals is less left-lateralized than in monolinguals. We explored dual-task decrement (DTD) for mono-, bi- and multilinguals in a verbal-motor dual-task paradigm. We expected monolinguals to show greater DTD than bilingual participants, who would show greater DTD than multilingual participants. Fifty right-handed participants (18 monolingual, 16 bilingual, 16 multilingual) completed verbal fluency and manual motor tasks in isolation and concurrently. Tasks were completed twice in isolation (left-handed, right-handed) and twice as dual-tasks (left-handed, right-handed); participants’ motor-executing hands served proxy for hemispheric activation. Results supported the hypotheses. Completing dual-tasks incurred greater cost for manual motor tasks than for verbal fluency tasks. Negative cost of performing dual-tasks diminished as number of languages spoken increased; in fact, multilingual individuals demonstrated a dual-task advantage in both tasks when using the right hand, strongest in the verbal task. Dual-tasking had the greatest negative impact on verbal fluency of monolingual participants when the motor task was completed with the right hand; for bi- and multi-lingual participants, the greatest negative impact on verbal fluency was seen when the motor task was completed with the left hand. Results provide support for the bi-lateralization of language function in bi- and multilingual individuals.

Impact of Artificial Intelligence on Purchasing and Procurement in South Korea

Purpose: The study sought to analyze the impact of artificial intelligence on purchasing and procurement in South Korea
 Methodology: The research was conducted entirely on computers. Secondary data, or data that doesn't require actual observation in the field, are the focus of desk research. Because it requires little more than an executive's time, telephone rates, and directories, desk research is generally seen as a low-cost strategy in comparison to field research. As a result, the research used data that had already been collected and reported. This secondary data was readily available via the internet's digital library and scholarly articles.
 Findings: The results show that the impact of AI on purchasing and procurement is significant. AI is used in automate manual tasks, reducing costs and improve accuracy. It also provides insights into purchasing and procurement processes that have previously been difficult to quantify. AI is a powerful tool that is used to improve the efficiency and effectiveness of the purchasing and procurement process. South Korea is making great strides in the use of AI and the impact it is having on purchasing and procurement is clear.
 Unique Contribution to Theory, Practice and Policy: Future research in the field of purchasing and procurement may be grounded in the rational economic theory and the resource based view theory. Policymakers, researchers, and academics from all across the world will all stand to gain from this study's findings. Executives in charge of national purchasing and procurement initiatives will also use the study's findings to boost artificial intelligence performance across the board. The research suggests that the purchasing and procurement sector should implement technological policies to boost the effectiveness of their primary operations and activities.

An overview of robotic grippers

The development of robotic grippers is driven by the need to execute particular manual tasks or meet specific objectives in handling operations. Grippers with specific functions vary from being small, accurate, and highly controllable, such as the surgical tool effectors of the Da Vinci robot (designed to be used as noninvasive grippers controlled by a human operator during keyhole surgeries), to larger, highly controllable grippers like the Shadow Dexterous Hand (designed to recreate the hand motions of a human). Additionally, there are less finely controllable grippers, such as the iRobot-Harvard-Yale (iHY) Hand or iRobot-Harvard-Yale (IIT)-Pisa SoftHand, which, instead, leverage natural motions during grasping via designs inspired by observed biomechanical systems. As robotic systems become more autonomous and widely used, it is becoming increasingly important to consider the design, form, and function of robotic grippers.

The Smart Network Management Automation Algorithm for Administration of Reliable 5G Communication Networks

Smart network management automation (SNMA) is a technology that enables the automation of network management tasks. It provides a platform for automating network management, allowing administrators to manage their networks more reliably and efficiently. It provides the tools to monitor and manage network performance, identify and troubleshoot problems, and automate the deployment of new services and applications. SNMA also allows for scalability to meet the needs of any organization, regardless of size or complexity. With SNMA, network administrators can quickly and accurately identify problems, increase network uptime, and reduce time spent on manual tasks. The SNMA provides the ability to adjust configurations quickly and respond to changes in the network environment. It can help reduce costs and improve the efficiency of network operations. This paper proposed an intelligent network management automation algorithm for network administration and management in 5G communication networks. The proposed algorithm achieved 91.82% of remote network administration, 95.25% of global network administration, 96.59% of urban network administration, and 95.07% of local network administration. It creates a single submission to the network to make configuration changes, register network resources, manage users’ IP addresses, and filter packets to ensure information security and other tasks.

Effects of Slide Sheet Use and Bed Position on Muscle Activities in the Low Back and Extremities: A Pilot Experimental Simulation Study.

Low-friction slide sheets (SS) are designed to reduce compression loads on the body during manual handling tasks, such as boosting patients. Using SS has been shown to decrease muscle activity in the lower back and upper extremities. However, it is unclear if this effect varies with different bed positions. To investigate this, we studied the effects of SS use, bed height, and their combination on muscle activity during a simulated patient boost. Thirty-three Japanese undergraduate students (age 21.0 ± 1.1 years; 14 men, 19 women) participated. Participants were asked to boost a dummy figure on the bed three times each using four conditions. During the repositioning task, electromyography of eight muscles of the lower back and upper and lower extremities, hip and knee joint flexion angles, pelvic forward tilt angle, and position of the center of mass based on the posterior superior iliac spine were evaluated. Electrophysiological activities of muscles of lower back and upper extremities were significantly lower with SS than without it in both bed positions (30% and 40% of body height); the reduction in muscle activities with SS use was 20% to 40%. Lowering the bed did not affect the SS effect magnitude on reducing muscle activities, although postural changes, including hip and knee joint flexion, were observed. SS reduced muscle activities in the back, upper, and lower extremities when the bed was in the low position, and this effect persisted at a bed height of ≥30% of the participant's height.

Effects of home-based manual dexterity training on cognitive function among older adults: a randomized controlled trial

BackgroundThe relationship between manual dexterity and cognitive function among older adults is well known; however, few studies have focused on manual dexterity training that confirms cognitive load of training in older adults through functional near-infrared spectroscopy (fNIRS) and verifies the effect of training. This study examined the effects of home-based manual dexterity training on cognitive function in older adults using a digital trail-making peg test device combining two conventional assessment tools namely, the peg and trail-making tests.MethodsFor 12 weeks, 57 healthy older adults aged 65–88 years participated in a parallel-group, randomized controlled trial, wherein home-based manual dexterity training was performed for approximately 20 min daily. To quantify the cognitive load in different manual dexterity conditions, we assessed the cortical activation patterns of the prefrontal cortex via a wearable four-channel fNIRS device. Participants in the control group were asked to continue their usual daily routines during the intervention period. Cognitive function was assessed using the Stroop Color and Word and Cognitive Impairment Tests. Manual dexterity was assessed using the Purdue Pegboard Test. All outcomes were estimated before and after the intervention.ResultsWe observed significant differences in prefrontal cortical activation between the different manual dexterity conditions. Only the intervention group showed a significant improvement in Stroop interference (169.0–108.9 ms, p = 0.032) and an executive function and assembly task of the Purdue Pegboard Test (22.5–26.4 counts, p < 0.001). Additionally, except the clock drawing task, cognitive function had a larger effect size (Cohen’s d) in the intervention group (d = 0.26–0.45) than in the control group (d = 0.11–0.28).ConclusionsHome-based manual dexterity training can improve performance in a complex manual dexterity task and executive functioning in older adults.Trial registrationUMIN-CTR Clinical Trial, UMIN000047203. Registered 17 March 2022 – Retrospectively registered, https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr.cgi?function=brows&action=brows&recptno=R000053844&type=summary&language=E

Benefits of repeated alternations – Task-specific vs. task-general sequential adjustments of dual-task order control

An important cognitive requirement in multitasking is the decision of how multiple tasks should be temporally scheduled (task order control). Specifically, task order switches (vs. repetitions) yield performance costs (i.e., task-order switch costs), suggesting that task order scheduling is a vital part of configuring a task set. Recently, it has been shown that this process takes specific task-related characteristics into account: task order switches were easier when switching to a preferred (vs. non-preferred) task order. Here, we ask whether another determinant of task order control, namely the phenomenon that a task order switch in a previous trial facilitates a task order switch in a current trial (i.e., a sequential modulation of task order switch effect) also takes task-specific characteristics into account. Based on three experiments involving task order switches between a preferred (dominant oculomotor task prior to non-dominant manual/pedal task) and a non-preferred (vice versa) order, we replicated the finding that task order switching (in Trial N) is facilitated after a previous switch (vs. repetition in Trial N - 1) in task order. There was no substantial evidence in favor of a significant difference when switching to the preferred vs. non-preferred order and in the analyses of the dominant oculomotor task and the non-dominant manual task. This indicates different mechanisms underlying the control of immediate task order configuration (indexed by task order switch costs) and the sequential modulation of these costs based on the task order transition type in the previous trial.

Effects of short-term hand immobilization on anticipatory mechanism for tool use.

The short-term immobilization of a limb such as the right arm can impair sensorimotor mechanisms, which in turn reduces motor control of this arm. However, it is not known whether immobilization also impairs the anticipatory mechanism for tool use without actual enactment. In two experiments, we asked participants to judge how they would use a tool in a particular environment (e.g., "Take the pencil to write on a sheet of paper"). Prior to this tool-use judgment task, some participants had been immobilized (right arm) for 24h. Results revealed that compared to controls, immobilized participants performed more poorly on the tool-use judgment task (accuracy and response time) as well as in a manual dexterity task. As our tool-use judgment task involved anticipating the expected perceptual effect of using a tool to achieve an environmental goal (e.g., writing on a sheet of paper), our data are discussed in line with theories of motor control (e.g., ideomotor theory) that emphasize the expected perceptual consequences of the action.

Performance and effectiveness of a passive back-support exoskeleton in manual material handling tasks in the construction industry

Performance and effectiveness of a passive back-support exoskeleton in manual material handling tasks in the construction industry

AUTOMATIC SEGMENTATION AND BONE ARCHITECTURE ANALYSIS OF SUBCHONDRAL BONE IN THE MURINE PROXIMAL TIBIA: EVALUATION OF AN OPEN-SOURCE PIPELINE

µCT images are commonly analysed to assess changes in bone density and architecture in preclinical murine models. Several platforms provide automated analysis of bone architecture parameters from volumetric regions of interest (ROI). However, segmentation of the regions of subchondral bone to create the volumetric ROIs remains a manual and time-consuming task. This study aimed to develop and evaluate automated pipelines for trabecular bone architecture analysis of mouse proximal tibia subchondral bone.A segmented dataset involving 62 knees (healthy and arthritic) from 10-week male C57BL/6 mice were used to train a U-Net type architecture, with µCT scans (downsampled) input that output segmentation and bone volume density (BV/TV) of the subchondral trabecular bone. Segmentations were upsampled and used in tandem with the original scans (10µ) as input for architecture analysis along with the thresholded trabecular bone. The analysis considered the manually and U-Net segmented ROIs using two available pipelines: the ITKBoneMorphometry library and CTan (SKYSCAN). The analyses included: bone volume (BV), total volume (TV), BV/TV, trabecular number (TbN), trabecular thickness (TbTh), trabecular separation (TbSp), and bone surface density (BSBV).There was good agreement for bone measures between the manual and U-Net pipelines utilizing ITK (R=0.88-0.98) and CTan (R=0.91-0.98). ITK and CTan showed good agreement for BV, TV, BV/TV, TbTh and BSBV (R=0.9-0.98). However, a limited agreement was seen between TbN (R=0.73) and TbSb (R=0.59) due to methodological differences in how spacing is evaluated.This U-Net/ITK pipeline seamlessly automated both segmentation and quantification of the proximal tibia subchondral bone. This automated pipeline allows the analysis of large volumes of data, and its open-source nature may enable the standardization of stereologic analysis of trabecular bone across different research groups.

Manual assembly learning, disability, and instructions: an industrial experiment

Cognitive assistance systems help people with learning disabilities to increase their skills and consequently their employment opportunities in the regular labour market. Research on advanced work instructions has encouraged training disabled workers in cognitively demanding production tasks, especially manual assembly. However, studies lack evidence on the effect of repetition or work cycle alongside the form of instruction and type of disability. This paper addresses this gap and reports on an experiment conducted at a sheltered workplace. Four forms of instruction (paper-based, animations, projection, adaptive projection) were tested to assist operators with three types of disability (illiterate, psychosocial, cognitive) with a manual assembly task. The results show that projection enhances the first assembly cycle. Challenging operators by filtering the content of the instruction with increased experience leads to greater independence and a better understanding of their tasks. However, adaptive instructions can form a barrier for those operators who are most dependent on mentor support. The form of instruction should thus be considered carefully for each operator as their adaptation to changes and cognitive assistance systems varies. The results are discussed in light of the Industry 5.0 human-centric and socially sustainable production agenda with managerial and research implications and future research priorities.

Hostel Management System

Software developed to oversee various hostel activities is the "HOSTELMANAGEMENT SYSTEM." For the beyond couple of years the quantity of instructive establishments is expanding quickly. As a result, the number of student housing options for this institution's students is also growing. As a result, running the hostel puts a lot of pressure on the person who runs it, so software is rarely used in this situation. This project addresses issues associated with hostel management and avoids issues that arise when manual tasks are carried out. The design of a computerized system that is compatible with the existing system and that is more user-friendly and GUI-oriented results from identifying the shortcomings of the current system. We can overcome the shortcomings of the existing system by increasing its efficiency. Both the front- end and back-end of this project will be developed using MySQL. Separately manage the students on the waiting list and the hostellers; • Process the allotment list. Administrator can email the endorsement warning to each supported understudy. When the allotment is confirmed by the administrator, the student's information is automatically added to the hosteller's record and deleted when vacation is taken or after the course ends. Understudies can enroll their protests. Administrator can alter notice board and every understudy can see it. The hostel secretary has the ability to edit the mess menu and calculate the hostel fee. • Hostellers have access to the status of each month's hostel fee. Key Word: Computerized system, GUI-oriented.

A Deep Attention-based U-Net for Airways Segmentation in Computed Tomography Images.

Airway segmentation is a way to quantify the diagnosis of pulmonary diseases, including chronic obstructive problems and bronchiectasis. Manual analysis by radiologists is a challenging task due to the complex airway structure. Additionally, tree-like patterns, varied shapes, sizes, and intensity make the manual airway segmentation task more complex. Deeper airways are even more difficult to segment as their intensity starts matching the lung parenchyma as the diameter of the airway cross-section gets reduced. Many earlier works have proposed different deep learning networks for airway segmentation but were unable to achieve the desired performance; hence the task of airway segmentation still possesses challenges in this field. This work proposes a convolutional neural network based on deep U-Net architecture and employs an attention block technique for airway segmentation. The attention mechanism aids in the extraction of the complicated and multi-sized airways found in the lung region, hence increasing the efficiency of the U-Net architecture. The model has been validated using VESSEL12 and EXACT09 datasets, individually and combined, with and without trachea images. The best DSC scores on EXACT09 and VESSEL12 datasets are 95.21% and 95.80%, respectively. The performance on both datasets combined gave a DSC score of 94.1%, showing that the overall performance of the proposed methodology is quite satisfactory. The generalizability of the model is also confirmed using k-fold cross-validation. The comparison of the proposed model to existing research on airway segmentation found competitive results. The use of an attention unit in the proposed model highlights the relevant information and reduces the irrelevant features, which helps to improve the performance and saves time.

The False King Olaf and His Necklace of Letters

The False King Olaf and His Necklace of Letters

An Empirical Study on Workers' Preferences in Human–Robot Task Assignment in Industrial Assembly Systems

Collaborative industrial robotic arms (cobots) are integrated industrial assembly systems relieving their human coworkers from monotonous tasks and achieving productivity gains. The question of task allocation arises in the organization of these human–robot interactions. State of the art shows static, compensatory task allocation approaches in current assembly systems and flexible, adaptive task sharing (ATS) approaches in human factors research. The latter should exploit the economic and ergonomic advantages of cobot usage. Previous research results did not provide a clear insight into whether industrial workers prefer static or adaptive task allocation and which tasks workers do prefer to assign to cobots. Therefore, we set up a cobot demonstrator with a realistic industrial assembly use case and did a user study with experienced workers from the shop floor (n = 25). The aim of the user study is to provide a systematic understanding and evaluation of workers' preferences in a practical context of human–robot interaction (HRI) in assembly. Our main findings are that participants preferred the ATS concept to a predetermined task allocation and reported increased satisfaction with the allocation. Results show that participants are more likely to give manual tasks to the cobot in contrast to cognitive tasks. It shows that workers do not entrust all tasks to robots, but like to take over cognitive tasks by themselves. This work contributes to the design of human-centered HRI in industrial assembly systems.

Assemble it like this! – Is AR- or VR-based training an effective alternative to video-based training in manual assembly?

AR- and VR-based training is increasingly being used in the industry to train workers safely and effectively for new tasks. In this study, we investigated and compared the effects of AR-, VR- and video-based training on short- and long-term objective performance measures and subjective evaluations in a manual assembly task. Our results showed that there was no difference between AR-, VR- and video-based training concerning the objective performance measures task completion time and error count. However, in the subjective evaluations VR-based training showed a significantly higher perceived task load and a lower usability rating than the AR- and video-based training regimes. An exploratory analysis additionally revealed partially better results for AR than for VR after adjusting the data for the age of the participants. Future research should further investigate the advantage of AR- and video-based methods over VR when the age and technology experience of participants are taken into account.