Assistive Tasks Research Articles

Using Gerontology Theory to Guide the Development of Artificial Intelligence to Support Aging-in-Place

If artificial intelligence (AI) is to support aging-in-place, determining how, when, and why to apply AI is a crucial endeavor. A seminal gerontology meta-theory called Se-lection, Optimization, and Compensation (SOC) model has promise to conceptualize how AI can play a role in aging-in-place. The model posits that successful aging re-quires selecting goals/domains to apply resources, opti-mizing means to best achieve those goals, and compen-sating for losses by attaining new resources or tapping into unused resources for alternative means of pursuing those goals. In this short paper, we describe the SOC model, and draw links to domains in which AI can sup-port aging in place. For example, AI can assist with health-related decision making (selection), cognitive training and reminders (optimization), and domestic task assistance (compensation). Human-centered considera-tions are provided for implementation of AI in the home.

Electrocardiogram and respiration recordings show a reduction in the physical burden on professional caregivers when performing care tasks with a transfer support robot

ABSTRACT In this study, we assessed the physical burden on professional caregivers when using a transfer support robot, “Hug,” to transfer and move a care recipient. We compared heart rate (HR), heart rate variability (HRV), and the time-series synchronization between HRV and respiration in caregivers using the robot or a normal wheelchair as control. Under both conditions, a commercial wearable device was used to simultaneously obtain electrocardiogram and respiration signals while performing care tasks, which comprised transfer from a bed to a wheelchair or to the robot, movement to a remote location, and transfer to a chair. We found that HR was significantly higher and HRV and wavelet coherence were significantly lower in the control than with Hug during at least the first 30 seconds of rest after the task (p < 0.05). This suggests that Hug could reduce the increase in HR and thereby maintain a more constant rhythm between HRV and respiration during care tasks. A post-task questionnaire also revealed that caregivers felt the physical burden of the tasks was reduced using Hug. Our results suggest that the introduction of transfer support robots can reduce the physical burden for caregivers of daily transfer assistance tasks.

The importance of institutional capacity in drinking water supply system governance: A policy evaluation from border area

In most developing countries, the equal access to clean and potable water is a major challenge faced by many communities. This research aims to evaluate water supply system governance policies as well as potential springs, constraints and problems related to the water needs of border communities. This research is a descriptive study using a qualitative approach. The qualitative approach looks at policies, institutions, conflicts, and problems faced, as well as looking at data on water sources, socio-economic challenges, culture, and local wisdom. The research location was in Belu Regency, on the Indonesia-Leste Timor border. Research informants were representatives of local government, academics and the community. The problems faced are the underdevelopment of piped networks due to budget constraints, low capacity and quality of human resources, and the absence of specific regulations governing the Drinking Water Supply System (SPAM). The study area has springs spread across 11 subdistricts, with raw water potential ranging from 0.5 to 250 liters per second. Most of these water sources are good for community consumption, but some have not been optimally utilized so that they cannot be fully utilized to meet the needs of the population. The results show that SPAM management policies can be implemented to a certain extent, but have not yet run optimally because they still require capacity building and competence of the apparatus and local communities and an increase in the role of local governments to help implement central government policies in relation to assistance tasks and as a tool for deconcentration of the central government. In conclusion, the potential of raw water sources has not been fully utilized due to the challenges of infrastructure development and the limited capacity of human resources and budgets. Without good management and institutional support, communities will continue to face problems related to access to clean water.

Design and Validation of an Ambulatory User Support Gait Rehabilitation Robot: NIMBLE

Relearning to walk requires progressive training in real scenarios—overground—along with assistance in basic tasks, such as balancing. In addition, user ability must be maximized through compliant robotic assistance as needed. Despite decades of research, gait rehabilitation robotic devices yield controversial results. This article presents the conceptual design of a novel walking assistance and rehabilitation robot, the NIMBLE robot, aimed at providing ambulatory, bodyweight-supported gait training, assisting the user’s center of mass trajectory to aid weight transfer and dynamic balance during walking. NIMBLE consists of a robotic mobile frame, a partial bodyweight support (PBWS) system, an ambulatory lower-limb exoskeleton (Exo-H3) and a cable-driven pelvis-assisting robot. Designed as a modular structure, it differentiates hierarchical communication levels through a Robot Operating System (ROS) 2 network. We present the mechatronic design and experimental results assessing the impact of the mechatronic coupling between the robotic modules on the walking kinematics and the frame movement control performance. The robotic frame hardly affects the walking kinematics up to 2 degrees in both the sagittal and frontal planes, making it feasible for lateral balance and weight translation training. Moreover, it successfully tracks and follows user trajectories. The NIMBLE robotic frame assessment shows promising results for ambulatory gait rehabilitation.

A maritime image dehazing algorithm based on improved VQGAN

ABSTRACT Maritime image dehazing faces challenges as existing methods struggle to handle different concentrations of sea fog, and the treatment of details in dehazed results is often suboptimal. This paper proposes a maritime image dehazing algorithm based on the VQGAN mode. Specifically, since VQGAN is capable of generating high-quality discrete codebooks, we input the haze-free maritime dataset into VQGAN for pre-training to obtain a high-quality codebook prior. During the vector quantisation stage,high-quality vectors from the discrete codebook undergo distance calculations with the upsampled vectors containing haze.This process replaces the vectors containing haze with the high-quality vectors. To address the issue of residual fog, the authors introducing the FAM-UNet encoder.To tackle distortion issues, the authors introduced the Pyramid, Cascading, and Deformable Convolution Feature Alignment Module (PCD) into the original model. The experimental results indicate that our model can effectively restore clear images, providing assistance for subsequent advanced computer vision tasks..

Assistance in Picking Up and Delivering Objects for Individuals with Reduced Mobility Using the TIAGo Robot

Individuals with reduced mobility, including the growing elderly demographic and those with spinal cord injuries, often face significant challenges in daily activities, leading to a dependence on assistance. To enhance their independence, we propose a robotic system that facilitates greater autonomy. Our approach involves a functional assistive robotic implementation for picking, placing, and delivering containers using the TIAGo mobile manipulator robot. We developed software and routines for detecting containers marked with an ArUco code and manipulating them using the MoveIt library. Subsequently, the robot navigates to specific points of interest within a room to deliver the container to the user or another designated location. This assistance task is commanded through a user interface based on a web application that can be accessed from the personal phones of patients. The functionality of the system was validated through testing. Additionally, a series of user trials were conducted, yielding positive feedback on the performance and the demonstration. Insights gained from user feedback will be incorporated into future improvements to the system.

Infrared and Harsh Light Visible Image Fusion Using an Environmental Light Perception Network.

The complementary combination of emphasizing target objects in infrared images and rich texture details in visible images can effectively enhance the information entropy of fused images, thereby providing substantial assistance for downstream composite high-level vision tasks, such as nighttime vehicle intelligent driving. However, mainstream fusion algorithms lack specific research on the contradiction between the low information entropy and high pixel intensity of visible images under harsh light nighttime road environments. As a result, fusion algorithms that perform well in normal conditions can only produce low information entropy fusion images similar to the information distribution of visible images under harsh light interference. In response to these problems, we designed an image fusion network resilient to harsh light environment interference, incorporating entropy and information theory principles to enhance robustness and information retention. Specifically, an edge feature extraction module was designed to extract key edge features of salient targets to optimize fusion information entropy. Additionally, a harsh light environment aware (HLEA) module was proposed to avoid the decrease in fusion image quality caused by the contradiction between low information entropy and high pixel intensity based on the information distribution characteristics of harsh light visible images. Finally, an edge-guided hierarchical fusion (EGHF) module was designed to achieve robust feature fusion, minimizing irrelevant noise entropy and maximizing useful information entropy. Extensive experiments demonstrate that, compared to other advanced algorithms, the method proposed fusion results contain more useful information and have significant advantages in high-level vision tasks under harsh nighttime lighting conditions.

Pushing with Soft Robotic Arms via Deep Reinforcement Learning

Soft robots can adaptively interact with unstructured environments. However, nonlinear soft material properties challenge modeling and control. Learning‐based controllers that leverage efficient mechanical models are promising for solving complex interaction tasks. This article develops a closed‐loop pose/force controller for a dexterous soft manipulator enabling dynamic pushing tasks using deep reinforcement learning. Force tests investigate the mechanical properties of a soft robot module, resulting in orthogonal forces of N. Then, the policy is trained in simulation leveraging a dynamic Cosserat rod model of the soft robot. Domain randomization mitigate the sim‐to‐real gap while careful reward engineering induced pose and force control even without explicit force inputs. Despite the approximate simulation, the sim‐to‐real transfer achieved an average reaching distance of mm (), an average orientation error of rad () and applied pushing forces up to N. Such performance is reasonable for the intended assistive tasks of the manipulator. The experiments uncovered that the soft robot interacting with the environment exhibited torsional and counter‐balancing movements. Although not explicitly enforced, they emerged from the mechanical intelligence of the manipulator. The results demonstrate the potential of soft robotic manipulation via reinforcement learning.

SCC-GPT: Source Code Classification Based on Generative Pre-Trained Transformers

Developers often rely on online resources, such as Stack Overflow (SO), to seek assistance for programming tasks. To facilitate effective search and resource discovery, manual tagging of questions and posts with the appropriate programming language is essential. However, accurate tagging is not consistently achieved, leading to the need for the automated classification of code snippets into the correct programming language as a tag. In this study, we introduce a novel approach to automated classification of code snippets from Stack Overflow (SO) posts into programming languages using generative pre-trained transformers (GPT). Our method, which does not require additional training on labeled data or dependency on pre-existing labels, classifies 224,107 code snippets into 19 programming languages. We employ the text-davinci-003 model of ChatGPT-3.5 and postprocess its responses to accurately identify the programming language. Our empirical evaluation demonstrates that our GPT-based model (SCC-GPT) significantly outperforms existing methods, achieving a median F1-score improvement that ranges from +6% to +31%. These findings underscore the effectiveness of SCC-GPT in enhancing code snippet classification, offering a cost-effective and efficient solution for developers who rely on SO for programming assistance.

Prosthetic home intervention induces cortical plasticity in paediatrics with congenital limb reduction.

Paediatrics with congenital upper-limb reduction deficiency often face difficulties with normal development such as motor skills, needing assistance with daily activities such as self-care limitations with certain movements, sports, or activities. The purpose of this non-randomized longitudinal controlled trial was to assess, using intent-to-treat analysis, the effects of an 8-week home intervention of prosthetic use on the sensorimotor cortex in paediatrics with congenital upper-limb reduction deficiency. A paediatric population with congenital upper-limb reduction deficiency (n = 14) who were aged 6-18 years and who had a 20° or greater range of motion in the appropriate joint of the affected arm to move the body-powered prosthesis were enrolled. An age- and sex-matched control group (n = 14) was also enrolled. Participants were non-randomized and fitted with a custom low-cost 3D printed prosthesis and participated in 8 weeks of prosthetic use training at home. Control participants utilized a prosthetic simulator. The home intervention incorporated daily use training and exercises utilizing the prosthesis in direct use and assistive tasks explained by the researchers. After the home intervention, both groups displayed significant improvements in gross manual dexterity. During prosthetic use with the affected limb, significant increases in oxygenated hemodynamic responses were only displayed in the left premotor cortex of the upper-limb reduction deficiency group. The novel findings of this non-randomized longitudinal controlled trial suggest that the intervention may have improved the functional role of the left hemisphere which translated to the improvement of learning direction during adaptation to visuomotor control. The prosthetic home intervention was assumed to provide closed-loop training which could provide a direct benefit to the motor development of paediatrics with upper-limb reduction deficiency.

Innovative Solutions for Gallery Administration: A Case Study of Modern Dashboards

Abstract : Efficient management solutions are necessary for the complicated and numerous responsibilities involved in the administration of art galleries and exhibitions. The design, use, and effects of advanced dashboard administration interfaces for gallery management are examined in this study. The study looks at the key components of a successful gallery dashboard, such as user experience, data integration, security, and user interface design. The study used a mixed-methods approach, combining user surveys, case studies, and expert interviews to identify the critical elements of effective gallery administration. The results show that well-designed dashboards greatly increase operational effectiveness, facilitate better decision-making, and offer strong assistance for curatorial tasks. The study ends with suggestions for creating and putting into practice gallery dashboard systems that can adapt to the changing requirements of contemporary art galleries, stressing the significance of customization, both user-centric design and scalability. This study adds to the body of knowledge about digital technologies in art management and provides useful information for software developers and gallery managers. Keywords – DotNet , Php, WordPress , Steamlight.

A systematic review of collaborative robots for nurses: where are we now, and where is the evidence?

Introduction: Robots present an opportunity to enhance healthcare delivery. Rather than targeting complete automation and nurse replacement, collaborative robots, or "cobots", might be designed to allow nurses to focus on high-value caregiving. While many institutions are now investing in these platforms, there is little publicly available data on how cobots are being developed, implemented, and evaluated to determine if and how they support nursing practice in the real world. Methods: This systematic review investigates the current state of cobotic technologies designed to assist nurses in hospital settings, their intended applications, and impacts on nurses and patient care. A comprehensive database search identified 28 relevant peer-reviewed articles published since 2018 which involve real studies with robotic platforms in simulated or actual clinical contexts. Results: Few cobots were explicitly designed to reduce nursing workload through administrative or logistical assistance. Most included studies were designed as patient-centered rather than nurse-centered, but included assistance for tasks like medication delivery, vital monitoring, and social interaction. Most applications emerged from India, with limited evidence from the United States despite commercial availability of nurse-assistive cobots. Robots ranged from proof-of-concept to commercially deployed systems. Discussion: This review highlights the need for further published studies on cobotic development and evaluation. A larger body of evidence is needed to recognize current limitations and pragmatic opportunities to assist nurses and patients using state-of-the-art robotics. Human-centered design can assist in discovering the right opportunities for cobotic assistance. Committed research-practice partnerships and human-centered design are needed to guide the technical development of nurse-centered cobotic solutions.

A Review on Artificial Intelligence in Pharmacy

This abstract provides a concise overview of the applications, benefits, and challenges of artificial intelligence (AI) in the pharmaceutical industry. AI technologies are revolutionizing drug discovery, clinical trials, personalized medicine, drug manufacturing, and more. While AI offers advantages such as error minimization, assistance in complex tasks, and continuous operation, challenges including the need for extensive training data and high costs must be addressed. Despite these limitations, AI holds significant promise in transforming the pharmaceutical landscape, enhancing efficiency, and improving patient outcomes.

Work-related musculoskeletal pain among physical therapists: a cross-sectional study in Kyoto and Shiga prefectures, Japan.

This study aimed to determine the actual working conditions and prevalence of musculoskeletal pain among physical therapists in Japan, and the risk factors associated with low back pain (LBP) and upper limb pain (ULP). A cross-sectional study of physical therapists in the Kyoto and Shiga prefectures was conducted using a self-administered questionnaire. The survey contents included questions regarding personal, work-related, and musculoskeletal pain factors. Logistic regression models were used to analyze factors associated with LBP and ULP. Responses from 1479 participants were included in the analyses. The prevalence of LBP at the time of the survey and in the past year was 40.1% and 74.3% in females, and 37.8% and 69.9% in males, respectively. The prevalence of ULP was 34.8% and 64.2% in females, and 27.2% and 53.3% in males. The numbers of patients who performed manual therapy per day, worked using a fixed-height bed, performed manual therapy, had job dissatisfaction and stress, were over 40years old, and slept less than 6hours were associated with LBP and ULP. Assistance task was a risk factor only for LBP, and female sex a risk factor only for ULP. The prevalence of LBP and ULP among physical therapists in Japan was as high as that in nurses and care workers. Work-related factors associated with LBP and ULP were identified among physical therapists. Thus, to prevent work-related musculoskeletal disorders, measures to reduce the physical burden from the perspective of occupational health are required.

Desktop Assistant using Machine Learning

The post-2017 era witnessed a surge in desktop assistants and voice searches, driven by advancements in technology, notably in mobile phones and desktops. Breakthroughs in AI, NLP, and ML empowered devices to understand and respond to human commands effectively, laying the groundwork for voice-driven functionalities. Mobile phones and desktops evolved as central hubs for digital activities, necessitating seamless integration and user-friendly interfaces. Desktop assistants serve as bridges, facilitating intuitive interaction. The demand for faster and more reliable input led to the development of desktop assistants leveraging voice recognition technology, offering hands-free interaction. Desktop assistants make technology interaction convenient and enjoyable by incorporating natural language understanding, allowing fluid conversations and enhancing user experience. AI plays a crucial role, enabling assistants to comprehend context, learn, and adapt to preferences, providing personalized and contextually relevant services. Their applications span personal productivity and smart home automation, offering assistance in tasks like setting reminders and controlling connected devices. Despite advancements, challenges like privacy concerns and data security require attention. Balancing convenience and privacy remains a paramount challenge. Future trends include enhancements in NLP, increased personalization, and integration with emerging technologies like AR and VR, promising more immersive interactions. Keywords: Voice searches, Advancements in technology, AI, ML, User-friendly interfaces, Voice recognition technology, Natural language understanding, Personalized services

Pictorial Task Assistance System using Electroencephalography Signals

Neuromuscular disorders are a significant health problem globally. Patients may experience paralysis, muscle weakness, and communication problems because of these disorders. We propose a Pictorial Task Assistance System to help patients with communication issues using Electroencephalography (EEG). We developed an interface for patients containing an image of food and water. We collected EEG data from 25 healthy students using the Muse headset and Muse monitor app for our study, while they selected one of the images. The EEG data was used to train three supervised machine-learning algorithms for classification. The labels were acquired manually from participants. Using 10-fold cross-validation the results demonstrated that the Random Forest (RF) classifier achieved 88% accuracy, K-Nearest Neighbors (KNN) 80%, and 76% accuracy in Logistic Regression (LR) in the classification of food and water images. These results suggest that the proposed system has the potential to be a useful tool for patients suffering from neuromuscular disorders to perform communication for their necessary tasks.

Effective Assistance for Iterative Visual Search Tasks by Using Gaze-Based Visual Cognition Estimation

Effective Assistance for Iterative Visual Search Tasks by Using Gaze-Based Visual Cognition Estimation

Empowering the Blind: An AI Driven Indoor Assistance for Visually Impaired

Abstract: This review paper presents the design and development of a comprehensive mobile application aimed at enhancing the daily lives of visually impaired individuals. The proposed mobile app offers real-time assistance for various visual recognition tasks, including object detection, distance estimation, currency recognition, barcode detection, color recognition, and emotion analysis. The primary objective of this application is to provide visually impaired users with a powerful tool to navigate their surroundings, identify objects and currency, scan barcodes, discern colors, and even gauge the emotions of those they interact with. The app integrates pre-trained deep learning models for object detection and facial emotion analysis, computer vision techniques for distance estimation, and optical character recognition for currency recognition. User feedback and engagement are central to the ongoing improvement of the application, ensuring that it remains a valuable resource for the community it serves. Ethical considerations and privacy concerns are also addressed, with a focus on data security and user privacy. By presenting the development and functionalities of this app, this review paper not only contributes to the field of assistive technology but also underscores the importance of harnessing cutting-edge technology to improve the quality of life for visually impaired individuals.

Examination of the relationship between tasks in geriatric health service facilities and benefits of working for older assistant care workers

Objective In this study, we clarified the characteristics of tasks performed by older assistant care workers at geriatric health services facilities, by individual characteristics and work status. Additionally, we examined the relationship between the tasks and benefits of working for older assistant care workers.Methods A self-administered questionnaire survey was provided to 2,571 elderly care facilities employing assistant care workers aged ≥60. A total of 1,606 responses were obtained, and personal characteristics, working conditions, tasks, and benefits of working for older care assistants were surveyed. Thirteen work tasks were established and categorized into four broad categories; user transfer assistance, facility maintenance, meal-related assistance, and managing and talking to users. Seven benefits of working as care assistants were "contribution to society," "social connection," "purpose of life," "income," "learning from caregiving," "health maintenance and promotion," and "time utilization."Results Most of the respondents were women and relatively young workers. Among tasks, men, younger workers, and those working >5 days weekly represented a high proportion of those performing user transfer assistance tasks. Tasks on managing and talking to users were not correlated with sex, age, and number of days worked, but those with more flexible work patterns were more likely to be engaged in such tasks. Logistic regression analysis showed that user transfer assistance was correlated with benefits of working, such as "contribution to society," "social connection," and "learning from caregiving." Moreover, facility maintenance was associated with "income," "health maintenance and promotion," and "time utilization" and meal-related assistance was associated with "learning from caregiving." Managing and talking to users was associated with "contribution of society," "social connection," "purpose of life," and "learning from caregiving."Conclusion The tasks of the workers were associated with individual characteristics and work status. The perception of benefits between the working tasks that involved frequent contact with users and those that did not were considerably different.

Task Analysis in Human-Computer Interaction: A Comparison between Four Task Analysis Techniques

Task analysis is an integral part of human-computer interaction, as it permits the client to comprehend, complete, and benefit from the utilization of an interaction system. Task analysis methodologies differ based on the selected approach and the motivation behind its use. The present study aims to compare several task analysis methodologies (i.e. hierarchical task analysis, cognitive task analysis, GOMS task analysis, and emotion task analysis), initially outlining their commonalities and distinctions in structure and usability. After that, it will go into each technique's benefits and drawbacks and how well-suited it is for different jobs. The main results that have been obtained by this research are: the HTA technique is an appropriate selection when the analysis seeks to explore the connections and interdependencies among different components of the task, whereas CTA is appropriate for the analysis that necessitates a technique with an undefined structure comprising of interviews. If the analysis system seeks to identify a cognitive process with a clearly defined structure, the GOMs technique can be a superb option. Lastly, when the task analysis aims to comprehend the emotional aspects of task performance, the ETA serves as an effective instrument to employ. In terms of usability, HTA is widely utilized in the fields of task assistance design and error prediction. CTA proves to be beneficial for tasks that demand sophisticated cognitive functions, GOMS is typically employed in the creation of training tools, the design of assist frameworks, and the documentation for clients, and ETA examines how users' psychological or emotional states influence their responses to tasks as they complete them. It is advised that readers go over this comparison paper in order to obtain a better knowledge of how to select the precise strategy that can aid in finishing an assignment.