User Assistance Research Articles

Path planning for assisting blind people in purposeful navigation

Blind <span>people present dificulties for reaching objects of interest in the daily life. In this sense, the integration of a path planning module for assisting blind people in purposeful navigation is noteworthy. In this work, we present an algorithm that leverages the high capability of an embedded computer with graphics processing unit (GPU) NVIDIA Jetson TX2 for computing optimal paths to objects of interest. The algorithm computes the optimal path to the objective, considering changes in the environment and changes in the position of the user. The algorithm is efficient for computing new paths when the environment changes by reusing parts of previous computations. In order to compare the performance, the algorithms were implemented and evaluated in MATLAB, C++ and CUDA, for different size of the grid and percentage of unknown obstacles. We found that the implementation on GPU has a speed up of 20 times W.R.T the implementation in C++ and more than 400 times W.R.T the implementation in MATLAB. These results boost us to integrate this module to our main system based on a stereo camera and a haptic belt and so provide to the user assistance in purposeful navigation at real time.</span>

SkillFence

Voice assistants are deployed widely and provide useful functionality. However, recent work has shown that commercial systems like Amazon Alexa and Google Home are vulnerable to voice-based confusion attacks that exploit design issues. We propose a systems-oriented defense against this class of attacks and demonstrate its functionality for Amazon Alexa. We ensure that only the skills a user intends execute in response to voice commands. Our key insight is that we can interpret a user's intentions by analyzing their activity on counterpart systems of the web and smartphones. For example, the Lyft ride-sharing Alexa skill has an Android app and a website. Our work shows how information from counterpart apps can help reduce dis-ambiguities in the skill invocation process. We build SkilIFence, a browser extension that existing voice assistant users can install to ensure that only legitimate skills run in response to their commands. Using real user data from MTurk (N = 116) and experimental trials involving synthetic and organic speech, we show that SkillFence provides a balance between usability and security by securing 90.83% of skills that a user will need with a False acceptance rate of 19.83%.

Perspectives of visualization onboarding and guidance in VA

A typical problem in Visual Analytics (VA) is that users are highly trained experts in their application domains, but have mostly no experience in using VA systems. Thus, users often have difficulties interpreting and working with visual representations. To overcome these problems, user assistance can be incorporated into VA systems to guide experts through the analysis while closing their knowledge gaps. Different types of user assistance can be applied to extend the power of VA, enhance the user’s experience, and broaden the audience for VA. Although different approaches to visualization onboarding and guidance in VA already exist, there is a lack of research on how to design and integrate them in effective and efficient ways. Therefore, we aim at putting together the pieces of the mosaic to form a coherent whole. Based on the Knowledge-Assisted Visual Analytics model, we contribute a conceptual model of user assistance for VA by integrating the process of visualization onboarding and guidance as the two main approaches in this direction. As a result, we clarify and discuss the commonalities and differences between visualization onboarding and guidance, and discuss how they benefit from the integration of knowledge extraction and exploration. Finally, we discuss our descriptive model by applying it to VA tools integrating visualization onboarding and guidance, and showing how they should be utilized in different phases of the analysis in order to be effective and accepted by the user.

Next2You: Robust Copresence Detection Based on Channel State Information

Context-based copresence detection schemes are a necessary prerequisite to building secure and usable authentication systems in theInternet of Things (IoT). Such schemes allow one device to verify proximity of another device without user assistance utilizing their physical context (e.g., audio). The state-of-the-art copresence detection schemes suffer from two major limitations: (1) They cannot accurately detect copresence in low-entropy context (e.g., empty room with few events occurring) and insufficiently separated environments (e.g., adjacent rooms), (2) They require devices to have common sensors (e.g., microphones) to capture context, making them impractical on devices with heterogeneous sensors. We address these limitations, proposingNext2You, a novel copresence detection scheme utilizing channel state information (CSI). In particular, we leverage magnitude and phase values from a range of subcarriers specifying a Wi-Fi channel to capture a robust wireless context created when devices communicate. We implementNext2Youon off-the-shelf smartphones relying only on ubiquitous Wi-Fi chipsets and evaluate it based on over 95 hours of CSI measurements that we collect in five real-world scenarios.Next2Youachieves error rates below 4%, maintaining accurate copresence detection both in low-entropy context and insufficiently separated environments. We also demonstrate the capability ofNext2Youto work reliably in real-time and its robustness to various attacks.

A Survey of Basic Daily Living Assistance in Dependency Units during the Morning First Period at Nursing Homes with a Healthcare Dysfunction.

(1) Background: It is important to evaluate the attention in the basic activities of daily life in the early hours of the day to evaluate the quality of care and to be able to increase the attention of human resources in case of observing an increase in dependency. The purpose was to improve healthcare quality in nursing homes, correctly identifying the work burden and incidents of daily planning, and completing the work plan by nursing assistant staffing. (2) Methods: The sample is based on 70 elderly people. The analysis used an observational trial every workday over a six-month period. An ad hoc sheet was prepared to collect socio-demographic data on each participant, and the Barthel Index was applied to the study subjects. A daily record of three basic activities of daily living (BADL), such as dressing, bathing, and eating, was kept. (3) Results: Our results showed a significant evolution in both units, but it was in the psychogeriatric unit in which higher compliance with the schedule and higher maintained stability was reached. (4) Conclusions: The use of some BADL registers helped us address situations of imbalance in terms of user assistance and establish an interdisciplinary communication with the nursing team as a way of achieving better organization and compliance with care protocols.

Перспективы мониторинга внутриглазного давления с помощью транспальпебральной тонометрии

Purpose: evaluate the safety and convenience of self-use by users of the intraocular pressure tonometer TVGD-02 in home practice without the involvement of medical professionals. Material and methods. 42 people formed 30 pairs: user-patient and user-assistant. In 18 cases (9 pairs), each person from the pair was in the role of both a patient and an assistant. The participants were independently trained at home in the technique of transpalpebral measurement of intraocular pressure (IOP), tested on the control circuit of the device and measured IOP to the user-patient. In the questionnaire, users assigned points, rating the convenience and safety of use of the tonometer. Results. Among the assistant users, individuals were represented (59.68±17.2 [22; 83] years) with different educational levels. Of the patient users (60.64±17.1 [22; 82] years), 82.14% of the study participants were persons with ocular pathology, and 35.7% of the patients had glaucoma with concomitant pathology. In 100% of cases, the participants were able to independently figure out how to properly prepare the tonometer for operation and perform IOP measurement. The accuracy and reproducibility of the measurement results significantly increased with the improvement of the skills of working with the tonometer: on the 1st day, the dispersion in the series of control measurements was 2.07±1.7 [7; 0] mm Hg. article, and IOP of the user-patient of 2.89±1,91 [8; 0], and on the 5th day, the measurement accuracy is significantly increased, and dispersion decreased to 1.21±0,72 [2; 0] and 0.85±0,69 [2; 0] mm Hg. accordingly. Users rated the ease of performing various stages of using the device from 4.21±0.67 to 5.0±0.0 points. Conclusion. Transpalpebral tonometry using the TVGD-02 tonometer makes it possible to adequately assess IOP in patients with assisted tonometry in home practice. Independent study of information about the principles of operation of the device and the method of working with it makes it possible to acquire the skills of using the tonometer without the participation of medical personnel.

A Baseline Statistical Method for Robust User-Assisted Multiple Segmentation

Recently, several image segmentation methods that welcome and leverage different types of user assistance have been developed. In these methods, the user inputs can be provided by drawing bounding boxes over image objects, drawing scribbles or planting seeds that help to differentiate between image boundaries or by interactively refining the missegmented image regions. Due to the variety in the types and the amounts of these inputs, relative assessment of different segmentation methods becomes difficult. As a possible solution, we propose a simple yet effective, statistical segmentation method that can handle and utilize different input types and amounts. The proposed method is based on robust hypothesis testing, specifically the DGL test, and can be implemented with time complexity that is linear in the number of pixels and quadratic in the number of image regions. Therefore, it is suitable to be used as a baseline method for quick benchmarking and assessing the relative performance improvements of different types of user-assisted segmentation algorithms. We provide a mathematical analysis on the operation of the proposed method, discuss its capabilities and limitations, provide design guidelines and present simulations that validate its operation.

Towards a Novel Software Framework for the Intuitive Generation of Process Flows for Multiple Robotic Systems

To make the process-specific programming of robotic and technical systems more efficient, we introduce the concept of a novel software framework to enable intuitive configuration of cooperative process flows for multiple systems. Our concept allows both the easy programming of systems and the generation of process flows with multiple systems to enable cooperation between them. Following the idea of Operator 4.0 our framework thus enables non-expert workers of industrial companies to effectively control the process flows of a production or logistical process with minimal training effort and costs.In this paper, we first analyze existing software frameworks that facilitate graphical programming and differentiate our approach from them. We explain in detail how our approach conceptually and technically works regarding the framework´s technical architecture, system requirements, interface design, user assistance methods based on digital twin and data analysis, as well as interaction patterns.We validate the functionality and generality of our software framework by integrating two systems of different manufacturers – a collaborative robot and a robotic gripper – into our framework and discuss the technical capabilities of our prototype. In addition, the interaction of unskilled workers and our framework will be compared to an industrial robot teach pendant in a case study in which the participants had to create a pick-and-place process flow.

Adaptive Control Method for Dynamic Synchronization of Wearable Robotic Assistance to Discrete Movements: Validation for Use Case of Lifting Tasks

Dynamic control of robotic exoskeletons is paramount to ensuring safe, synergistic assistive action of functional benefit to users. To date, exoskeleton controllers have excelled in rhythmic and quasi-rhythmic tasks, whereas control methods for assisting discrete movements remain limited by their task-specificity. Inspired by neurophysiological dynamic movement primitives (DMPs), we formulated a novel controller that facilitated a variety of lifting movements using a single <i>adaptive</i> DMP (aDMP), for wearable robotic assistance of discrete movements. For a variety of load lifting tasks, we first benchmarked our method&#x0027;s trajectory prediction accuracy against the state-of-the-art DMP using passively recorded exoskeleton sensor data (offline), followed by a functional validation of online aDMP trajectory estimates. Finally, we assessed the functional effects of aDMP-based exoskeletal assistance on joint kinematics and muscular activity during repetitive lifting. The new aDMP method accurately predicted and smoothly synchronized robotic assistance with variable movement trajectories, resulting in reduced muscular activation of the erector spinae muscles (up to 47.6&#x0025;) while preserving lower-limb joint kinematics and reducing the extension time by 15.5&#x0025; compared to unassisted conditions. This method holds promise for use in a wide range of wearable robotic applications, including both clinical rehabilitation and user assistance in activities of daily living and&#x002F;or manual labor.

Two-Stage Adaptive Relay Selection and Power Allocation Strategy for Cooperative CR-NOMA Networks in Underlay Spectrum Sharing

In this paper, we consider a novel cooperative underlay cognitive radio network based on non-orthogonal multiple access (CR-NOMA) with adaptive relay selection and power allocation. In secondary networks, dedicated relay assistance and user assistance are used to achieve communication between the base station and the far (and near) user. Here, a two-stage adaptive relay selection and power allocation strategy is proposed to maximize the achievable data rate of the far user while ensuring the service quality of near user. Furthermore, the closed-form expressions of outage probability of two secondary users are derived, respectively, under interference power constraints, revealing the impact of transmit power, number of relays, interference threshold and target data rate on system outage probability. Numerical results and simulations validate the advantages of the established cooperation and show that the proposed adaptive relay selection and power allocation strategy has better outage performance.

Temporal feature adaptive non-intrusive load monitoring via unsupervised probability density evolution

Toward the smart power and energy consumption, non-intrusive load monitoring is emerging as the promising technical assistance of intelligent energy user. The load behaviors of individual power users are distinct, that is potential to enhance the monitoring performance if effectively addressed. In this paper, the customized temporal behaviors are thoroughly investigated and utilized for load disaggregation from the view of time characteristics. At the first stage, the temporal features of appliance usage are formularized via customized time of use probability, and the model is adaptive for the specific user habit via unsupervised probability density evolution method. Then, a generic two-stage load disaggregation framework is proposed, where the primary stage is formulized by dictionary learning and for basic load disaggregation, and the secondary stage is integrated with probabilistic temporal weights and for optimal disaggregation decision. Lastly, the sparse coding principle and risk analysis theory are employed for the robust problem solution. By comprehensive verifications on low voltage networks simulator, it is demonstrated that the proposed approach is effective in temporal load feature modeling, and thereby achieving the higher accuracy and flexibility for the non-intrusive load monitoring problem.

Exploring Interactions Between Trust, Anthropomorphism, and Relationship Development in Voice Assistants

Modern conversational agents such as Alexa and Google Assistant represent significant progress in speech recognition, natural language processing, and speech synthesis. But as these agents have grown more realistic, concerns have been raised over how their social nature might unconsciously shape our interactions with them. Through a survey of 500 voice assistant users, we explore whether users' relationships with their voice assistants can be quantified using the same metrics as social, interpersonal relationships; as well as if this correlates with how much they trust their devices and the extent to which they anthropomorphise them. Using Knapp's staircase model of human relationships, we find that not only can human-device interactions be modelled in this way, but also that relationship development with voice assistants correlates with increased trust and anthropomorphism.

Artificial Intelligence Service Agents: Role of Parasocial Relationship

ABSTRACT Increased use of artificial intelligence service agents (AISA) has been associated with improvements in AISA service performance. Whilst there is consensus that unique forms of attachment develop between users and AISA that manifest as parasocial relationships (PSRs), the literature is less clear about the AISA service attributes and how they influence PSR and the users’ subjective well-being. Based on a dataset collected from 408 virtual assistant users from the US, this research develops and tests a model that can explain how AISA-enabled service influences subjective well-being through the mediating effect of PSR. Findings also indicate significant gender and AISA experience differences in the PSR effect on subjective well-being. This study advances current understanding of AISA in service encounters by investigating the mediating role of PSR in AISA’s effect on users’ subjective well-being. We also discuss managerial implications for practitioners who are increasingly using AISA for delivering customer service.

Study on fog computing: security & privacy challenges in terms of IoT

As of late, the idea of (IoT) Internet of Things is pulling in strong consideration because of the gigantic potential. IoT is the systems administration of physical devices that are both associated and smart. Threats and Security issues are a constant problem faced in cloud computing these days, but in total assurance it’s not yet completely researched. Cisco made the term ‘Fog Computing’ which is nothing but an extension of Cloud Computing to the system edge. Along with the enormous advancements in IoT, current Cloud frameworks face different disadvantages, for example, absence of versatility support, area mindfulness, geo-dissemination, high latency, just as digital dangers. Fog computing extends cloud computing, cloud computing give data, figure, stockpiling, and application administrations to end-user, additionally the fog computing likewise offer the types of assistance like process, data, application and capacity to end user. In this manner, fog computing might be viewed as decision for empowering the IoT to offer proficient and safe types of assistance for some IoT users. This paper dissects the difficulties and issues that are looked by Fog calculation regarding privacy and security in detail.

Creating and Capturing Artificial Emotions in Autonomous Robots and Software Agents

This paper presents ARTEMIS, a control system for autonomous robots or software agents. ARTEMIS can create human-like artificial emotions during interactions with their environment. We describe the underlying mechanisms for this. The control system also captures its past artificial emotions. A specific interpretation of a knowledge graph, called an Agent Knowledge Graph, stores these artificial emotions. ARTEMIS then utilizes current and stored emotions to adapt decision making and planning processes. As proof of concept, we realize a concrete software agent based on the ARTEMIS control system. This software agent acts as a user assistant and executes their orders and instructions. The environment of this user assistant consists of several other autonomous agents that offer their services. The execution of a user’s orders requires interactions of the user assistant with these autonomous service agents. These interactions lead to the creation of artificial emotions within the user assistant. The first experiments show that it is possible to realize an autonomous user assistant with plausible artificial emotions with ARTEMIS and record these artificial emotions in its Agent Knowledge Graph. The results also show that captured emotions support successful planning and decision making in complex dynamic environments. The user assistant with emotions surpasses an emotionless version of the user assistant.

Social support networks of users of crack cocaine and the role of a Brazilian health program for people living on the street: A qualitative study

This cross-sectional qualitative study analyzed characteristics of social support for users of crack cocaine and the role of “Consultório na Rua” (CR), or “Office in the Street,” a Brazilian program for people living on the street. Data were collected using 1) ethnographic field observations during the delivery of services from this program, 2) in-depth interviews with 17 users of crack cocaine, and 3) a focus group with professionals from CR. To analyze data, we used content analysis and analytical categories based on Social Network Analysis (SNA) theoretical statements. Results showed that family, peers, community members, and professionals from CR were the main social support providers. Participants mentioned receiving material, informational, and emotional support from CR members. It was observed that CR had a welcoming and inclusive approach, but CR team members identified challenges related to stigma directed toward people who use substances and live on the street. CR assumed a central role in the health and social assistance of users of crack cocaine living on the street, providing an important link to healthcare and social services. However, initiatives related to motivation to receive mental health services, treatment, or social reintegration were not observed in conjunction with this program.

Prospects of using the TVGD-02 tonometer for assisted monitoring of intraocular pressure at home

Purpose: to evaluate the safety and convenience of self-using the intraocular pressure tonometer TVGD-02 at home with no participation of medical professionals. Material and methods. 42 people formed 30 pairs: a patient and an assistant. In 9 pairs, each person played both roles. The participants practiced the technique of transpalpebral measurement of intraocular pressure (IOP) at home, and, as assistants, measured IOP of their partner patients. All subjects were asked to fill in a questionnaire to evaluate the convenience and safety of the tonometer use by a 5-point system. Results. The assistant users, aged 22 to 83 (ave. 59.68 ± 17.20 years) had diverse educational levels. Of the patient users, aged 22 to 82 (ave. 60.64 ± 17.10 years), 82.14 % had ocular pathology, while 35.7 % had glaucoma with concomitant pathology. In 100 % of cases, the participants required no professional assistance to properly prepare the tonometer for operation and perform IOP measurement. The accuracy and reproducibility of measurement results significantly increased with the assistants’ gaining skills of operating the tonometer. On the 1st day, the data obtained in a series of measurements scattered for the control block within 2.07 ± 1.70 [7; 0] mm Hg, while the measured IOP of the patient scattered within 2.89 ± 19.10 [8; 0]. In contrast, on the 5th day, measurement accuracy increased significantly and the scatter dropped to 1.21 ± 0.72 [2; 0] and 0.85 ± 0.69 [2; 0] mm Hg, respectively. The users rated the ease of using the device at different staged from 4.21 ± 0.67 to 5.0 ± 0.0 points. Conclusion. Assisted transpalpebral tonometry with the TVGD-02 tonometer ensures an adequate assessment of IOP in patients at home. The principles of device operation and the technique can be learned and skills can be gained without the participation of medical personnel.

Parenting with Assistance – The Views of Disabled Parents and Personal Assistants

This qualitative study, based on semi-structured interviews with eight parents with disabilities and five personal assistants, explores two different but interrelated perspectives: how parents with extensive physical disabilities use personal assistants in their parenting strategies and how personal assistants experience assisting in parenting strategies. The assistance users’ parenting strategies are affected by gender, age of the children and whether the disabilities were congenital or acquired later in life. The assistants were seen as enablers, competitors for the child’s love or compensators. Access to personal assistance has increased parents’ possibilities to be active in their parenting. However, total adaptation to the assistance user’s parenting strategies could be challenging for assistants with different parenting ideals. There is a need for discussions on how assistants can work to strengthen parenting roles as well as receive support to work in a sustainable way.

AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing

Thanks to the popularity of personal mobile devices, more and more of the different types of private content, such as images and videos, are shared on social networking applications. While content sharing may be an effective practice to enhance social relationships, it is also a source of relevant privacy issues. Unfortunately, users find it difficult to understanding the terms and implications of the privacy policies of apps and services. Moreover, taking privacy decisions about content sharing on social networks is cumbersome and prone to errors that could determine privacy leaks. In this paper, we propose two techniques aimed at supporting the user in taking privacy choices about sharing personal content online. Our techniques are based on machine learning and natural language processing to analyze privacy policies, and on computer vision to assist the user in the privacy-conscious sharing of multimedia content. Experiments with real-world data show the potential of our solutions. We also present ongoing work on a system prototype and chatbot for natural language user assistance.

Effective Clustering of scRNA-seq Data to Identify Biomarkers without User Input

Clustering unleashes the power of scRNA-seq through identification of appropriate cell groups. It is considered a pre-requisite to performing differential expression analysis, followed by functional profiling to identify potential biomarkers from scRNA-seq data. Most existing clustering methods either integrate cluster validity indices or need user assistance to identify clusters of arbitrary shape. We develop two clustering methods 1) UIFDBC to identify clusters of arbitrary shapes, 2) UIPBC to cluster scRNA-seq data. Neither method integrates a cluster validity index nor takes any user input. However, specialised approaches are used to benchmark the parameters. Both approaches outperform state-of-the-art methods.