Automatic Fall Risk Detection based on Imbalanced Data

In recent years, the declining birthrate and aging population have gradually brought countries into an ageing society. Regarding accidents that occur amongst the elderly, falls are an essential problem that quickly causes indirect physical loss. In this paper, we propose a pose estimation-based fall detection algorithm to detect fall risks. We use body ratio, acceleration and deflection as key features instead of using the body keypoints coordinates. Since fall data is rare in real-world situations, we train and evaluate our approach in a highly imbalanced data setting. We assess not only different imbalanced data handling methods but also different machine learning algorithms. After oversampling on our training data, the K-Nearest Neighbors (KNN) algorithm achieves the best performance. The F1 scores for three different classes, Normal, Fall, and Lying, are 1.00, 0.85 and 0.96, which is comparable to previous research. The experiment shows that our approach is more interpretable with the key feature from skeleton information. Moreover, it can apply in multi-people scenarios and has robustness on medium occlusion

Snapshot of Research Issues in Service Robots

A service (social) robot is defined as the Internet of Things (IoT) consisting of a physical robot body that connects to one or more Cloud services to facilitate human-machine interaction activities to enhance the functionality of a traditional robot. Many studies found that anthropomorphic designs in robots resulted in greater user engagement. Humanoid service robots usually behave like natural social interaction partners for human users, with emotional features such as speech, gestures, and eye-gaze, referring to the users’ cultural and social background. During the COVID-19 pandemic, service robots play a much more critical role in helping to safeguard people in many countries nowadays. This paper gives an overview of the research issues from technical and social-technical perspectives, especially in Human-Robot Interaction (HRI), emotional expression, and cybersecurity issues, with a case study of gamification and service robots

Security Assessment for Zenbo Robot Using Drozer and mobSF Frameworks

These days, almost everyone has been entirely relying on mobile devices and mobile related applications running on Android Operating Systems, the most used Mobile Operating System in the world with the largest market share. These Mobile devices and applications can become an information goldmine for hackers and are considered one of the significant concerns mobile users face who stand a chance of being victimized during data breach from hackers due to lapse in information security and controls. Such challenge can be put to bare through systematic digital forensic analysis through penetration testing for a humanoid robot like Zenbo, which run Android OS and related application, to help identify associated security vulnerabilities and develop controls required to improve security using popular penetration testing tools such as Drozer, Mobile Application Security framework (mobSF), and AndroBugs with the help of Santoku Linux distribution

Institutional Violence Complaints in Argentina: A Privacy Study

Argentina is a federal republic located in South America. Despite Argentina’s redemocratization in 1983, conditions favoring human rights abuses still persist. Institutional violence refers to structured practices of human rights violation by state officials belonging to public institutions. In this paper, we outline and discuss privacy issues in institutional violence complaints in Argentina. To this aim, we defined a BPMN process model for registering victims’ complaints in a database, and proposed an approach to investigate the privacy of such process from a threat modeling perspective. With the approach, we identified privacy threats of information disclosure and content unawareness, and defined privacy requirements and controls needed to mitigate these threats

Towards a Tangible Blocky Coding Design for Visually Impaired Children

This paper investigates the role of coding in children\u27s education and the barriers to inclusion for Visually Impaired (VI) children in this environment. In the learning process, these children face numerous challenges, including the fact that most playful coding-related learning options rely on visualization. This paper incorporates insights from a literature review to design a solution involving a model called DODO through computer vision and tangible paper blocky modules for VI children to learn to code. The main result is the proof of concept prototype demonstrates that the proposed design can recognize the various paper modules used to represent different codes, targeting the tactile sense. The design advocates for VI children to have access to economic Science, Technology, Engineering, and Mathematics (STEM) learning

Forensic investigation of small-scale digital devices: a futuristic view

Small-scale digital devices like smartphones, smart toys, drones, gaming consoles, tablets, and other personal data assistants have now become ingrained constituents in our daily lives. These devices store massive amounts of data related to individual traits of users, their routine operations, medical histories, and financial information. At the same time, with continuously evolving technology, the diversity in operating systems, client storage localities, remote/cloud storages and backups, and encryption practices renders the forensic analysis task multi-faceted. This makes forensic investigators having to deal with an array of novel challenges. This study reviews the forensic frameworks and procedures used in investigating small-scale digital devices. While highlighting the challenges faced by digital forensics, we explore how cutting-edge technologies like Blockchain, Artificial Intelligence, Machine Learning, and Data Science may play a role in remedying concerns. The review aims to accumulate state-of-the-art and identify a futuristic approach for investigating SSDDs

Perceived Innovativeness and Privacy Risk of Smart Toys in Brazil and Argentina

A smart toy, such as Hello Barbie, is a device consisting of a physical toy component that connects to a computing system with online services through networking to enhance the functionality of a traditional toy. Whilst these are new educational and entertaining values of smart toys, experts in western countries such as U.S. and Germany have warned consumers of the data security and privacy issues of these toys. In this preliminary research study, we particularly studied Brazilian and Argentinian consumers’ perceived innovativeness, risks and benefits of smart toys and their purchase intention toward such toys. Results indicate that Brazilian consumers have better perception and evaluation of the toy and thus higher purchase intention than Argentinian consumers do. Such difference may be explained by the cultural differences be-tween the two countries, such as relatively low vs. high uncertainty avoidance

Recommendations for a smart toy parental control tool

Current smart toy parental control tools offered by toy companies do not adequately support parents in protecting their children. Moreover, there is no reference solution in the literature to be used by toymakers. Most studies are limited to mentioning the importance and purpose of these tools or some specific requirements. This article proposes a reference solution for smart toy parental control tools, with which parents can take control and adequately manage their children’s data according to their preferences. This reference solution comprises a recommended list of requirements, a conceptual model, and a prototype developed as a proof of concept for the solution. An analysis highlights that current legal standards for privacy protection do not satisfy the requirements raised in this study. Furthermore, the results of an evaluation conducted with experts show that the proposed solution is adequate to be used as a reference by academia and industry

Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces