Wearable Capacitive-based Wrist-worn Gesture Sensing System

Gesture control plays an increasingly significant role in modern human-machine interactions. This paper presents an innovative method of gesture recognition using flexible capacitive pressure sensor attached on user’s wrist towards computer vision and connecting senses on fingers. The method is based on the pressure variations around the wrist when the gesture changes. Flexible and ultrathin capacitive pressure sensors are deployed to capture the pressure variations. The embedding of sensors on a flexible substrate and obtain the relevant capacitance require a reliable approach based on a microcontroller to measure a small change of capacitive sensor. This paper is addressing these challenges, collect and process the measured capacitance values through a developed programming on LabVIEW to reconstruct the gesture on computer. Compared to the conventional approaches, the wrist-worn sensing method offerings a low-cost, lightweight and wearable prototype on the user’s body. The experimental result shows that the potentiality and benefits of this approach and confirms that accuracy and number of recognizable gestures can be improved by increasing number of sensor

PILOT: Password and PIN Information Leakage from Obfuscated Typing Videos

This paper studies leakage of user passwords and PINs based on observations of typing feedback on screens or from projectors in the form of masked characters that indicate keystrokes. To this end, we developed an attack called Password and Pin Information Leakage from Obfuscated Typing Videos (PILOT). Our attack extracts inter-keystroke timing information from videos of password masking characters displayed when users type their password on a computer, or their PIN at an ATM. We conducted several experiments in various attack scenarios. Results indicate that, while in some cases leakage is minor, it is quite substantial in others. By leveraging inter-keystroke timings, PILOT recovers 8-character alphanumeric passwords in as little as 19 attempts. When guessing PINs, PILOT significantly improved on both random guessing and the attack strategy adopted in our prior work [4]. In particular, we were able to guess about 3% of the PINs within 10 attempts. This corresponds to a 26-fold improvement compared to random guessing. Our results strongly indicate that secure password masking GUIs must consider the information leakage identified in this paper

Ethical problems of smart wearable devices

The stock market plays a major role in the entire financial market. How to obtain effective trading signals in the stock market is a topic that stock market has long been discussing. This paper first reviews the Deep Reinforcement Learning theory and model, validates the validity of the model through empirical data, and compares the benefits of the three classical Deep Reinforcement Learning models. From the perspective of the automated stock market investment transaction decision-making mechanism, Deep Reinforcement Learning model has made a useful reference for the construction of investor automation investment model, the construction of stock market investment strategy, the application of artificial intelligence in the field of financial investment and the improvement of investor strategy yield

Security and Privacy Analysis of Wearable Health Device

Wearable technology allows for consumers to record their healthcare data for either personal or clinical use via portable devices. As advancements in this technology continue to rise, the use of these devices has become more widespread. In this paper, we examine the significant security and privacy features of three health tracker devices: Fitbit, Jawbone and Google Glass. We also analyze the devices\u27 strength and how the devices communicate via its Bluetooth pairing process with mobile devices. We explore possible malicious attacks through Bluetooth networking. The outcomes of this analysis illustrate how these devices allow third parties to access sensitive information, such as the device exact location, which causes the potential privacy breach for users. We analyze and compare how unauthorized parties may access the user data and the challenges to secure user data on three wearable devices (Fitbit, Jawbone, and Google Glass) security vulnerability and attack type

A smart textile system to detect urine leakage

©2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper a wearable system with a sensor embroidered on a textile substrate to detect urine leaks is presented. The system consists of a moisture textile capacitive sensor together with the signal conditioning and its wireless transmission to the cloud. The proposed system has been integrated on underwear and hospital sheet to detect the urine leakage on the diaper users and critical ill patient, respectively. The methodology used by the microcontroller to measure the sensor value is a charge/discharge method. The information is visualised through a computer or smartphone, where can be seen the current state of the sensor. The system has a warning set up to communicate any urine leakage. The experimental results show the functionality of the proposed system which could supply a new tool to hospitals, nursing homes or other institutions to detect when the patient diaper or sheet need to be removed. This tool can optimise the hospital protocol and improve the patient quality of life.This work was supported by Spanish Government-MINECO under Project TEC2016-79465-R and AGAUR-UPC(2020 FIB 00028).Peer ReviewedPostprint (author's final draft

WristSpy: Snooping Passcodes in Mobile Payment Using Wrist-worn Wearables

Mobile payment has drawn considerable attention due to its convenience of paying via personal mobile devices at anytime and anywhere, and passcodes (i.e., PINs or patterns) are the first choice of most consumers to authorize the payment. This paper demonstrates a serious security breach and aims to raise the awareness of the public that the passcodes for authorizing transactions in mobile payments can be leaked by exploiting the embedded sensors in wearable devices (e.g., smartwatches). We present a passcode inference system, WristSpy, which examines to what extent the user's PIN/pattern during the mobile payment could be revealed from a single wrist-worn wearable device under different passcode input scenarios involving either two hands or a single hand. In particular, WristSpy has the capability to accurately reconstruct fine-grained hand movement trajectories and infer PINs/patterns when mobile and wearable devices are on two hands through building a Euclidean distance-based model and developing a training-free parallel PIN/pattern inference algorithm. When both devices are on the same single hand, a highly challenging case, WristSpy extracts multi-dimensional features by capturing the dynamics of minute hand vibrations and performs machine-learning based classification to identify PIN entries. Extensive experiments with 15 volunteers and 1600 passcode inputs demonstrate that an adversary is able to recover a user's PIN/pattern with up to 92% success rate within 5 tries under various input scenarios

Security Analysis of the Masimo MightySat: Data Leakage to a Nosy Neighbor

Embedded technology known as the Internet of Things (IoT) has been integrated into everyday life, from the home, to the farm, industry, enterprise, the battlefield, and even for medical devices. With the increased use of networked devices comes an increased attack surface for malicious actors to gather and inject data, putting the privacy of users at risk. This research considers the Masimo MightySat fingertip pulse oximeter and the companion Masimo Professional Health app from a security standpoint, analyzing the Bluetooth Low Energy (BLE) communication from the device to the application and the data leakage between the two. It is found that with some analysis of a personally owned Masimo MightySat Rx through the use of an Ubertooth BLE traffic sniffer, static analysis of the HCI\_snoop.log and application data, and dynamic analysis of the app, data could be reasonably captured for another MightySat and interpret it to learn user health data