Towards a Practical Pedestrian Distraction Detection Framework using Wearables

Pedestrian safety continues to be a significant concern in urban communities and pedestrian distraction is emerging as one of the main causes of grave and fatal accidents involving pedestrians. The advent of sophisticated mobile and wearable devices, equipped with high-precision on-board sensors capable of measuring fine-grained user movements and context, provides a tremendous opportunity for designing effective pedestrian safety systems and applications. Accurate and efficient recognition of pedestrian distractions in real-time given the memory, computation and communication limitations of these devices, however, remains the key technical challenge in the design of such systems. Earlier research efforts in pedestrian distraction detection using data available from mobile and wearable devices have primarily focused only on achieving high detection accuracy, resulting in designs that are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system that achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption, this paper makes the following main contributions: (i) design of a novel complex activity recognition framework which employs motion data available from users' mobile and wearable devices and a lightweight frequency matching approach to accurately and efficiently recognize complex distraction related activities, and (ii) a comprehensive comparative evaluation of the proposed framework with well-known complex activity recognition techniques in the literature with the help of data collected from human subject pedestrians and prototype implementations on commercially-available mobile and wearable devices

Smartwatch-Based IoT Fall Detection Application

This paper proposes using only the streaming accelerometer data from a commodity-based smartwatch (IoT) device to detect falls. The smartwatch is paired with a smartphone as a means for performing the computation necessary for the prediction of falls in realtime without incurring latency in communicating with a cloud server while also preserving data privacy. The majority of current fall detection applications require specially designed hardware and software which make them expensive and inaccessible to the general public. Moreover, a fall detection application that uses a wrist worn smartwatch for data collection has the added benefit that it can be perceived as a piece of jewelry and thus non-intrusive. We experimented with both Support Vector Machine and Naive Bayes machine learning algorithms for the creation of the fall model. We demonstrated that by adjusting the sampling frequency of the streaming data, computing acceleration features over a sliding window, and using a Naive Bayes machine learning model, we can obtain the true positive rate of fall detection in real-world setting with 93.33% accuracy. Our result demonstrated that using a commodity-based smartwatch sensor can yield fall detection results that are competitive with those of custom made expensive sensors

Design and Implementation of a Usability-Framework for Smartwatches

Due to technological developments in the last decade, the class of wearable computers arose which offers innovative access to human-computer interaction. Especially smartwatches attracted attention and are established as a permanently worn computer device on many wrists nowadays. In particular, for new technologies usability is an important success factor. Although usability is a well-known domain with a long research history, unique characteristics of smartwatch applications complicate the utilization of recent usability analysis methods. Therefore, we survey recent techniques for the usability analysis, outline and respectively adapt suited approaches based on the requirements induced by the special characteristics of smartwatches. In addition, we design and implement a usability framework which facilitates the automated usability analysis for smartwatch applications in a design science research approach. Furthermore, we demonstrate the applicability of the developed framework and show the results of a usability analysis for an exemplary case study

Snoopy: Sniffing Your Smartwatch Passwords via Deep Sequence Learning

Demand for smartwatches has taken off in recent years with new models which can run independently from smartphones and provide more useful features, becoming first-class mobile platforms. One can access online banking or even make payments on a smartwatch without a paired phone. This makes smartwatches more attractive and vulnerable to malicious attacks, which to date have been largely overlooked. In this paper, we demonstrate Snoopy, a password extraction and inference system which is able to accurately infer passwords entered on Android/Apple watches within 20 attempts, just by eavesdropping on motion sensors. Snoopy uses a uniform framework to extract the segments of motion data when passwords are entered, and uses novel deep neural networks to infer the actual passwords. We evaluate the proposed Snoopy system in the real-world with data from 362 participants and show that our system offers a ~ 3-fold improvement in the accuracy of inferring passwords compared to the state-of-the-art, without consuming excessive energy or computational resources. We also show that Snoopy is very resilient to user and device heterogeneity: it can be trained on crowd-sourced motion data (e.g. via Amazon Mechanical Turk), and then used to attack passwords from a new user, even if they are wearing a different model. This paper shows that, in the wrong hands, Snoopy can potentially cause serious leaks of sensitive information. By raising awareness, we invite the community and manufacturers to revisit the risks of continuous motion sensing on smart wearable devices

Exploring the Use of Wearables to develop Assistive Technology for Visually Impaired People

This thesis explores the usage of two prominent wearable devices to develop assistive technology for users who are visually impaired. Specifically, the work in this thesis aims at improving the quality of life of users who are visually impaired by improving their mobility and ability to socially interact with others. We explore the use of a smart watch for creating low-cost spatial haptic applications. This app explores the use of haptic feedback provided using a smartwatch and smartphone to provide navigation instructions that let visually impaired people safely traverse a large open space. This spatial feedback guides them to walk on a straight path from source to destination by avoiding veering. Exploring the paired interaction between a Smartphone and a Smartwatch, helped to overcome the limitation that smart devices have only single haptic actuator.We explore the use of a head-mounted display to enhance social interaction by helping people with visual impairments align their head towards a conversation partner as well as maintain personal space during a conversation. Audio feedback is provided to the users guiding them to achieve effective face-to-face communication. A qualitative study of this method shows the effectiveness of the application and explains how it helps visually impaired people to perceive non-verbal cues and feel more engaged and assertive in social interactions

THE APPLICATION OF SMARTWATCH IN MANAGING EMPLOYEE HEALTH MONITORING

ABSTRACTWorkplace health issues have continued to increase, and this has caused problems such as increasing medical cost and medical leaves. In response to these issues, employers are starting to adopt health technology to overcome the problem such as smartwatch. Smartwatch technology is a wrist-worn device provided with a variety of sensors that are available for collecting physical activity and location data in real time. This paper aims to explore the future drivers of the smartwatch application in monitoring and managing employees’ health. The research study used exploratory research design utilizing the foresight methods. STEEPV analysis was used to identify the key drivers of smartwatch application and to develop a descriptive survey for assessing the impact and uncertainty of each driver. The survey was distributed to human resources managers of medium-sized companies in Malaysia. Technology readiness of smart watch adoption was evaluated using Technology Readiness Index (TRI). Thirty-five respondents took part in online survey. From the data analysis, top two drivers had been identified which are “social interaction” and “data transparency”. These drivers were used for developing future scenario of the smartwatch application in monitoring and managing employee health in the next 5 to 10 years. Four scenarios had been discussed in this paper which are healthy workplace environment, unattainable technology adoption, inefficient technology, and low adoption of smartwatch. This research would provide additional information about the future scenario of smartwatch application in managing employee health monitoring in Malaysia. Keywords: Smartwatch; Employee Health Monitoring; Technological Readines

Activity-Based User Authentication Using Smartwatches

Smartwatches, which contain an accelerometer and gyroscope, have recently been used to implement gait and gesture- based biometrics; however, the prior studies have long-established drawbacks. For example, data for both training and evaluation was captured from single sessions (which is not realistic and can lead to overly optimistic performance results), and in cases when the multi-day scenario was considered, the evaluation was often either done improperly or the results are very poor (i.e., greater than 20% of EER). Moreover, limited activities were considered (i.e., gait or gestures), and data captured within a controlled environment which tends to be far less realistic for real world applications. Therefore, this study remedies these past problems by training and evaluating the smartwatch-based biometric system on data from different days, using large dataset that involved the participation of 60 users, and considering different activities (i.e., normal walking (NW), fast walking (FW), typing on a PC keyboard (TypePC), playing mobile game (GameM), and texting on mobile (TypeM)). Unlike the prior art that focussed on simply laboratory controlled data, a more realistic dataset, which was captured within un-constrained environment, is used to evaluate the performance of the proposed system. Two principal experiments were carried out focusing upon constrained and un-constrained environments. The first experiment included a comprehensive analysis of the aforementioned activities and tested under two different scenarios (i.e., same and cross day). By using all the extracted features (i.e., 88 features) and the same day evaluation, EERs of the acceleration readings were 0.15%, 0.31%, 1.43%, 1.52%, and 1.33% for the NW, FW, TypeM, TypePC, and GameM respectively. The EERs were increased to 0.93%, 3.90%, 5.69%, 6.02%, and 5.61% when the cross-day data was utilized. For comparison, a more selective set of features was used and significantly maximize the system performance under the cross day scenario, at best EERs of 0.29%, 1.31%, 2.66%, 3.83%, and 2.3% for the aforementioned activities respectively. A realistic methodology was used in the second experiment by using data collected within unconstrained environment. A light activity detection approach was developed to divide the raw signals into gait (i.e., NW and FW) and stationary activities. Competitive results were reported with EERs of 0.60%, 0% and 3.37% for the NW, FW, and stationary activities respectively. The findings suggest that the nature of the signals captured are sufficiently discriminative to be useful in performing transparent and continuous user authentication.University of Kuf