Assentication: User Deauthentication and Lunchtime Attack Mitigation with Seated Posture Biometric

Biometric techniques are often used as an extra security factor in authenticating human users. Numerous biometrics have been proposed and evaluated, each with its own set of benefits and pitfalls. Static biometrics (such as fingerprints) are geared for discrete operation, to identify users, which typically involves some user burden. Meanwhile, behavioral biometrics (such as keystroke dynamics) are well suited for continuous, and sometimes more unobtrusive, operation. One important application domain for biometrics is deauthentication, a means of quickly detecting absence of a previously authenticated user and immediately terminating that user's active secure sessions. Deauthentication is crucial for mitigating so called Lunchtime Attacks, whereby an insider adversary takes over (before any inactivity timeout kicks in) authenticated state of a careless user who walks away from her computer. Motivated primarily by the need for an unobtrusive and continuous biometric to support effective deauthentication, we introduce PoPa, a new hybrid biometric based on a human user's seated posture pattern. PoPa captures a unique combination of physiological and behavioral traits. We describe a low cost fully functioning prototype that involves an office chair instrumented with 16 tiny pressure sensors. We also explore (via user experiments) how PoPa can be used in a typical workplace to provide continuous authentication (and deauthentication) of users. We experimentally assess viability of PoPa in terms of uniqueness by collecting and evaluating posture patterns of a cohort of users. Results show that PoPa exhibits very low false positive, and even lower false negative, rates. In particular, users can be identified with, on average, 91.0% accuracy. Finally, we compare pros and cons of PoPa with those of several prominent biometric based deauthentication techniques

Detection of Spine curvature using wireless sensors

Ankylosing spondylitis (AS) is a progressive disease of the spine where the spine slowly stiffens and can eventually become completely inflexible. It can be difficult to diagnose in primary care, and thus there is often a 10-year delay in diagnosis. Within this study an intelligent wearable system is designed and developed to detect the displacement of the spine and provide the subject with a continuous posture monitoring and feedback signals when an incorrect posture is detected using accelerometer and gyroscope sensors. This wearable system can be used both to diagnose AS in early stages and to prevent subjects from lower back and neck pain caused by incorrect posture. We outline here the system which detects the curvature of the spine by using Shimmer sensors placed on the back and provides relevant exercises based on the user’s pain records

An investigation into the utility of wearable sensor derived biofeedback on the motor control of the lumbar spine

Lower back pain (LBP) is a disability that affects a large proportion of the population and treatment for this has been shifting towards a more individualized, patient-centered approach. There has been a recent uptake in the utilization and implementation of wearable sensors that can administer biofeedback in various industrial, clinical, and performance-based settings. The overall aim of this Master’s thesis was to investigate how wearable sensors can be used in a sensorimotor (re)training approach, including how sensory biofeedback from wearable sensors can be used to improve measures of spinal motor control and proprioception. Two complementary research studies were completed to address this overall aim. As a systematic review, Study #1 focused on addressing the lack of consensus surrounding wearable sensor derived biofeedback and spine motor control. The results of this review suggest that haptic/vibrotactile feedback is the most common and that it is administered in an instantaneous real-time manner within most experimental paradigms. Further, study #1 identified clear gaps within the research literature. Specifically, future research would benefit from more clarity regarding study design, and movement instructions, and explicit definitions of biofeedback parameters to enhance reproducibility. The aim of Study #2 was to assess the acute effects of wearable sensor-derived auditory biofeedback on gross lumbar proprioception. To assess this, participants completed a target repositioning protocol, followed by a training period where they were provided with auditory feedback for two of four targets based on a percentage of their lumbar ROM. Results suggest that mid-range targets benefitted most from the acute auditory feedback training. Further, individuals with poorer repositioning abilities in the pre-training assessment showed the greatest improvements from the auditory feedback training. This suggests that auditory biofeedback training may be an effective tool to improve proprioception in those with proprioceptive deficits. Collectively these complimentary research studies will improve the understanding surrounding the ecological utility of wearable sensor derived biofeedback in industrial, clinical, and performance settings to enhance to sensorimotor control of the lumbar region

Wearable sensors for measuring movement in short sessions of mindfulness sitting meditation: A pilot study

Mindfulness techniques are useful tools in health and well-being. To improve and facilitate formal training, beginners need to know if they are in a stable sitting posture and if they can hold it. Previous monitoring studies did not consider stability during sitting meditation or were specific for longer traditional practices. In this paper, we have extended and adapted previous studies to modern mindfulness practices and posed two questions: (a) Which is the best meditation seat for short sessions? In this way, the applications of stability measures are expanded to meditation activities, in which the sitting posture favors stability, and (b) Which is the most sensitive location of an accelerometer to measure body motion during short meditation sessions? A pilot study involving 31 volunteers was conducted using inertial sensors. The results suggest that thumb, head, or infraclavicular locations can be chosen to measure stability despite the habitual lumbar or sacral region found in the literature. Another important finding of this study is that zafus, chairs, and meditation benches are suitable for short meditation sessions in a sitting posture, although the zafu seems to allow for fewer postural changes. This finding opens new opportunities to design very simple and comfortable measuring systems

Care-Chair: Opportunistic health assessment with smart sensing on chair backrest

A vast majority of the population spend most of their time in a sedentary position, which potentially makes a chair a huge source of information about a person\u27s daily activity. This information, which often gets ignored, can reveal important health data but the overhead and the time consumption needed to track the daily activity of a person is a major hurdle. Considering this, a simple and cost-efficient sensory system, named Care-Chair, with four square force sensitive resistors on the backrest of a chair has been designed to collect the activity details and breathing rate of the users. The Care-Chair system is considered as an opportunistic environmental sensor that can track each and every activity of its occupant without any human intervention. It is specifically designed and tested for elderly people and people with sedentary job. The system was tested using 5 users data for the sedentary activity classification and it successfully classified 18 activities in laboratory environment with 86% accuracy. In an another experiment of breathing rate detection with 19 users data, the Care-Chair produced precise results with slight variance with ground truth breathing rate. The Care-Chair yields contextually good results when tested in uncontrolled environment with single user data collected during long hours of study. --Abstract, page iii

Intelligent Sitting Posture Classifier for Wheelchair Users

In recent years, there has been growing interest in postural monitoring while seated, thus preventing the appearance of ulcers and musculoskeletal problems in the long term. To date, postural control has been carried out by means of subjective questionnaires that do not provide continuous and quantitative information. For this reason, it is necessary to carry out a monitoring that allows to determine not only the postural status of wheelchair users, but also to infer the evolution or anomalies associated with a specific disease. Therefore, this paper proposes an intelligent classifier based on a multilayer neural network for the classification of sitting postures of wheelchair users. The posture database was generated based on data collected by a novel monitoring device composed of force resistive sensors. A training and hyperparameter selection methodology has been used based on the idea of using a stratified K-Fold in weight groups strategy. This allows the neural network to acquire a greater capacity for generalization, thus allowing, unlike other proposed models, to achieve higher success rates not only in familiar subjects but also in subjects with physical complexions outside the standard. In this way, the system can be used to support wheelchair users and healthcare professionals, helping them to automatically monitor their posture, regardless physical complexions.This work was supported in part by the Ministry of Science and Innovation-StateResearch Agency/Project funded by MCIN/State Research Agency(AEI)/10.13039/501100011033 under Grant PID2020-112667RB-I00,in part by the Basque Government under Grant IT1726-22, and in part by the Predoctoral Contracts of the Basque Government under Grant PRE-2021-1-0001 and Grant PRE-2021-1-021

LifeChair: A Conductive Fabric Sensor-Based Smart Cushion for Actively Shaping Sitting Posture.

The LifeChair is a smart cushion that provides vibrotactile feedback by actively sensing and classifying sitting postures to encourage upright posture and reduce slouching. The key component of the LifeChair is our novel conductive fabric pressure sensing array. Fabric sensors have been explored in the past, but a full sensing solution for embedded real world use has not been proposed. We have designed our system with commercial use in mind, and as a result, it has a high focus on manufacturability, cost-effectiveness and adaptiveness. We demonstrate the performance of our fabric sensing system by installing it into the LifeChair and comparing its posture detection accuracy with our previous study that implemented a conventional flexible printed PCB-sensing system. In this study, it is shown that the LifeChair can detect all 11 postures across 20 participants with an improved average accuracy of 98.1%, and it demonstrates significantly lower variance when interfacing with different users. We also conduct a performance study with 10 participants to evaluate the effectiveness of the LifeChair device in improving upright posture and reducing slouching. Our performance study demonstrates that the LifeChair is effective in encouraging users to sit upright with an increase of 68.1% in time spent seated upright when vibrotactile feedback is activated

Tailoring Interaction. Sensing Social Signals with Textiles.

Nonverbal behaviour is an important part of conversation and can reveal much about the nature of an interaction. It includes phenomena ranging from large-scale posture shifts to small scale nods. Capturing these often spontaneous phenomena requires unobtrusive sensing techniques that do not interfere with the interaction. We propose an underexploited sensing modality for sensing nonverbal behaviours: textiles. As a material in close contact with the body, they provide ubiquitous, large surfaces that make them a suitable soft interface. Although the literature on nonverbal communication focuses on upper body movements such as gestures, observations of multi-party, seated conversations suggest that sitting postures, leg and foot movements are also systematically related to patterns of social interaction. This thesis addressees the following questions: Can the textiles surrounding us measure social engagement? Can they tell who is speaking, and who, if anyone, is listening? Furthermore, how should wearable textile sensing systems be designed and what behavioural signals could textiles reveal? To address these questions, we have designed and manufactured bespoke chairs and trousers with integrated textile pressure sensors, that are introduced here. The designs are evaluated in three user studies that produce multi-modal datasets for the exploration of fine-grained interactional signals. Two approaches to using these bespoke textile sensors are explored. First, hand crafted sensor patches in chair covers serve to distinguish speakers and listeners. Second, a pressure sensitive matrix in custom-made smart trousers is developed to detect static sitting postures, dynamic bodily movement, as well as basic conversational states. Statistical analyses, machine learning approaches, and ethnographic methods show that by moni- toring patterns of pressure change alone it is possible to not only classify postures with high accuracy, but also to identify a wide range of behaviours reliably in individuals and groups. These findings es- tablish textiles as a novel, wearable sensing system for applications in social sciences, and contribute towards a better understanding of nonverbal communication, especially the significance of posture shifts when seated. If chairs know who is speaking, if our trousers can capture our social engagement, what role can smart textiles have in the future of human interaction? How can we build new ways to map social ecologies and tailor interactions