Tractor cabin ergonomics analyses by means of Kinect motion capture technology

Kinect is the de facto standard for real-time depth sensing and motion capture cameras. The sensor is here proposed for exploiting body tracking during driving operations. The motion capture system was developed taking advantage of the Microsoft software development kit (SDK), and implemented for real-time monitoring of body movements of a beginner and an expert tractor drivers, on different tracks (straight and with curves) and with different driving conditions (manual and assisted steering). Tests show how analyses can be done not only in terms of absolute movements, but also in terms of relative shifts, allowing for quantification of angular displacements or rotations

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

Channel State Information from pure communication to sense and track human motion: A survey

Human motion detection and activity recognition are becoming vital for the applications in smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology

An experimental approach for the characterization of prolonged sitting postures using pressure sensitive mats

The adoption of prolonged sitting posture,which is a condition commonly encountered in several working tasks,is known to induce a wide range of negative effects,including discomfort,which has been recognized as an early predictor for musculoskeletal disorders (particularly low back pain).In this regard,the continuous monitoring of worker’s psychophysical state while sitting for long periods of time, may result useful in to preventing and managing potentially risky situations and to promote ergonomics and macroergonomics interventions,aimed to better organize work shifts and workplaces.The aim of this dissertation is to provide and test the reliability of a set of monitoring parameters,based on the use of quantitative information derived from body-seat contact pressure sensors.In particular, he study was focused on the assessment of trunk postural sway (the small oscillations resulting from the stabilization control system) and the number of In Chair Movements (ICM) or postural shifts performed while sitting, proven as a reliable tool for discomfort prediction. This thesis is articulated into four experimental campaigns.The first is a pilot study which aimed to define the most reliable algorithm and the set of parameters useful to assess the performed postural shifts or In chair Movements (ICM), which result useful to characterize postural strategies in the long term-monitoring. In this regard, a pilot study was conducted in which two different algorithms for the ICM computing were tested, based on different parameters and having different thresholds. The chosen algorithm was used, together with trunk sway parameters, to evaluate postural strategies in the other three experiments of this thesis. The second and the third studies evaluated sitting postural strategies among bus drivers during regular, long-term work shifts performed on urban and extra-urban routes. The results, in this case, showed that, all drivers reported a constant increase in perceived discomfort levels and a correspondent increase in trunk sway and overall number of ICM performed. This may indicate the adoption of specific strategies in order to cope with discomfort onset, a fatigue-induced alteration of postural features, or both simultaneously. However, it was interesting to observe differences in ICM vs trunk sway trend considering the single point-to-point route in the case of urban drivers. This difference between may indicate that these parameters refer to different aspects of sitting postural strategies: ICM may be more related to discomfort while sway may be more representative of task-induced fatigue. Trunk sway monitoring, as well as the count of ICM performed by bus drivers may thus be a useful tool in detecting postural behaviors potentially associated with deteriorating performance and onset of discomfort. Finally, the last experiment aimed to characterize modifications in sitting behavior, in terms of trunk sway and ICM among office workers during actual shifts. Surprisingly, results showed a decreasing trend in trunk sway parameters and ICM performed over time, with significant modifications in sitting posture in terms of trunk flexion-extension. Subjects were also stratified basing on their working behavior (staying seated or making short breaks during the trial) and significant differences were identified among these two groups in terms of postural sway and perceived discomfort. This may indicate that the adoption of specific working strategies can significantly influence sitting behavior and discomfort onset. In conclusion, the trunk sway monitoring and the ICM assessment in actual working environments may represent a useful tool to detect specific postural behaviors potentially associated with deteriorating performance and onset of discomfort, both among professional drivers and office workers.They might effectively support the evaluation of specific working strategies,as well as the set-up of macroergonomics interventions

Sensing Senses: Tactile Feedback for the Prevention of Decubitus Ulcers

Decubitus ulcers, also known as pressure sores, is a major problem in health care, in particular for patients with spinal cord injuries. These patients cannot feel the discomfort that would urge healthy people to change their posture. We describe a system that uses a sensor mat to detect problematic postures and provides tactile feedback to the user. The results of our preliminary study with healthy subjects show that the tactile feedback is a viable option to spoken feedback. We envision the system being used for rehabilitation games, but also for everyday Decubitus ulcers prevention

User Mobility Detection using Foot Force Sensors and Mobile Phone GPS.

PhDA user (or human) mobility context is defined as a type of user context that describes a type of whole body posture (e.g., standing versus sitting) and/or a type of travel or transportation mode (e.g., walking, cycling, travel by bus, etc). Such a context can be derived from low-level sensor data and spatial contexts, including location coordinates, 3D-orientation, direction (with respect to magnetic north), velocity and acceleration. Different value-added services can be adapted to users’ mobility contexts such as assessing how eco-friendly our travel is, and adapting travel information services such as maps to different transportation modes. Current sensor-based methods for user mobility detection have several key limitations: narrow range of recognition, coarse user mobility recognition capability, and low recognition accuracy. In this thesis, a new Foot-Force and GPS (FF+GPS) sensor method is proposed to overcome these challenges that leverages a set of wearable FF sensors in combination with mobile phone GPS. The novelty of this approach is that it provides a more comprehensive recognition capability in terms of reliably recognising various fine-grained human postures and transportation modes. In addition, by comparing the new FF+GPS method with both an accelerometer (ACC) method (62% accuracy) and an ACC+GPS based method (70% accuracy) as baseline methods, it obtains a higher accuracy (90%) with less computational complexity, when tested on a dataset obtained from ten individuals. In addition, the new FF+GPS method has been further extended and evaluated. More specifically, the trade-off between the computation and resources needed to support lower versus higher number of features and sensors has been investigated. The improved FF+GPS method reduced the number of classification features from 31 to 12, reduced the number of FF sensors from 8 to 4, and reduced the use of GPS in mobility activity recognition