Augmented Human Research最新文献

In this paper, the design of adaptive artifact canceler (AAC) filter using bacteria foraging optimization (BFO) algorithm is presented. The performance of proposed AAC filter is tested on a corrupted ECG signal. Based on simulation results, it is observed that the AAC filter designed with BFO technique achieves significant improvement in fidelity parameters such as SNR, NRMSE, and NRME when compared with other reported algorithms in the literature. AAC filter based on BFO technique provides 6 dB improvement in output SNR, 85% reduction in NRMSE, and 90% lower NRME as compared to recently reported AAC filter based on ABC-SF algorithm. Further, AAC filter using BFO technique enhances the coherence between pure and reconstructed ECG signals.

The increasing trend of systematic collection of medical data (diagnoses, hospital admission emergencies, blood test results, scans, etc) by healthcare providers offers an unprecedented opportunity for the application of modern data mining, pattern recognition, and machine learning algorithms. The ultimate aim is invariably that of improving outcomes, be it directly or indirectly. Notwithstanding the successes of recent research efforts in this realm, a major obstacle of making the developed models usable by medical professionals (rather than computer scientists or statisticians) remains largely unaddressed. Yet, a mounting amount of evidence shows that the ability to understand and easily use novel technologies is a major factor governing how widely adopted by the target users (doctors, nurses, and patients, amongst others) they are likely to be. In this work we address this technical gap. In particular, we describe a portable, web-based interface that allows healthcare professionals to interact with recently developed machine learning and data driven prognostic algorithms. Our application interfaces a statistical disease progression model and displays its predictions in an intuitive and readily understandable manner. Different types of geometric primitives and their visual properties (such as size or colour) are used to represent abstract quantities such as probability density functions, the rate of change of relative probabilities, and a series of other relevant statistics which the heathcare professional can use to explore patients’ risk factors or provide personalized, evidence and data driven incentivization to the patient.

Arm swing is a well-known exercise practiced throughout Asia. The benefits of regular exercise affect a person’s gait, shoulder function as well as head and body strength. This paper presents a new study of gesture-based communication by using arm swing as a gestural input command applied to any digital device used by persons aged 21–50 years of age during their workday. In five difference occupations, i.e., student, lecturer, office workers, designer and industrial worker, with different routine tasks were studied. The sampling comprised 30 participants, 6 persons representing each occupation. Data were collected through focus groups. The objective of this study is to provide possibilities to integrate proper user experience of gesture-based communication toward an arm-swing input device onto participants’ routine activities. It is believed this research can have a significant impact on quality on human daily lives. A part of user experience methodology, like a field experiment, is applied to provoke participants to follow the correct sequence case by case. This paper adopts user experience measurement for this qualitative research approach. A proper user experience occurs when one understands how strength and weakness are affected by gestural input. An input gesture, feedback, system and output can then be mapped together into a complete scenario of a person’s daily life. Each participant is encouraged to raise various ideas on how he/she accesses technology rather than on how technologies allow them to access information. Based on the experimental results, this paper will then discuss the possible tasks performed in the five occupations in which an arm-swing input gesture is integrated. The influence of this integration into the participants’ daily activities presents confirmation of a preferable wearable input device for all focus groups. This wearable input device can then be integrated into activities without any interruption in a person’s main actions.

With terms like ‘fake news’ and ‘cyber attack’ dominating the news, skepticism toward the media and other online individuals has become a major facet of modern life. This paper views the way we process information during HCI through the lens of suspicion, a mentally taxing state that people enter before making a judgment about whether or not to trust information. With the goal of enabling objective, real-time measurements of suspicion during HCI, we describe an experiment where fNIRS was used to identify the neural correlates of suspicion in the brain. We developed a convolutional long short-term memory classifier that predicts suspicion using a leave-one-participant-out cross-validation scheme, with average accuracy greater than 76%. Notably, the brain regions implicated by our results dovetail with prior theoretical definitions of suspicion. We describe implications of this work for HCI, to augment users’ capabilities by enabling them to develop a ‘healthy skepticism’ to parse out truth from fiction online.

We present a multi-embodiment interface aimed at assisting human-centered ergonomics design, where traditionally the design process is hindered by the need of recruiting diverse users or the utilization of disembodied simulations to address designing for most groups of the population. The multi-embodiment solution is to actively embody the user in the design and evaluation process in virtual reality, while simultaneously superimposing additional simulated virtual bodies on the user’s own body. This superimposed body acts as the target and enables simultaneous anthropometrical ergonomics evaluation for both the user’s self and the target. Both virtual bodies of self and target are generated using digital human modeling from statistical data, and the animation of self-body is motion-captured while the target body is moved using a weighted inverse kinematics approach with end effectors on the hands and feet. We conducted user studies to evaluate human ergonomics design in five scenarios in virtual reality, comparing multi-embodiment with single embodiment. Similar evaluations were conducted again in the physical environment after virtual reality evaluations to explore the post-VR influence of different virtual experience.

Many real-life scenarios can benefit from both physical proximity and natural gesture interaction. In this paper, we explore shared collocated interactions on unmodified wearable devices. We introduce an interaction technique which enables a small group of people to interact using natural gestures. The proximity of users and devices is detected through acoustic ranging using inaudible signals, while in-air hand gestures are recognized from three-axis accelerometers. The underlying wireless communication between the devices is handled over Bluetooth for scalability and extensibility. We present (1) an overview of the interaction technique and (2) an extensive evaluation using unmodified, off-the-shelf, mobile, and wearable devices which show the feasibility of the method. Finally, we demonstrate the resulting design space with three examples of multi-user application scenarios.

Augmented attention, assisting the user in noticing important things, is one of the ways human action can be enhanced with technologies. We investigated how vibrotactile stimulation given to the forehead could be used to cue gaze direction. We built a vibrotactile headband with an array of six actuators that presented short, tap-like cues. In the first experiment, the participant was instructed to look at the point on a horizontal line that they thought the vibrotactile cue was pointing to. Analysis of the participant’s gaze points showed that for the majority there were statistically significant differences between cues from different actuators. This indicated that the six actuators could successfully direct the participant’s gaze to different areas of the visual field. In addition, vibrotactile cueing of gaze direction could be used for directing visual attention and providing navigation cues with wearable headbands. To strengthen our findings, we investigated how effective the vibrotactile stimulation would be to cue gaze direction in a visual search task. Participant’s were asked to find a deviant shape (a target) from a display full of simple shapes. The vibrotactile cueing implemented with the headband device was used to inform the participants of the approximate horizontal position of the target in three different experimental conditions. In the most informative condition, six actuators were used to inform the participant of the horizontal area where the target would be found, in the second condition two actuators were used to inform the participant of the target side on the display (left or right), and in the least informative condition no directional information was given. Analysis of the trial completion times showed that there were statistically significant differences between the least informative condition and the two other conditions. However, we did not find significant differences in trial completion times between the two conditions where information of the target location was given. This indicated that while the actuators could successfully direct the participant’s attention to different areas of the visual field to help in the search task, the simple approach of just adding actuators and dividing the visual field to more sub-areas did not improve the results. The findings of this study showed that while there is potential in using vibrotactile cueing of gaze direction, more research is needed to fully exploit it.

The penetration of mathematical modelling in sports science to date has been highly limited. In particular and in contrast to most other scientific disciplines, sports science research has been characterized by comparatively little effort investment in the development of phenomenological models. Practical applications of such models aimed at assisting trainees or sports professionals more generally remain nonexistent. The present paper aims at addressing this gap. We adopt a recently proposed mathematical model of neuromuscular engagement and adaptation, and develop around it an algorithmic framework which allows it to be employed in actual training program design and monitoring by resistance training practitioners (coaches or athletes). We first show how training performance characteristics can be extracted from video sequences, effortlessly and with minimal human input, using computer vision. The extracted characteristics are then used to fit the adopted model i.e. to estimate the values of its free parameters, from differential equations of motion in what is usually termed the inverse dynamics problem. A computer simulation of training bouts using the estimated (and hence athlete specific) model is used to predict the effected adaptation and with it the expected changes in future performance capabilities. Lastly we describe a proof-of-concept software tool we developed which allows the practitioner to manipulate training parameters and immediately see their effect on predicted adaptation (again, on an athlete specific basis). Thus, this work presents a holistic view of the monitoring–assessment–adjustment loop which lies at the centre of successful coaching. By bridging the gap between theoretical and applied aspects of sports science, the present contribution highlights the potential of mathematical and computational modelling in this field and serves to encourage further research focus in this direction.