Cognitive engineering

Smart technologies and connected objects are rapidly changing the organizational frontline. Yet, our understanding of how these technologies infuse service encounters remains limited. Therefore, the purpose of this paper is to update existing classifications of Frontline Service Technology (FST) infusion. Moreover, the authors discuss three promising smart and connected technologies – conversational agents, extended reality (XR) and blockchain technology – and their respective implications for customers, frontline employees and service organizations.,This paper uses a conceptual approach integrating existing work on FST infusion with artificial intelligence, robotics, XR and blockchain literature, while also building on insights gathered through expert interviews and focus group conversations with members of two service research centers.,The authors define FST and propose a set of FST infusion archetypes at the organizational frontline. Additionally, the authors develop future research directions focused on understanding how conversational agents, XR and blockchain technology will impact service.,This paper updates and extends existing classifications of FST, while paving the road for further work on FST infusion.

Frontline Service Technology infusion: conceptual archetypes and future research directions

Introduction I: Multimedia Input Analysis II: Multimedia Presentation Design III: Automated Graphic Design IV: Automated Layout V: User and Discourse Modeling VI: Model Based Interfaces VII: Agent Interfaces VIII: Evaluation

Readings in Intelligent User Interfaces

As smartphones evolve researchers are studying new techniques to ease the human-mobile interaction. We propose EyePhone, a novel "hand-free" interfacing system capable of driving mobile applications/functions using only the user's eyes movement and actions (e.g., wink). EyePhone tracks the user's eye movement across the phone's display using the camera mounted on the front of the phone; more specifically, machine learning algorithms are used to: i) track the eye and infer its position on the mobile phone display as a user views a particular application; and ii) detect eye blinks that emulate mouse clicks to activate the target application under view. We present a prototype implementation of EyePhone on a Nokia N810, which is capable of tracking the position of the eye on the display, mapping this positions to an application that is activated by a wink. At no time does the user have to physically touch the phone display.

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EyePhone: activating mobile phones with your eyes

Analysis of gait features provides important information during the treatment of neurological disorders, including Parkinson’s disease. It is also used to observe the effects of medication and rehabilitation. The methodology presented in this paper enables the detection of selected gait attributes by Microsoft (MS) Kinect image and depth sensors to track movements in three-dimensional space. The experimental part of the paper is devoted to the study of three sets of individuals: 18 patients with Parkinson’s disease, 18 healthy aged-matched individuals, and 15 students. The methodological part of the paper includes the use of digital signal-processing methods for rejecting gross data-acquisition errors, segmenting video frames, and extracting gait features. The proposed algorithm describes methods for estimating the leg length, normalised average stride length (SL), and gait velocity (GV) of the individuals in the given sets using MS Kinect data. The main objective of this work involves the recognition of selected gait disorders in both the clinical and everyday settings. The results obtained include an evaluation of leg lengths, with a mean difference of 0.004 m in the complete set of 51 individuals studied, and of the gait features of patients with Parkinson’s disease (SL: 0.38 m, GV: 0.61 m/s) and an age-matched reference set (SL: 0.54 m, GV: 0.81 m/s). Combining both features allowed for the use of neural networks to classify and evaluate the selectivity, specificity, and accuracy. The achieved accuracy was 97.2 %, which suggests the potential use of MS Kinect image and depth sensors for these applications. Discussion points include the possibility of using the MS Kinect sensors as inexpensive replacements for complex multi-camera systems and treadmill walking in gait-feature detection for the recognition of selected gait disorders.

/pdf/motion-tracking-and-gait-feature-estimation-for-recognising-30ktsqyc1z.pdf

Motion tracking and gait feature estimation for recognising Parkinson’s disease using MS Kinect

The intention of this paper is to provide an overview on the subject of Human-Computer Interaction. The overview includes the basic definitions and terminology, a survey of existing technologies and recent advances in the field, common architectures used in the design of HCI systems which includes unimodal and multimodal configurations, and finally the applications of HCI. This paper also offers a comprehensive number of references for each concept, method, and application in the HCI.

Mo Nours Arab

Papers

Human-Computer Interaction: Overview on State of the Art