Measuring passers-by engagement with AmPost: a printed interactive audio poster

This paper presents AmPost, a prototype of an interactive audio poster, which integrates ink-jet printed sonic and tactile elements along with textual and graphical information. This enables users to directly interact and engage with the poster. To achieve a seamless paper-based interactive poster that includes printed interactive elements, we implement a printed speaker and audio feedback system directly into the paper-based medium, which plays a short tune when someone walks by. Engaging passers-by with this new interface becomes a challenge as posters, in general, are non-interactive. This paper also presents an initial empirical evaluation of AmPost that captures and analyses the frequency and length of user engagement, and evaluates whether user engagement improves with the integration of interactive features, in comparison to the traditional non-interactive poster. This study facilitates in building the generic approach to measure engagement that applies to any kind of posters

An ambient-physical system to infer concentration in open-plan workplace

One of the core challenges in open-plan workspaces is to ensure a good level of concentration for the workers while performing their tasks. Hence, being able to infer concentration levels of workers will allow building designers, managers, and workers to estimate what effect different open-plan layouts will have and to find an optimal one. In this article, we present an ambient-physical system to investigate the concentration inference problem. Specifically, we deploy a series of pervasive sensors to capture various ambient and physical signals related to perceived concentration at work. The practicality of our system has been tested on two large open-plan workplaces with different designs and layouts. The empirical results highlight promising applications of pervasive sensing in occupational concentration inference, which can be adopted to enhance the capabilities of modern workplaces

An ambient-physical system to infer concentration in open-plan workplace

One of the core challenges in open-plan workspaces is to ensure a good level of concentration for the workers while performing their tasks. Hence, being able to infer concentration levels of workers will allow building designers, managers, and workers to estimate what effect different open-plan layouts will have and to find an optimal one. In this article, we present an ambient-physical system to investigate the concentration inference problem. Specifically, we deploy a series of pervasive sensors to capture various ambient and physical signals related to perceived concentration at work. The practicality of our system has been tested on two large open-plan workplaces with different designs and layouts. The empirical results highlight promising applications of pervasive sensing in occupational concentration inference, which can be adopted to enhance the capabilities of modern workplaces

Interactive self-adaptive clutter-aware visualisation for mobile data mining

There is an emerging focus on real-time data stream analysis on mobile devices. A wide range of data stream processing applications are targeted to run on mobile handheld devices with limited computational capabilities such as patient monitoring, driver monitoring, providing real-time analysis and visualisation for emergency and disaster management, real-time optimisation for courier pick-up and delivery etc. There are many challenges in visualisation of the analysis/data stream mining results on a mobile device. These include coping with the small screen real-estate and effective presentation of highly dynamic and real-time analysis. This paper proposes a generic theory for visualisation on small screens that we term Adaptive Clutter Reduction ACR. Based on ACR, we have developed and experimentally validated a novel data stream clustering result visualisation technique that we term Clutter-Aware Clustering Visualiser CACV and its enhancement of enabling user interactivity that we term iCACV. Experimental results on both synthetic and real datasets using the Google Android platform are presented proving the effectiveness of the proposed techniques

Adaptive clutter-aware visualization for mobile data stream mining

Adaptive clutter-aware visualization for mobile data stream minin

Clutter-adaptive visualization for mobile data mining

Clutter-adaptive visualization for mobile data minin

OccuSpace: Towards a robust occupancy prediction system for activity based workplace

Workplace occupancy detection is becoming increasingly important in large Activity Based Work (ABW) environments as it helps building and office management understand the utilisation and potential benefits of shared workplace. However, existing sensor-based technologies detect workstation occupancy in indoor spaces require extensive installation of hardware and maintenance incurring ongoing costs. Moreover, accuracy can depend on the specific seating styles of workers since the sensors are usually placed under the table or overhead. In this research, we provide a robust system called OccuSpace to predict occupancy of different atomic zones in large ABW environments. Unlike fixed sensors, OccuSpace uses statistical features engineered from Received Signal Strength Indicator (RSSI) of Bluetooth card beacons carried by workers while they are within the ABW environment. These features are used to train state-of-the-art machine learning algorithms for prediction task. We setup the experiment by deploying our system in a realworld open office environment. The experimental results show that OccuSpace is able to achieve a high accuracy for workplace occupancy prediction

OccuSpace: Towards a robust occupancy prediction system for activity based workplace

Workplace occupancy detection is becoming increasingly important in large Activity Based Work (ABW) environments as it helps building and office management understand the utilisation and potential benefits of shared workplace. However, existing sensor-based technologies detect workstation occupancy in indoor spaces require extensive installation of hardware and maintenance incurring ongoing costs. Moreover, accuracy can depend on the specific seating styles of workers since the sensors are usually placed under the table or overhead. In this research, we provide a robust system called OccuSpace to predict occupancy of different atomic zones in large ABW environments. Unlike fixed sensors, OccuSpace uses statistical features engineered from Received Signal Strength Indicator (RSSI) of Bluetooth card beacons carried by workers while they are within the ABW environment. These features are used to train state-of-the-art machine learning algorithms for prediction task. We setup the experiment by deploying our system in a realworld open office environment. The experimental results show that OccuSpace is able to achieve a high accuracy for workplace occupancy prediction

Intelligent task recognition: Towards enabling productivity assistance in daily life

We introduce the novel research problem of task recognition in daily life. We recognize tasks such as project management, planning, meal-breaks, communication, documentation, and family care. We capture Cyber, Physical, and Social (CPS) activities of 17 participants over four weeks using device-based sensing, app activity logging, and an experience sampling methodology. Our cohort includes students, casual workers, and professionals, forming the first real-world context-rich task behaviour dataset. We model CPS activities across different task categories, results highlight the importance of considering the CPS feature sets in modelling, especially work-related tasks

Imagining future digital assistants at work: A study of task management needs

Digital Assistants (DAs) can support workers in the workplace and beyond. However, target user needs are not fully understood, and the functions that workers would ideally want a DA to support require further study. A richer understanding of worker needs could help inform the design of future DAs. We investigate user needs of future workplace DAs using data from a user study of 40 workers over a four-week period. Our qualitative analysis confirms existing research and generates new insight on the role of DAs in managing people's time, tasks, and information. Placing these insights in relation to quantitative analysis of self-reported task data, we highlight how different occupation roles require DAs to take varied approaches to these domains and the effect of task characteristics on the imagined features. Our findings have implications for the design of future DAs in work settings and we offer some recommendations for reduction to practice