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

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