Smart Office Environment Research Articles

Biometric face authentication system for secure smart office environments

<span>The use of the smart office concept in security has increased significantly recently. One of the areas of concern is the use of facial finger biometric technology for authentication systems, for example, authentication to enter a particular room in an office. This paper aims to describe a new prototype for office door automation and security that combines facial biometric technology and NodeMCU. Hopefully, this system will help increase the safety and comfort of office employees with easy installation and low cost. This system automatically controls (opens or closes) the door based on the biometrics of the user’s face registered in the database on the NodeMCU microcontroller. The main system comprises a NodeMCU microcontroller, face sensors, and a door lock system.</span>

Privacy-preserving activity recognition using multimodal sensors in smart office

Developing a human activity recognition (HAR) system for employees is essential to incorporate intelligence into smart office environments, enabling various human-centered applications to enhance employees’ well-being. Although remarkable progress has been made for the HAR in the smart office, several issues still exist, including lacking a privacy-preserving and unobtrusive method and demanding enhanced generalization performance across users. Therefore, a novel privacy-preserving HAR method based on multimodal sensors is investigated in this study, containing an infrared array sensor, a sensing chair, and a triaxial accelerometer built in a smartphone. The effectiveness of different multimodal combinations is examined. Moreover, a deep learning model is developed for multimodal data fusion to enhance the generalization performances across users. The model contains a residual 3D convolutional neural network (CNN) and 1D CNN for learning spatial–temporal feature representation of different modalities. Additionally, external memory units and an adaptive decision fusion operation are utilized for multimodal data fusion. Finally, extensive experiments are conducted to examine the performance of the proposed model using a self-collected dataset and the leave-one-subject-out cross-validation approach. The results verify the effectiveness of the proposed model.

Occupant privacy perception, awareness, and preferences in smart office environments

Building management systems tout numerous benefits, such as energy efficiency and occupant comfort but rely on vast amounts of data from various sensors. Advancements in machine learning algorithms make it possible to extract personal information about occupants and their activities beyond the intended design of a non-intrusive sensor. However, occupants are not informed of data collection and possess different privacy preferences and thresholds for privacy loss. While privacy perceptions and preferences are most understood in smart homes, limited studies have evaluated these factors in smart office buildings, where there are more users and different privacy risks. To better understand occupants’ perceptions and privacy preferences, we conducted twenty-four semi-structured interviews between April 2022 and May 2022 on occupants of a smart office building. We found that data modality features and personal features contribute to people’s privacy preferences. The features of the collected modality define data modality features – spatial, security, and temporal context. In contrast, personal features consist of one’s awareness of data modality features and data inferences, definitions of privacy and security, and the available rewards and utility. Our proposed model of people’s privacy preferences in smart office buildings helps design more effective measures to improve people’s privacy.

A Privacy-Preserving Desk Sensor for Monitoring Healthy Movement Breaks in Smart Office Environments with the Internet of Things.

Smart workplace Internet of Things (IoT) solutions rely on several sensors deployed efficiently in the workplace environment to collect accurate data to meet system goals. A vital issue for these sensor-based IoT solutions is privacy. Ideally, the occupants must be monitored discreetly, and the strategies for maintaining privacy are dependent on the nature of the data required. This paper proposes a new sensor design approach for IoT solutions in the workplace that protects occupants' privacy. We focus on a novel sensor that autonomously detects and captures human movements in the office to monitor a person's sedentary behavior. The sensor guides an eHealth solution that uses continuous feedback about desk behaviors to prompt healthy movement breaks for seated workers. The proposed sensor and its privacy-preserving characteristics can enhance the eHealth solution system's performance. Compared to self-reporting, intrusive, and other data collection techniques, this sensor can collect the information reliably and timely. We also present the data analysis specific to this new sensor that measures two physical distance parameters in real-time and uses their difference to determine human actions. This architecture aims to collect precise data at the sensor design level rather than to protect privacy during the data analysis phase.

Using big data and federated learning for generating energy efficiency recommendations

Internet of Things (IoT) devices are becoming popular solutions for smart home and office environments and contribute the most to energy efficiency. The most common implementation of such solutions relies on smart home systems that are hosted on the cloud. They collect data from a multitude of sensors, process it in real-time on the cloud and deliver immediate actions to sets of actuators that are installed locally. In this work, we present the (EM) $$^3$$ project (Consumer Engagement towards Energy Saving Behaviour by Means of Exploiting Micro Moments and Mobile Recommendation Systems), which combines data collection, information abstraction, timed recommendations for energy saving actions and automations that promote energy saving in a household or office setup. The advantage of the (EM) $$^3$$ project is that each room or office setup is controlled locally on an edge device, thus removing the need to share data to the cloud. The current article details on the data and information processing aspects of the (EM) $$^3$$ solution, which efficiently handles thousands of sensor events on a daily basis and provides useful analytics and recommendations to the end user to support habit change. It also demonstrates the scalability of the solution by simulating a simple scenario of distributed data collection and processing on the edge nodes, which takes advantage of federated learning in order to adapt to the needs of multiple users without exposing their privacy.

What is a smart office environment? An exploratory study from a user perspective

PurposeThis paper aims to explore the meaning of smart office environments from a user perspective by investigating user preferences and expectations.Design/methodology/approachEleven semi-structured interviews with the users after moving into a smart office building of a Dutch Municipality and an observation as complementary data were conducted. The data were analysed based on the grounded theory and thematic analysis, combining a reflexive approach to the literature review.FindingsTwo main themes were revealed addressing user expectations and preferences for smart office environments: “enhanced interaction” with the social and physical office environment and “sense-making” of the smart concept (or smartness). Within these themes, basic and smart office aspects were identified and classified based on their association with smart office concepts or technology.Practical implicationsThe findings reveal the meaning of the smart office concepts from a user perspective by highlighting the importance of user experience on enhanced interaction and sense-making of the smart office concept, equipped with basic and smart aspects.Originality/valueTo the best of the authors’ knowledge, this study is the first to qualitatively examine drivers underlying the meaning of smart office concepts from a user point of view. Organisations, environmental psychologists, designers and managers can use the findings of this study to develop guidelines for a successful smart office design.

The phases of user experience during relocation to a smart office building: A qualitative case study

Smart office concepts became popular in recent decades, and organisations adopt this concept with expected benefits on users. Yet, it is unclear how users experience the relocation process to a smart office environment. This study examines user experiences during such relocation by focusing on their behaviour and satisfaction. Eleven semi-structured interviews were conducted with users after relocation to a smart office building of a Dutch Municipality. The data were analysed based on grounded theory and thematic analysis. The results reveal that the process of experiencing a smart office environment consisted of four phases: pre-relocation phase, confrontational phase, progressive phase, and stabilised phase. Each participant experienced the process differently as four different trend lines emerged related to their interpreted emotional responses throughout this process. This study concludes by advocating that the understanding of user behaviour in each phase is crucial to enhance user satisfaction when moving into a smart office. The phases further highlight the importance of understanding user expectations, and the relationships between users and workplaces before and after the relocation.

A Practical Experience on the Amazon Alexa Integration in Smart Offices.

Smart offices are dynamically evolving spaces meant to enhance employees’ efficiency, but also to create a healthy and proactive working environment. In a competitive business world, the challenge of providing a balance between the efficiency and wellbeing of employees may be supported with new technologies. This paper presents the work undertaken to build the architecture needed to integrate voice assistants into smart offices in order to support employees in their daily activities, like ambient control, attendance system and reporting, but also interacting with project management services used for planning, issue tracking, and reporting. Our research tries to understand what are the most accepted tasks to be performed with the help of voice assistants in a smart office environment, by analyzing the system based on task completion and sentiment analysis. For the experimental setup, different test cases were developed in order to interact with the office environment formed by specific devices, as well as with the project management tool tasks. The obtained results demonstrated that the interaction with the voice assistant is reasonable, especially for easy and moderate utterances.

Multimodal Deep Learning for Group Activity Recognition in Smart Office Environments

Deep learning (DL) models have emerged in recent years as the state-of-the-art technique across numerous machine learning application domains. In particular, image processing-related tasks have seen a significant improvement in terms of performance due to increased availability of large datasets and extensive growth of computing power. In this paper we investigate the problem of group activity recognition in office environments using a multimodal deep learning approach, by fusing audio and visual data from video. Group activity recognition is a complex classification task, given that it extends beyond identifying the activities of individuals, by focusing on the combinations of activities and the interactions between them. The proposed fusion network was trained based on the audio–visual stream from the AMI Corpus dataset. The procedure consists of two steps. First, we extract a joint audio–visual feature representation for activity recognition, and second, we account for the temporal dependencies in the video in order to complete the classification task. We provide a comprehensive set of experimental results showing that our proposed multimodal deep network architecture outperforms previous approaches, which have been designed for unimodal analysis, on the aforementioned AMI dataset.

Speaker Identity Recognition by Acoustic and Visual Data Fusion through Personal Privacy for Smart Care and Service Applications

Abstract With rapid developments in techniques related to the internet of things, smart service applications such as voice-command-based speech recognition and smart care applications such as context-aware-based emotion recognition will gain much attention and potentially be a requirement in smart home or office environments. In such intelligence applications, identity recognition of the specific member in indoor spaces will be a crucial issue. In this study, a combined audio-visual identity recognition approach was developed. In this approach, visual information obtained from face detection was incorporated into acoustic Gaussian likelihood calculations for constructing speaker classification trees to significantly enhance the Gaussian mixture model (GMM)-based speaker recognition method. This study considered the privacy of the monitored person and reduced the degree of surveillance. Moreover, the popular Kinect sensor device containing a microphone array was adopted to obtain acoustic voice data from the person. The proposed audio-visual identity recognition approach deploys only two cameras in a specific indoor space for conveniently performing face detection and quickly determining the total number of people in the specific space. Such information pertaining to the number of people in the indoor space obtained using face detection was utilized to effectively regulate the accurate GMM speaker classification tree design. Two face-detection-regulated speaker classification tree schemes are presented for the GMM speaker recognition method in this study—the binary speaker classification tree (GMM-BT) and the non-binary speaker classification tree (GMM-NBT). The proposed GMM-BT and GMM-NBT methods achieve excellent identity recognition rates of 84.28% and 83%, respectively; both values are higher than the rate of the conventional GMM approach (80.5%). Moreover, as the extremely complex calculations of face recognition in general audio-visual speaker recognition tasks are not required, the proposed approach is rapid and efficient with only a slight increment of 0.051 s in the average recognition time.

The Impact of Smartwork Activation on Organizational Performance - Focusing on Mediating Effects of Quality of Life and Moderating Effects of Manager’s Interest

Background/Objectives: The interest in work-life balance has increased in Korea since the low birthrate problem intensified in the 2000s, and recently, a 52-hour workweek has been implemented depending on the size of the company to ensure work-life balance. Companies are also adopting the direction and policies of the government to spread job redesign or organizational culture that can bring work-life balance. Work-life balance is seen as a paradigm for high performance in modern society. Activation of smart work, which is a system that can work anytime and anywhere without being bound by time and place, is inevitable. It is assumed that the quality of life of the members of the organization will be improved through smartwork activation, and that the improvement of the quality of life will soon increase the organizational performanceMethods/Statistical analysis: This study utilizes the original data from the 2018 Smartwork Survey conducted by the Korea Information Society Agency, targeting employees of businesses with five or more business units and CEOs of businesses or heads of departments responsible for introducing and operating smart work. The survey collected samples using stratified sampling methods, and the survey was conducted online and in writing to 1,700 people from October 8, 2018 for 27 days. Finally, 566 data were hypothesized through SPSS 21.0 and AMOS structural model analysis.Findings: As a result of analyzing the effect of smart work on organizational performance through the effect of quality on life, Smartwork activation affects organizational performance, and the improvement of quality of life through time saving and work efficiency due to smartwork affects organizational performance. Managers' interest was found to play a moderating role in the structural relationship between smartwork activation, quality of life and organizational performance.Improvements/Applications: This study suggests smartwork activation by empirically analyzing the effect of smartwork activation on organizational performance by the mediating effect of quality of life for employees of companies working in smart office environment.

Wireless Sensing for Human Activity: A Survey

With the advancement of wireless technologies and sensing methodologies, many studies have shown the success of re-using wireless signals (e.g., WiFi) to sense human activities and thereby realize a set of emerging applications, ranging from intrusion detection, daily activity recognition, gesture recognition to vital signs monitoring and user identification involving even finer-grained motion sensing. These applications arguably can brace various domains for smart home and office environments, including safety protection, well-being monitoring/management, smart healthcare and smart-appliance interaction. The movements of the human body impact the wireless signal propagation (e.g., reflection, diffraction and scattering), which provide great opportunities to capture human motions by analyzing the received wireless signals. Researchers take the advantage of the existing wireless links among mobile/smart devices (e.g., laptops, smartphones, smart thermostats, smart refrigerators and virtual assistance systems) by either extracting the ready-to-use signal measurements or adopting frequency modulated signals to detect the frequency shift. Due to the low-cost and non-intrusive sensing nature, wireless-based human activity sensing has drawn considerable attention and become a prominent research field over the past decade. In this paper, we survey the existing wireless sensing systems in terms of their basic principles, techniques and system structures. Particularly, we describe how the wireless signals could be utilized to facilitate an array of applications including intrusion detection, room occupancy monitoring, daily activity recognition, gesture recognition, vital signs monitoring, user identification and indoor localization. The future research directions and limitations of using wireless signals for human activity sensing are also discussed.

QR code based Visitor Management System for Smart Offices

In recent years, owing to the proliferation of the Internet of Things (IoT), homes and offices are being translated into smart homes and offices. The paper demonstrates an IoT system for managing the visitors in a smart office environment. The automation system comprises Raspberry Pi equipped with camera and an android mobile phone. The Raspberry Pi equipped with camera is kept in the visitor’s waiting area and the consulting person possesses an android mobile phone in which the visitor management software is installed. The visitor takes an appointment using the client application installed in his android mobile phone by selecting an available slot and receives a QR code. The consulting person has the access to information regarding each appointment through professional application installed in his android phone. Whenever the visitor shows the QR code to the Raspberry Pi camera, the professional application is notified and the consulting person is able to access the list of all the visitors in real time. An intelligent token system is used to determine the order of passage when several visitors are there at the office.

A Efficient Resource Utilization Flexible Trust Based Secure Signal Transmission in Pervasive Computing Environment

In today’s environment, pervasive computing plays important role to perform data transmission between IoT objects in distributed environment which communicate, each other directly without any centralized object. Security is very challenging task in pervasive computing where simulation environment is distributed. In this paper, an attempt has been made to resolve this kind of issues and a secure trust based signals transmission in distributed environment of pervasive computing mechanism has been proposed. In this trust value of each IoT object is calculated on the basis of their context information and these objects have been distinguished on the basis of this trust value that either the object is trusted or not. Later, propose a mechanism to enhance utilization of energy of resources used in smart office environment and this has been implemented in ONE (Opportunistic Network Environment) simulator.

Automatic Malware Detection with Machine Learning Algorithms in a Smart Office Environment

Automatic Malware Detection with Machine Learning Algorithms in a Smart Office Environment

The feasibility of highly granular lighting control in open-plan offices: Exploring the comfort and energy saving potential

Highly granular lighting control reduces the energy consumption of shared office spaces by automatically controlling lighting at unoccupied desks. Typically, lighting is switched off, but this approach results in non-uniform illuminance distributions. In offices where individual work areas are often not separated by partitions and occupants oversee the entire office space, a dimming approach might be more comfortable. Therefore, this study tests the feasibility of a new concept that involves the use of different dimming levels in the task, surrounding, and background area. We evaluated this concept on both user comfort and the energy use through a user study in a controlled environment (final N = 25). From this work, we concluded that most users (68–84%) are comfortable with different dimming levels throughout a workspace. Initial energy calculations based on assumed activity patterns showed promising results for the four most extreme dimming scenarios (LDP reduction > 26%). We identified two conditions as most promising in achieving both energy savings and comfort, namely when the dimming level was at minimum in the background area of the office space. These findings encourage research to continue investigating individual occupancy-based dimming as it has high practical value in the increasing smart office environment.

Using smart offices to predict occupational stress

Occupational stress is increasingly present in our society. Usually, it is detected too late, resulting in physical and mental health problems for the worker, as well as economic losses for the companies due to the consequent absenteeism, presenteeism, reduced motivation or staff turnover. Therefore, the development of early stress detection systems that allow individuals to take timely action and prevent irreversible damage is required. To address this need, we investigate a method to analyze changes in physiological and behavioral patterns using unobtrusively and ubiquitously gathered smart office data. The goal of this paper is to build models that predict self-assessed stress and mental workload scores, as well as models that predict workload conditions based on physiological and behavior data. Regression models were built for the prediction of the self-reported stress and mental workload scores from data based on real office work settings. Similarly, classification models were employed to detect workload conditions and change in these conditions. Specific algorithms to deal with class-imbalance (SMOTEBoost and RUSBoost) were also tested. Results confirm the predictability of behavioral changes for stress and mental workload levels, as well as for change in workload conditions. Results also suggest that computer-use patterns together with body posture and movements are the best predictors for this purpose. Moreover, the importance of self-reported scores' standardization and the suitability of the NASA Task Load Index test for workload assessment is noticed. This work contributes significantly towards the development of an unobtrusive and ubiquitous early stress detection system in smart office environments, whose implementation in the industrial environment would make a great beneficial impact on workers’ health status and on the economy of companies.

Behavior Recognition Using Multiple Depth Cameras Based on a Time-Variant Skeleton Vector Projection

User behavior recognition in a smart office environment is a challenging research task. Wearable sensors can be used to recognize behaviors, but such sensors could go unworn, making the recognition task unreliable. Cameras are also used to recognize behaviors, but occlusions and unstable lighting conditions reduce such methods’ recognition accuracy. To address these problems, we propose a time-variant skeleton vector projection scheme using multiple infrared-based depth cameras for behavior recognition. The contribution of this paper is threefold: 1) The proposed method can extract reliable projected skeleton vector features by compensating occluded data using nonoccluded data; 2) the proposed occlusion-based weighting element generation can be employed to train support-vector-machine-based classifiers to recognize behaviors in a multiple-view environment; and 3) the proposed method achieves superior behavior recognition accuracy and involves less computational complexity compared with other state-of-the-art methods for practical testing environments.

RETE-ADH: An Improvement to RETE for Composite Context-Aware Service

We propose a new pattern matching algorithm for composite context-aware services. The new algorithm, RETE-ADH, extends RETE to enhance systems that are based on the composite context-aware service architecture. RETE-ADH increases the speed of matching by searching only a subset of the rules that can be matched. In addition, RETE-ADH is scalable and suitable for parallelization. We describe the design of the proposed algorithm and present experimental results from a simulated smart office environment to compare the proposed algorithm with other pattern matching algorithms, showing that the proposed algorithm outperforms original RETE by 85%.

An Audit Model for Customer Relationship Management in Smart Mobile Environments

Since the supply of smart phones, a change in mobile environment brought a turning point called a mobile generation. Smart mobile office is a combined form of smart phones' new mobile environment and its social media. A construction of mobile office environment using smart phones brought revitalization of the smart phone market. CRM construction also became new requirements for a customer management. However, based on the current information system audit standard, check fields or check lists are insufficient to apply to audit for CRM construction in a smart mobile office environment. Therefore, this paper proposes a model for auditing CRM system construction in smart mobile office environment. It proposes audit domain and check lists of CRM construction. It also verified whether the proposed model is suitable or not by doing a survey if deduced audit domain and check lists correspond with the purpose of the CRM construction audit during smart mobile office environment. As the result, this study appear to have more than average satisfaction the suitability results were.