A RASPBERRY PI-BASED AI WORKSPACE MONITORING SYSTEM

Abstract

A Raspberry Pi-based AI workspace monitoring system uses multiple sensors, including a camera, an infrared sensor, and a Lidar sensor, for real-time workspace occupancy monitoring. The system is integrated with a Neural Stick to improve processing power and is incorporated into a centralized monitoring and analysis framework. The system can also send real-time alerts when unusual occupancy levels are detected.

Specification

Description:FIELD OF THE INVENTION
This invention relates to workspace utilization monitoring using computer vision.
BACKGROUND OF THE INVENTION.
E In the modern workplace, efficient space utilization is a key component that reduces cost and serves employees better. For instance, traditional techniques of monitoring and evaluating space utilization like manual audits or simple occupancy sensors often led to incorrect information and missed chances for improvement. But now, there have been several advancements in computer vision technology so that AI can be used to observe different parts of an office on a live basis. This innovation can also count the number of people who are found within one area and ascertain if they are far apart enough from each other or not.
There are cameras available for companies to use which are connected to edge devices such as Jetson Nano or Raspberry Pi for processing data locally. By doing so, it is possible for the camera system providers operating on this model is not only guaranteeing security but also provides valuable insights into how people are using their workplaces. This way, organizations can optimize their physical spaces as well as make them more adaptable to the changing needs in real-time.
Both hardware and software should be taken into consideration when establishing such a system. It is possible to place cameras at strategic locations within the office that will feed back into edge devices like Jetson Nano or Raspberry Pi which locally process those feeds. Consequently, employee privacy never gets compromised with an uninterruptible transmission of video feeds from the office to edge devices through Wi-Fi.
AI models in these cases can look at movement trends, number of occupants and staff interrelationships. The administration gives the team a chance to have an idea on how to approach different situations. It is possible to combine collected data for visual display into dashboards which provide information necessary for HR and facility managers. For example, if it is established that some meeting rooms are regularly underutilized, they could be converted to other uses. Also, areas with high movement could be considered for expanding or redesigning them so as increase comfort and productivity.
To get more than space optimization with this method is its benefit. Comprehending the manner in which different areas are utilized, businesses may make such environments that better bolster employee efficiency and welfare. In addition, this data-based strategy can help to decide on the best arrangement of desks and chairs at offices or where to put a coffee point or collaboration rooms. Also with the hybrid workplace models as the new normal after covid19 pandemic; real-time monitoring of workspaces allows companies to be flexible with varying occupational density so that office remains vibrant and supportive. Through investing in computer vision-based workspace utilization monitoring, organizations will enhance both the efficiency and experience of their physical workspaces making them more responsive to changes within workforce needs.
US10965482B2 A building management system and method that determines space utilization in a building. The building management system comprises room utilization sensors, such as occupancy sensors, a gateway that gathers raw occupancy data records from the occupancy sensors, and at least one processor. Each raw occupancy data record comprises an occupancy state associated with a timestamp. The processor is configured for normalizing the raw occupancy data records into normalized occupancy data records each comprising a time segment and an occupancy state indicating whether for a given time segment a room is occupied. The processor is further configured for structuring the normalized occupancy data records by associating each normalized occupancy data record with a space node corresponding to a room in the building from where the raw occupancy data record was collected and associating each space node with a set of tags, each tag defining a single state from a plurality of states. The processor may further receive a selection of a tag from a user interface, split the normalized occupancy data records along the selected tag's states into sets of normalized occupancy data records, for each split set of occupancy data records, calculate at least one occupancy rate, and display a visualization of space utilization of the building comprising a comparison of the occupancy rates of the selected tag's states.
RESEARCH GAP: Enhanced Space Efficiency: With its continuous monitoring of how various sections of the office are used, the system identifies spaces that are not fully utilized so that organizations can reallocate or change their use. This optimization results in better utilization of existing space, thus minimizing the need to rent more offices and consequently reducing operational expenses.
TWI743642B Systems for managing a workspace are disclosed. A system to manage a workspace includes a plurality of docking stations located at corresponding workstations. Each docking station is configured to provide a network connection and power to a computer device at a corresponding workstation. Each docking station of the plurality of docking stations includes a power input and a network interface to communicate with a network. The system also includes a system computer including a system network interface to communicate with each docking station of the plurality of docking stations via the network.
RESEARCH GAP: Improved Employee Productivity: Real-time insights on workspace utilization enable HR and facility managers to create environments that meet employees' preferences. For example, overcrowded areas could be reconfigured for enhanced comfort, creating a conducive work environment that boosts employee output and satisfaction.
US8577711B2 The disclosed embodiments relate to transparent and/or non-disruptive systems and methods for monitoring and analyzing actual space utilization, and in particular, analyzing space utilization over time and/or in real time to accurately understand and report the utilization of the space. In particular, the disclosed embodiments analyze data representative of occupancy of the space, the data being autonomously determined based on the likelihood that occupants are present within the space. The data is periodically determined and automatically reported to an automated collection system which collects the data and forwards it to a central repository for analysis. The data is then analyzed to provide meaningful reports regarding occupancy of the space or otherwise contextualize the utilization of the space. Further, the data collected from different spaces may be anonymized, aggregated, or otherwise combined, and analyzed to contextualize a given space's utilization, provide utilization forecasts, etc.
RESEARCH GAP: Informed Decision-Making: The data gathered from this monitoring system produce actionable insights necessary for making take strategic decisions like altering office layout plans or investing in new facilities. By using live usage data in order to make these determinations, organizations can ensure that their workspaces satisfy both current and future needs.
US10459611B1 A method and system for anonymously associating a workstation user's station control preferences with a workstation, the method comprising the steps of correlating anonymous user IDs with user preference sets in a database, obtaining input from a user at a workstation, comparing the user input to the anonymous user IDs to distinguish one distinguished user from other users without determining the identity of the user, accessing the user preference set associated with the distinguished user and controlling workstation affordances per the accessed user preferences while the user is located within a present zone proximate the workstation.
RESEARCH GAP: Increased Workplace Safety: After the pandemic conditions have eased up somewhat, it can still verify if offices adhere to social distancing guidelines while alerting managers about overcrowded areas. Such a proactive approach maintains a healthier working place resulting into less sicknesses and better employee welfare generally.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to workspace utilization monitoring using computer vision.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
To begin with, the first picture explains a well-thought-out device configuration for monitoring how workspace is used. The system is based on Raspberry Pi (13) that can be referred to as a jack of all trades microcomputer and works in the capacity of the central processing unit. To boost its computational power, especially when running AI-based tasks such as object detection using YOLO, it gets connected to a Neural Stick (9). The snapshot or picture (8) takes videos of what is happening at the moment in those offices and sends this information to Raspberry Pi for analysis.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: GENERAL ARCHITECTURE OF THE PRESENT INVENTION
FIGURE 2: DETAILED DEVICE STRUCTURE OF THE PRESENT INVENTION
FIGURE 3: FLOWCHART OF THE PRESENT INVENTION
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a"," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", "third", and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
To begin with, the first picture explains a well-thought-out device configuration for monitoring how workspace is used. The system is based on Raspberry Pi (13) that can be referred to as a jack of all trades microcomputer and works in the capacity of the central processing unit. To boost its computational power, especially when running AI-based tasks such as object detection using YOLO, it gets connected to a Neural Stick (9). The snapshot or picture (8) takes videos of what is happening at the moment in those offices and sends this information to Raspberry Pi for analysis.
In addition, the system has a Lidar Sensor (15) and Infrared Sensor (12) incorporated into it to provide depth sensing and presence detection, thereby increasing the accuracy of captured data.
Moreover, Raspberry Pi is equipped with WiFi/Bluetooth modules (6) for seamless communication and data transfer which are used to send processed data to a Cloud Server (2) for long-term storage and further analysis. The Microcontroller (14) works with Raspberry Pi in conjunction with external input devices such as Keyboard (10), Mouse (11), and output devices including Display Interface(5). This way all other components are also connected. The complete system operates throughout using a 12V 3amp Lithium Polymer Battery(16), which ensures that the power is not disrupted. Power management is supported by a dedicated Power Supply Management module having three parts; DC Outlet(18), Charger(19).
The second diagram outlines the algorithmic flow of Workspace Utilization Monitoring system starting with Video Feed Acquisition and ending at Visualization and decision making processes. The system's cameras capture video data to start the process. This happens through Preprocessing stage in which initial filtering to ready it for further analysis is done. A Condition Box is a critical decision point, representing if the workspace occupancy level is normal or not. The system continues monitoring if so, otherwise a signal will be sent by it indicating overutilization or underutilization of work space.
This is followed by preprocessing of the data which is then subjected to Object Detection and Tracking such as YOLO that recognizes and tracks people within the work area. This is important in order to know how people move and how many are usually present in the room. The next step involves transmitting the detected objects to a central server or cloud for it to be analyzed. Another choice point checks if there are any underutilized or overcrowded areas around them. Depending on these outcomes, the system proposes Reassignment or Restructuring of workspace or keeps watching.
Finally, the data undergoes an analysis, and after that it is sent to Visualization stage for display on a Dashboard in real time.
ADVANTAGES OF THE INVENTION
Optimizing Office Space Utilization: This system enables organizations to identify where their office spaces are not properly used in order to facilitate reshuffling or renovation of such areas especially in resource constrained environments when the aim is to improve employees' working conditions.
Boosting Worker Productivity; this system can help in planning workspaces that attend to the needs of employees by understanding how different spaces are utilized. The essence of this is that a spatial layout which has been well designed enhances productivity generally.
Reducing Real Estate and Operating Costs: Reducing physical space too reduces operating costs like maintenance and utility expenses tremendously, hence companies should downsize or remodel underutilized offices.
Enhancing Space Efficiency-Through monitoring office space usage continually, it's possible for these systems to locate areas within an organization's premises that remain largely unoccupied. In doing so, they enable reassigning or repurposing them which ensures that available resources are put into better use and minimizes necessity for more office rentals thus reducing operational costs.
Employee productivity enhancement: Real-time data on the usage of workspaces can inform HR and facility managers to create environments that are more in line with employee preferences. For instance, congested spaces could be redesigned to enhance comfort hence creating an enabling working atmosphere that raises employees' morale and output.
Informed Decision-Making Support: This acquired information from the system helps organizations make strategic changes such as office layouts or new facilities. Workspaces should meet present and future requirements in companies by using real time usage data.
Office Safety Improvement: The system is still useful for the offices to follow pandemic social distancing protocols so-called overcrowding for managers may be avoided. By doing this, it helps the institution have a better working environment reducing diseases among workers and improving their general well-being.
, Claims:1. A Raspberry Pi-based AI workspace monitoring system comprising:
a camera configured to capture video data;
an infrared sensor configured to detect presence;
a Lidar sensor configured to sense depth;
a Neural Stick configured to boost computational power;
a Raspberry Pi configured to centrally process data; and
a WiFi/Bluetooth module configured for data transfer and communication.
2. The system as claimed in claim 1, wherein the camera and the Lidar sensor are configured to capture real-time occupancy data with high precision.
3. The system as claimed in claim 1, wherein the Raspberry Pi is configured to execute object detection algorithms to identify and track individuals.
4. The system as claimed in claim 1, wherein the WiFi/Bluetooth module is configured to send data to a cloud server for remote storage and reporting.
5. The system as claimed in claim 1, further comprising a microcontroller configured to interface with external devices including a keyboard, a mouse, and a display interface.
6. The system as claimed in claim 1, further comprising a Power Supply Management module comprising a 12V 3amp Lithium Polymer Battery, a DC outlet, and a charger current manager.
7. The system as claimed in claim 1, wherein the infrared sensor is configured to detect presence during low-light situations.
8. The system as claimed in claim 1, wherein the system is configured to send real-time alerts when unusual occupancy levels are detected.
9. The system as claimed in claim 1, wherein the Raspberry Pi is configured to generate a visualized dashboard for display on a screen.

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