Indoor positioning data analytics

A dashboard can be defined as a graphical user interface that often provides at-a-glance views of key performance indicators (KPIs) relevant to a particular objective or business process. In another usage, ‘dashboard’ is a technical term for ‘progress report’ or ‘activity report’ and is considered a form of data visualisation. Here’s a blog on how the dashboard is structured and the tools used for Indoor Positioning data analytics. Gain new insights for warehouse tracking systems and hospital asset tracking systems.

Like smart warehouses, hospitals are the places where the maximum amount of deployments (patients, staff, assets, inventories) occur. The installation of an Indoor positioning System in such workplaces is a necessary thing for a smooth workflow. In the monitoring process of such deployments, hardware components like beacons, tags, anchors play a crucial role. At the same time, the data signals collected by these components can be perceived only through software, called shopfloor. It is a central managing unit that acts as a dashboard, where a user can monitor, surveil, access, receive signals and analyze those signals in real-time using several data analytic tools. A user can monitor every signal from the sensors through the floor map. The data received from these sensors are collected and projected in the form of the pivot table. 

Pivot Table for indoor positioning matrices

The pivot table consists of fixed variables like date, zone, floor, object name, category, staff details in its rows, and changeable variables like name of the zones (OPD block, reception, exit way, etc), floors, cluster names in its columns. The pivot table also provides an environment monitoring option to keep the room’s humidity, temperature, and airflow at optimum levels. 

The ultimate feature offered by the Indoor Positioning Analytics solution is its unique customization for representing those collected data. The data can be represented either or altogether of the following ways,

• Visual representation
• Quantitative representation
• Parameter to be represented
• Custom sorting

Visual Representation – The data received from the sensors are projected onto the pivot table through visual representation tools. It will be represented in the form of various illustrations (not limited to) such as histogram, box plot, heat map, bar graph, line graph, and frequency table.

Parameter to be represented – The quantitative data obtained from the sensors are displayed on the pivot table as the numerical entries based on various parameters. These parameters can be selected based on needs. The parameters include time spent in a room, the temperature of a room, number of persons in a zone, number of visits paid to a zone, etc. If one wants to know the number of visits made by a physician to a particular zone, the parameter to be selected will be ‘VISIT COUNT’, which is customizable.

Quantitative Representation – Once the parameter is decided, the numerical entries can be represented by an array of quantitative parameters. These quantitative arrays include the sum, average, range, median, mode, standard deviation, etc. For example, if the parameter selected is visit count, then the data can be viewed as the sum of the visit counts by a physician or an average of the visit counts and so on. 

Custom Sorting – This is a basic feature tool available in the activity report dashboard. It facilitates sorting the data entries in ascending and descending order. This sorting can be done by both horizontal rows and vertical columns of the pivot table for effective indoor positioning data analytics.

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