Due to remodeling construction in the lobby area where these sensors are located, the data feeds are down. As such, this phase of this project is complete. Sorry for the abrupt closure, but this is an unforeseen and sudden event. However! We are planning a new sensor setup soon in a in a convenient store environment and will include additional data beyond Human Direction Direction. Stay Tune!
REAL! Real-time IoT data stream available for Pentaho Analysis and Visualization
Everyone knows how hard it is to get access to real-time data feeds. Well, here is a chance to access real-time data using a 3D LiDAR motion sensor.
There has been a lot of talk about the new 3D LiDAR (Light Radar) motion sensor from Hitachi LG Data Systems (HLDS) recently. The 3D LiDAR is a Time of Flight (ToF) motion sensor that calculates distance by measuring the time it takes for an infrared laser to emit light and receive the reflection back. Because it measures a pixel-by-pixel image via the sensor, it shows the shape, size and position of a human and/or an object in 3D at 10 to 30 fps (frames per second), so it is possible to detect and track the motion, direction, height, volume, etc. of humans or objects.
Unfortunately, general access to this sensor it a bit difficult to come by at the moment and setting one up in a useful location, like a bank, retail store or casino, is also a challenge. So, in a partnership with HLDS, we have setup a LiDAR configuration at a company lobby on the 8th floor at HLDS in Seoul South Korea and will make the real-time output stream available to Hitachi Vantara Pentaho developers to use and develop to. The real-time data stream will be published from an MQTT broker at,
There is a problem with the original broker and we have moved this
data stream to a new broker. Please note the new broker URL below.
Sorry for the inconvenience.
Broker location – tcp://iot.eclipse.org:1883 tcp://mqtt.iot-hlds.com:1883
Topic – hlds/korea/TOFData
An example .json formatted data record published from this broker and topic looks like this,
The data stream will be published in clear text. The data is not sensitive. We are looking for real-time dashboards, visuals, analytics and integration transformations.
To help start this off, there is a collection of transformations to start from here.
The setup scenario is a “Human Direction Detection” challenge using the filter processor "Human Counter Pro". There are two zones being monitored by the 2 ceiling mounted LiDARs (the two LiDARs are grouped together to cover the wide area). The first zone is the entrance area called “entrance” and the second zone is the lobby area called the “hallway”. What can be happening in this configuration scenario is that,
- People arrive (out of the elevator) and enter the “entrance” area, then they enter the “hallway” area, and are either walking towards the South Wing doorway or the North Wing doorway. This is the most common scenario and is basically employees arriving on their floor and heading to their work area.
- This scenario can also happen in reverse order where people enter in the "hallway" from either the North Wing or South Wing and enter the "entrance" signifying leaving.
- Someone enters and stays in the “hallway” for a period of time. Someone or others arrive in the entrance area and the group heads to one of the doorways. This scenario is basically an employee waiting for visitors to be escorted to a meeting or other activity.
- Someone or a group crosses the “hallway” from the South Wing to the North Wing, or from the North Wing to the South Wing. This is a scenario where people are crossing over from one side of the building to the other side.
- Someone enters the “hallway” area and stays there for a period of time, then heads to one of the doorways. In this scenario, someone is probably looking at one of the live demos or items in the lobby’s display area.
- There could be other scenarios that you can identify with the data from the LiDARs, these are just a few that we came up with.
The published data stream will have identified and tracked people as they move into the “entrance” area and then move to the “hallway” area. Timing information of when each person enters (Appear) in the zones and when they leave (Disappear) the zone. Duration time in the zones area will need to be calculated yourself.
Lastly, remember South Korea is 16 hours ahead of pacific time, so the work day and work week activity is very skewed. It will be busy in the evening pacific time, and it will be the weekend on Friday pacific time.
You can use a MQTT inspection tool like "MQTT Spy" to explore and examine the data coming from the sensor.
Originally, this was going to be setup for me, then it was discussed that since this is an MQTT design, we can open this up company wide. Access to real world IoT data is hard to come by.
There are other Processor Filters in the LiDAR device middle-ware suite that provide different functions from the sensor. We are starting with the Human Counter Pro because this one publishes via MQTT. If this is successful, the other Processor Filters will also be integrated with MQTT as a simple mechanism for integrating Pentaho to the LiDAR sensor, and future physical sensors and Processing Filters.
No special plugin development is required to integrate to a state-of-the-art motion sensor to Pentaho. We’ve had access to MQTT steps for PDI for a few years now. There are a few blogs in the Vantara Community here and here describing how to use MQTT with Pentaho.
Some analysis ideas,
- How many people entered the “entrance” only and then “Disappeared” (wrong floor?)?
- How many people exited from “entrance”?
- How many people went to North Wing?
- How many people went to South Wing?
- How many people crossed the “hallway”?
- How long did people stay in the “hallway”?
- Most people in the “hallway” at what times of the day?
- Does the time of day matter?
- What reports, visuals, dashboards and/or real-time dashboards can be created from this data?
Please share what you come up with in the comments section and/or submit your own write-up or blog. Who knows, there might be some recognition in it for you. Enjoy!