During the recent TMA 2022 Annual Meeting Marco Island., Fla., I addressed the topic of presence detection. For this Monitoring Today column, I want to focus on its value, especially how it relates when converged with other platforms.

Presence detection and the attributed AI associated with the various platforms has progressed significantly in the past few years. Most of what’s referred to as artificial intelligence (AI) is essentially very sophisticated machine learning (ML). ML is a subfield of AI, which is essentially the capability of a machine to imitate intelligent human behavior. The AI or ML systems are leveraged by video and presence detection platforms to analyze data points and activity and perform complex tasks in a way that is similar to how humans solve problems.

As with video-related AI, presence detection comes in a variety of flavors that bring a variety of advantages depending on the goals of the proposition. Having said that, the use and understanding of AI with video or as part of a presence detection system can be close to identical. For example, video AI can be set to detect certain behaviors as a person walking from left to right rather than right to left, or someone running fast rather than walking at a steady pace or even a person falling down.

This can all be easily accomplished through sophisticated video AI. Although this is all extremely valuable, leveraging video AI requires the use of multiple cameras at the proper angles to assure ample coverage of the entire area desired for monitoring. Additionally, if it is a location where privacy is required, video may be frowned upon and not an option.

With a presence detection platform focused on behavior and activity, this can all be accomplished without compromising privacy as these systems do not use cameras or video. Systems that concentrate on activity and behavior sensing and monitoring are very well suited for aging in place activity and lack of activity monitoring in a non-intrusive manner, for example.

These platforms utilize Wi-Fi sensing or Wi-Fi radar, wireless AI sensing and ambient sensing, which allows for the analyzing of a variety of metrics when monitoring the behavior and well-being of subscribers, in addition to all activity within a premises. These systems provide full premises coverage as the technology coverage is not reduced by walls and doors. Companies such as Origin Wireless have dedicated tremendous resources developing and honing this technology and bringing it to market.

In addition to the aging in place model, this technology approach is valuable when utilized and converged with a legacy intrusion systems and monitoring. The more relevant information a central station monitoring center can process during an event, the better chance exists to either verify a condition or avoid a catastrophic condition or loss. When a monitoring center can definitively verify through AI or other means that movement and activity exist in a premise that triggered an alarm, they and the authorities have much more to work with toward a suitable result.

An approach to AI and presence detection that has intrigued me the most is the technology introduced by Ubiety and marketed to the consumer market as HomeAware. Platforms such as Ubiety monitor all wireless devices that transmit radio signals and standardized languages such as Wi-Fi, Bluetooth and cellular. They emit these signals to transmit information or periodically look for and inform other devices and networks they are present. Other leaders in this category of technologies and platforms include Aloe Care, Cognitive Systems, Intellithings and Origin Wireless.

Although sensing and cataloging any wireless device that enters and exits a premise doesn’t sound all that exciting, layering in the AI/ML takes this to a new level. Yes, the data is important but what the AI is able to do with this data is what really delivers on a great value proposition. Just think about the value of knowing who is or is not within the confines of a premise at any time — during an alarm event or just during a certain period of any given day.

The AI would catalogue all devices that enter and exit, even how long they may stay on premise. As with any AI, the more data recorded, the more of a basis exists for a decision when necessary. An event could trigger and the monitoring center would know who was on premise or who may have recently departed just from the sensing of their devices or vehicles. At the least this can lead to more verifications and in real cases can identify that the action within or around the premise is stimulated by someone who never visited that premise.

All that’s been described here is really the tip of the iceberg. These technologies alone and in collaboration with other systems will be major disrupters. Information is of paramount importance and from what I see systems such as these as I described will carry the most relevance for all-around security, and I believe law enforcement will engage in a big way.

Although the platforms may leverage different technologies in delivering these services, they are all able to monitor, analyze and report the various points without compromising security or personal data of any individual detected or monitored. Although I draw the comparison to video AI, I am not suggesting that video AI is any less valuable. These systems will work in collaboration in some instances. They will also work standalone when certain requirements dictate this need.

Pay close attention. This is a disrupter like we haven’t experienced in quite some time that I believe will have a long, lasting positive effect on how systems are deployed and managed for many years to come.