New Hampshire Patent of the Month – June 2021
Environmental monitoring of conditions like humidity, temperature, motin, and so on is critical in scientific laboratories, warehouses, and a variety of other institutions. In some setups, a sensor is rigged to trigger an alarm or notification if the condition surpasses a threshold – for instance if the temperature falls below a certain level. However, these systems don’t necessarily convey the required information, such as how long it’s been outside of the optimal range or how far it is from the threshold and therefore fail to provide some much needed context around the alarm. Elemental Machines, Inc. designed a more intelligent sensor system that provides this context and better differentiates between false and true alarms.
Their system uses multiple sensors to track and detect a range of data. This can then verify an alarm condition by referencing and analyzing the available data. This way, only a “true” alarm is triggered and only when this anomalous data does not match typical activities. The system further monitors and analyzes this anomalous data to predict if the data is anticipated to cross thresholds and communicates the time of this predicted crossing.
For instance, a single condition, such as temperature, is influenced by a great number of things. So measuring other conditions alongside it can provide context as to what is causing a change in temperature. For instance, the inclusion of sensors to track humidity, motion, and number of individuals interacting with the environment builds a more complete picture of the activity being observed. Their intelligent sensor system uses algorithms to describe how these different sensors are measured and interact with each other. This offers contextual information about the crossing of a threshold and allows for predictions. For instance, if the algorithms notice there is an increased number of people in the environment, it can expect the temperature to rise due to the increased body heat – and can notify the user of this information. This intelligent sensor can better assist in quality assurance and monitoring by providing context for changes and errors.
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