{"id":4616,"date":"2020-01-17T09:58:20","date_gmt":"2020-01-17T09:58:20","guid":{"rendered":"https:\/\/www.beaconzone.co.uk\/blog\/?p=4616"},"modified":"2020-01-17T10:19:27","modified_gmt":"2020-01-17T10:19:27","slug":"understanding-sensor-beacon-accelerometer-data","status":"publish","type":"post","link":"https:\/\/www.beaconzone.co.uk\/blog\/understanding-sensor-beacon-accelerometer-data\/","title":{"rendered":"Understanding Sensor Beacon Accelerometer Data"},"content":{"rendered":"\n

In this post we will take a look at data from the INGICS iGS01RG beacon<\/a>.<\/p>\n\n\n\n

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The x axis is time. You can see the x, y and z values, every 100ms, over time. The y axis is normalised between -1 and -1 for use in our SensorCognition<\/a> Edge device. The chart is for when the beacon has been moving, followed by a stationary period. Notice how the orange line continues to show acceleration even though the beacon isn’t moving. This is caused by gravity.<\/p>\n\n\n\n

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In this chart the beacon has been flipped over and the orange line now shows a constant negative acceleration.<\/p>\n\n\n\n

A good thing about the presence of a constant offset in one of the x y z inputs is that it can be used to help determine the orientation of the beacon. The less desirable aspect is that the offset significantly complicates using the x y z to determine types of movement such as human gestures. <\/p>\n\n\n\n

Such complex data problems are more easily solved using AI machine learning than trying to write a traditional algorithm to make sense of the data.<\/p>\n\n\n\n

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Here’s an example of output from a SensorCognition Edge device trained with up and down movement and left and right movement. In this case, the output 227 is showing the beacon is moving left and right.<\/p>\n\n\n\n

Read about SensorCognition<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

In this post we will take a look at data from the INGICS iGS01RG beacon. The x axis is time. You can see the x, y and z values, every 100ms, over time. The y axis is normalised between -1 and -1 for use in our SensorCognition Edge device. The chart is for when the … Continue reading “Understanding Sensor Beacon Accelerometer Data”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[173,227,5],"tags":[],"_links":{"self":[{"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/posts\/4616"}],"collection":[{"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/comments?post=4616"}],"version-history":[{"count":4,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/posts\/4616\/revisions"}],"predecessor-version":[{"id":4625,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/posts\/4616\/revisions\/4625"}],"wp:attachment":[{"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/media?parent=4616"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/categories?post=4616"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.beaconzone.co.uk\/blog\/wp-json\/wp\/v2\/tags?post=4616"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}