{"id":10391,"date":"2015-05-30T10:00:31","date_gmt":"2015-05-30T14:00:31","guid":{"rendered":"http:\/\/embedded-lab.com\/blog\/?p=10391"},"modified":"2015-05-30T10:00:31","modified_gmt":"2015-05-30T14:00:31","slug":"hacking-bathroom-weighscale-cloud-connectivity","status":"publish","type":"post","link":"https:\/\/embedded-lab.com\/blog\/hacking-bathroom-weighscale-cloud-connectivity\/","title":{"rendered":"Hacking a bathroom weighscale for cloud connectivity"},"content":{"rendered":"<p>Hardware hacker <em><strong>Darrel Tan<\/strong><\/em> has a great write-up about his <a href=\"http:\/\/irq5.io\/2014\/09\/25\/cloud-enabling-a-bathroom-scale\/#more-814\" target=\"_blank\"><strong>experience with hacking a Chinese-made bathroom scale for cloud connectivity<\/strong><\/a> using a Raspberry Pi. His bathroom weighscale has a separate display part that receves the weight measurements from the scale through an infrared link. Darrel\u00a0hooked up an 38kHz IR receiver to the logic analyzer and placed it near the scale as he was standing on it, so that he could decode the IR bursts. He found that the device used\u00a0<em>pulse distance coding<\/em>\u00a0to encode the data with ~75ms spacing between the consecutive bursts. With further analysis, he was able to figure out how ones and zeros are transmitted and how the weight data is represented in the transmitted stream between the scale and display. After the most difficult part of the hack done, he then hooked up a\u00a0Vishay TSOP4138 IR receiver to one of the Raspberry Pi&#8217;s I\/O pins to send the data to the Google Spreadsheet\u00a0for recording and future plotting.<\/p>\n<div id=\"attachment_10393\" style=\"width: 595px\" class=\"wp-caption aligncenter\"><a href=\"http:\/\/embedded-lab.com\/blog\/?attachment_id=10393\" rel=\"attachment wp-att-10393\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-10393\" class=\"size-full wp-image-10393\" alt=\"hh\" src=\"http:\/\/embedded-lab.com\/blog\/wp-content\/uploads\/2015\/05\/hackScale.jpg\" width=\"585\" height=\"403\" srcset=\"https:\/\/embedded-lab.com\/blog\/wp-content\/uploads\/2015\/05\/hackScale.jpg 585w, https:\/\/embedded-lab.com\/blog\/wp-content\/uploads\/2015\/05\/hackScale-550x378.jpg 550w\" sizes=\"auto, (max-width: 585px) 100vw, 585px\" \/><\/a><p id=\"caption-attachment-10393\" class=\"wp-caption-text\">Hacking a bathroom weighing scale for cloud connectivity<\/p><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Hardware hacker Darrel Tan has a great write-up about his experience with hacking a Chinese-made bathroom scale for cloud connectivity using a Raspberry Pi. His bathroom weighscale has a separate display part that receves the weight measurements from the scale through an infrared link. Darrel\u00a0hooked up an 38kHz IR receiver to the logic analyzer and placed it near the scale as he was standing on it, so that he could decode the IR bursts. He found that the device used\u00a0pulse distance coding\u00a0to encode the data with ~75ms spacing between the consecutive bursts. With further analysis, he was able to figure<\/p>\n","protected":false},"author":1,"featured_media":10393,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[602],"tags":[1194,713],"class_list":["post-10391","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-raspberry-pie","tag-cloud-connectvity","tag-raspberry-pi"],"_links":{"self":[{"href":"https:\/\/embedded-lab.com\/blog\/wp-json\/wp\/v2\/posts\/10391","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/embedded-lab.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/embedded-lab.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/embedded-lab.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/embedded-lab.com\/blog\/wp-json\/wp\/v2\/comments?post=10391"}],"version-history":[{"count":4,"href":"https:\/\/embedded-lab.com\/blog\/wp-json\/wp\/v2\/posts\/10391\/revisions"}],"predecessor-version":[{"id":10396,"href":"https:\/\/embedded-lab.com\/blog\/wp-json\/wp\/v2\/posts\/10391\/revisions\/10396"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/embedded-lab.com\/blog\/wp-json\/wp\/v2\/media\/10393"}],"wp:attachment":[{"href":"https:\/\/embedded-lab.com\/blog\/wp-json\/wp\/v2\/media?parent=10391"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/embedded-lab.com\/blog\/wp-json\/wp\/v2\/categories?post=10391"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/embedded-lab.com\/blog\/wp-json\/wp\/v2\/tags?post=10391"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}