![\"\"](\"https:\/\/marcel-jan.eu\/datablog\/wp-content\/uploads\/2024\/11\/CleanShot-2024-11-04-at-22.10.52.png\")
<\/figure>\n\n\n\nI’ve been training harder in the last 2 years, but according to the fat measurements I gained more fat, not less. And also, after a day of a long bike ride, the fat percentage would peak the next day, instead of getting lower. In the last few months I would regularly get fat percentage measurements of 30+%. And it was not like I was eating burgers, fries and ice cream everyday. It didn’t look like the fat measurements were very accurate anymore.<\/p>\n\n\n\n
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My new scale<\/h2>\n\n\n\n
I decided it was time for a new personal scale. After some deliberation I picked the Robi S11. It is a “Smart body composition scale” according to the brochure. It has a handheld device that measures your body fat (and a whole lot of other things) more accurately. It is similar to how my doctor measures my fat percentage during my half yearly checkup. And it was moderately priced.<\/p>\n\n\n\n Now this is one of those scales that has a Bluetooth connection. I’ve always had a healthy mistrust of sharing my health data with apps like these. Especially when the parent company is one Guandong Icomon Technology. Who knows where your data goes to and how securely it is stored?<\/p>\n\n\n\n I decided to give their Fitdays app a try anyway. I filled in the limited amount of personal details (and not all of them entirely accurate). And of course I didn’t give the app any more access to iPhone data than absolutely necessary. For what it’s worth.<\/p>\n\n\n\n The device does an impressive amount of measurements. It measures not just weight, fat, water and muscle tissue. It can do so per arm and leg. And somehow it also can measure bone mass and protein mass in your body. Not sure how accurate and scientific all this is though.<\/p>\n\n\n\n The app shows all these results. And then came the little matter of me wanting to copy all that data. Luckily the app has a “share” option. I was able to Airdrop that data to my MacBook. So I was excited… until I got said data. Because it was in the form of a jpeg file.<\/p>\n\n\n\n You can’t copy the values. You can’t get the data in any other form. Good luck!<\/p>\n\n\n\n Good luck? Well we’ll see about that. I decided to summon the power of Python! Surely there must be some way to OCR the heck out of this jpg? And, as almost ever, there is a Python solution. Quite quickly I learned there is a Python package called pytesseract that can do OCR.<\/p>\n\n\n\n For a first attempt the code is fairly simple:<\/p>\n\n\n\n And sure enough, when you run it, you get this result:<\/p>\n\n\n\n Now all I have to do is select the lines with the data that I want, write it to a cleaned up data output, and I have my data in consumable form.<\/p>\n\n\n\n I got a lot of data out of this. But not all. For example, on this multiline name it would get the text, but value was wrong:<\/p>\n\n\n\n As you can see in the result here:<\/p>\n\n\n\n It was probably confused by the value being in the middle of the multiline name?<\/p>\n\n\n\n Also it would not get the text from this part with the human image:<\/p>\n\n\n\n It would not get the numbers here (except the “Standard range”):<\/p>\n\n\n\n Maybe that’s something to look into in a later phase.<\/p>\n\n\n\n In any case, I was pretty happy about how easy it was to get the first results. I got enough out of it to start with. Hiding my data in a jpeg is no match for some rudimentary Python skills anymore.<\/p>\n\n\n\n I’ve put my Python code in a Github repository: https:\/\/github.com\/Marcel-Jan\/extract_fitdays_data<\/a><\/p>\n\n\n\n <\/p>\n\n\n\n I’ve been thinking how to improve the quality of the results from pytesseract. One approach is to cut parts of the image out, so it can “focus” on these.<\/p>\n\n\n\n But I also read you can do other forms of preprocessing of the image that can help. Like what I read in this post:<\/p>\n\n\n\n![\"\"](\"https:\/\/marcel-jan.eu\/datablog\/wp-content\/uploads\/2024\/11\/image.png\")
<\/figure>\n\n\n\n![\"\"](\"https:\/\/marcel-jan.eu\/datablog\/wp-content\/uploads\/2024\/11\/CleanShot-2024-11-04-at-22.33.45.png\")
Your data, in jpeg form<\/h2>\n\n\n\n
Using pytesseract for OCR<\/h2>\n\n\n\n
import pytesseract\nfrom PIL import Image\n\nim = Image.open(\"IMG_69EC2B66C329-1.jpeg\") # the ROBI image with data\ntext = pytesseract.image_to_string(im)\n\nprint(text)<\/code><\/pre>\n\n\n\n83.2 kg 18.7 %\n\nGewicht Lichaamsvet\n\nIndicator Waarde Standaard\n\nGewicht 83.2kg Standaard\nBMI 23.0 Standaard\nLichaamsvet 18.7% Standaard\n\nVetmassa 15.6kg Standaard\n\nVetvrij\n\nlichaamsgewicht 878k\n\nSpiermassa 63.1kg Standaard\nSpiersnelheid 75.8% Standaard\nSkeletspier 46.5% Standaard\nBotmassa 4.5kg Standaard\nEiwitmassa 13.5kg Standaard\nEiwit 16.2% Standaard\nWatergewicht 49.6kg Standaard\nLichaamswater 59.6% Standaard\nOnderhuids vet 13.4% Standaard\nVisceraal vet 5.0 Standaard\nBMR 1830kcal\n\nLichaamsleeftijd 52 Uitstekend\n\nWHR 0.90 Standaard<\/code><\/pre>\n\n\n\n![\"\"](\"https:\/\/marcel-jan.eu\/datablog\/wp-content\/uploads\/2024\/11\/CleanShot-2024-11-04-at-22.53.52.png\")
<\/figure>\n\n\n\nVetvrij\n\nlichaamsgewicht 878k<\/code><\/pre>\n\n\n\n![\"\"](\"https:\/\/marcel-jan.eu\/datablog\/wp-content\/uploads\/2024\/11\/CleanShot-2024-11-04-at-22.55.52.png\")
<\/figure>\n\n\n\nSegmentale vetanalyse\n\nStandaardbereik: 80%-160%\n\nStandaard\n\nStandaard \\\\ Standaard\nl R l<\/code><\/pre>\n\n\n\nFurther research<\/h2>\n\n\n\n