Gait Analyzer

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Gait Analyzer

When ever I visit my spine doctor, the first thing they ask me to do is to walk for checking my gait. I've been wanting to analyze my gait myself for a long time and only now its been possible due to accessible & efficient ML/AI and the ability to run complex models on local computer. Gait abnormalities can be attributed to various musculoskeletal and neurological conditions and so gait analysis is being used as an important diagnostic tool by doctors. Automated gait analysis requires expensive motion capture or multiple-camera systems. But with Gait Analyzer one can analyze their gait in comfort and privacy of their home on their computer. Gait Analyzer implements the algorithm published in the paper titled Automated Gait Analysis Based on a Marker-Free Pose Estimation Model - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10384445/ . This algorithm for gait analysis is shown to be as reliable as a motion capture system for most scenarios. Gait Analyzer further uses Llama 2 large language model to interpret the gait data to the end user in simple terms. You can test Gait Analyzer by uploading a short side-view video of you walking. In case you want to run it locally in your computer, It's available on docker and its open-source. I'd appreciate your feedback to improve Gait Analyzer. https://github.com/abishekmuthian/gaitanalyzer

Visit product View on Hacker NewsDec 13, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, computer · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: ios, video · Missing: mobile apps, personal, entrepreneurs

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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