Hand Joint Angle Detection Tool

@ZohaibRoy

⭐⭐⭐⭐⭐ Create a Hand Analysis Tool Using Deep Learning and Computer Vision ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project needs and see you're looking for a solution to analyze hand photographs. You don't need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in computer vision and deep learning. I will combine classical preprocessing with a deep-learning model to deliver accurate results, all within your budget. ➡️ Why Me? I can easily create your hand analysis tool as I have 5 years of experience in computer vision and deep learning. My expertise includes image processing, model training, and data augmentation. Additionally, I have a strong grip on relevant technologies like PyTorch, TensorFlow, and OpenCV, ensuring a solid approach to your project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ Deep Learning ✅ Computer Vision ✅ Image Processing ✅ PyTorch ✅ TensorFlow ✅ OpenCV ✅ MediaPipe ✅ Data Augmentation ✅ Model Validation ✅ JSON/CSV Data Handling ✅ Dashboard Development ✅ Cross-Platform App Design Waiting for your response! Best Regards, Zohaib

@umg536

Hello, With over a decade of experience in data analysis and software development, my team at Modular Solutions is ideally suited to bring your hand joint angle detection tool to life. We have a rich skill set in the requisite technologies for this project, including C++ Programming, Computer Vision, Deep Learning, Image Processing, Machine Learning (ML), Matlab and Mathematica, and Python. We're very comfortable working with well-known libraries such as MediaPipe or OpenCV so leveraging these libraries isn't an issue. Our proficiency also extends to developing simple and intuitive user interfaces that streamline the information gathering process. For phase two of your project, we'll design an efficient application where you can easily drag and drop multiple photos of the same patient and get instant angle readings along with visual confirmations for landmarks. Our primary goal is to deliver accurate results while ensuring good performance. We will use robust data augmentation techniques while training the deep learning models and deploy optimization strategies to make sure the end-to-end inference time remains under 3 seconds for a 2000×2000 px image on CPU. This guarantee comes from our deep dedication to satisfying our clients' needs through meticulous craftsmanship and iterative development processes. Choose Modular Solutions for not just a trustworthy partner in innovation but also one that will provide you with end-to-end su Thanks!

@Herculesz

Hi I’ve worked on computer-vision pipelines in Python using PyTorch, TensorFlow, OpenCV, and landmark-based pose estimation, and I can help you so on. The main technical challenge here is not just detecting hand joints, but producing angle measurements that stay reliable across inconsistent lighting, backgrounds, hand shapes, and cases where digits or phalanges are actually missing. I’d approach this by combining a robust preprocessing and augmentation pipeline with a landmark-detection model, then adding a post-processing layer that computes flexion and extension angles and explicitly classifies absent finger segments. For Phase 1, I can build a training and validation workflow that outputs clean JSON or CSV with landmarks, calculated joint angles, and Boolean missing-segment flags for each image. For Phase 2, I can package the model into a practical interface with batch image upload, visual landmark overlays, and fast CPU inference so you can review one patient or many photos quickly. I’m also comfortable optimizing for your acceptance criteria, especially MAE on held-out data, low false negatives for missing anatomy, and inference performance on large images without needing a GPU. The result will be a usable prototype with clear setup instructions, clean code, and a foundation that can keep improving as you add more labeled data and feedback. Thanks, Hercules

@devendrathakur12

Hi, i’ve done many ML Object detection projects multiple times, building hand pose pipelines with MediaPipe + PyTorch and custom angle extraction. Are your images labeled with landmarks or fully unlabeled? Do you need CPU-only inference or can we use optional GPU for training? I suggest using MediaPipe for initial landmarks and fine-tuning a PyTorch model because it speeds development. I also suggest heavy augmentation (lighting, rotation, occlusion) because your dataset is non-uniform and this improves accuracy. I will first preprocess data and build landmark detection with angle calculations. Then I will train and validate the model to meet error targets. Finally I will package it with a simple UI and overlay visualization for batch use. Best, Dev S.

@hassannaweed4

Drawing from my solid background in machine learning, complemented by my AI and big data expertise, I am well-positioned to tackle the intricacies of your Hand Joint Angle Detection project. Remarkably, I have a wealth of experience in developing and implementing deep learning models like the one you need for your images analysis. To ensure robust results that meet your accuracy standards, I‘ve successfully deployed data-augmentation and domain-adaptation techniques in similar projects. My master's degree in software engineering equips me with a deep understanding of computer vision and hands-on experience using PyTorch and TensorFlow, which are highly applicable to your project. Additionally, my strength in delivering intuitive user experiences makes me confident I can provide an interface that easily provides the information you're seeking from your dataset. I've developed web dashboards and cross-platform desktop applications that are usable, even in scenarios where dealing with large images on a CPU is required. Lastly, as a seasoned professional who consistently delivers high-impact solutions, I'm excited about the prospect of handling the full development phases of your project: from model training through to packaging it behind an easy-to-navigate platform for you. Engage me for this task and together we'll transform your diverse hand photographs into a highly practical Hand Joint Angle Detection Tool that meets all your specified acceptance criteria.

@kamran2012

Good to see this project, I will build a two-phase hand joint angle detection pipeline — landmark extraction, per-joint flexion/extension angle calculation, and missing-phalanx flagging — delivered as a clean Python package with a lightweight web interface for batch patient analysis. For landmark detection, I will fine-tune a pose-estimation model on your dataset rather than relying solely on MediaPipe defaults, which struggle with missing digits and non-standard anatomy. Domain-specific augmentation — random occlusion, varied skin tones, lighting shifts — will be critical to hitting the ≤5° MAE target across your diverse image set. Questions: 1) Are the images annotated with ground-truth joint landmarks already, or will I need to bootstrap labels using semi-supervised methods? Looking forward to discussing further. Best regards, Kamran

@quan9824

Hi, this is a strong and very practical computer-vision problem, and I can take it from dataset to a usable tool. My approach combines robust landmark detection + geometric post-processing + lightweight classification: Phase 1 (Model + Angles) I will start with a reliable hand landmark model (MediaPipe or a fine-tuned CNN/transformer in PyTorch) and adapt it to your dataset using augmentation (lighting, skin tone, occlusion, background noise). From detected landmarks, I will compute joint angles (flexion/extension) using vector geometry, ensuring consistency and numerical stability. For missing phalanges or digits, I will add a detection layer that flags absent landmarks based on confidence scores, spatial consistency, and optionally a small classifier trained on edge cases. Output will be structured JSON/CSV with: • Landmark coordinates • Joint angles per finger • Boolean flags for missing segments Phase 2 (Usable Tool) I will package this into a simple interface (likely a lightweight web app using FastAPI + minimal frontend) where you can upload images or batches, see results instantly, and view overlays of detected landmarks for verification. Performance & Accuracy • Target ≤5° MAE via calibration + test-set tuning • Optimized CPU inference (<3s for 2K images) • Strong augmentation to handle real-world variability Timeline: ~1–2 weeks including validation and iteration. Best regards.

@kareemmaize

Hi - the real challenge here is not detecting a hand, it is deciding when not to trust a landmark. In a dataset this varied, the model can look accurate on normal fingers but still invent joints when lighting, occlusion, or an absent phalanx breaks the expected anatomy. That looks correct in an overlay, but fails in measurement output, so I would treat landmark confidence and missing-segment logic as part of the core model, not post-processing. The flow is: image enters the pipeline -> hand and finger landmarks are detected -> the system checks whether each expected segment is truly present -> joint angles are calculated from validated landmarks -> JSON or CSV is produced -> the review tool shows the same image with visual overlays for fast verification. The part to get right early is the landmark validation and missing-structure logic, because it affects both usability and measurement accuracy. This comes together cleanly once that boundary is set right.

@sf264

Hello, I see that you need your dataset turned into a practical analysis tool that will do the things as given. I have a rich experience in similar projects. I would love to discuss the requirements in more detail via chat. I am excited to collaborate with you, Fahad.

@alizain721

You’ve collected a messy, real-world set — varied lighting, skin tones, backgrounds and even missing fingers — which is exactly the kind of dataset where robustness matters more than a fancy model. Detecting absent phalanges is often a detection/segmentation problem more than pure regression; if the model knows a region is missing it should avoid spurious angle estimates and flag it instead. I recently delivered a hand-angle estimator for a clinical hand-therapy study using MediaPipe as an initializer and a PyTorch refinement network; the prototype produced ~4.5° MAE on a held-out set and handled occasional missing digits robustly. My plan: normalize and crop hands, run MediaPipe/OpenCV for initial landmarks, refine with a lightweight CNN regressor for joint positions, compute angles from joint vectors, and add a small classifier+segmentation head to flag missing phalanges. Heavy domain augmentation and ONNX quantization for sub-3s CPU inference. I can start Phase 1 for £255 — can you share ~20 representative images (including some with missing phalanges) so I can run a quick baseline and confirm expected error spread?

@zahidhasta

Hi, This is a very interesting computer vision problem, and it aligns well with my experience building practical image-analysis pipelines using deep learning and landmark-based modeling. I can develop Phase 1 as a robust hand-analysis pipeline that detects landmarks, computes flexion and extension angles for each finger joint, and flags missing digits or phalanges in a structured JSON or CSV output. My approach would combine strong preprocessing, targeted augmentation, and a landmark-aware model using PyTorch with MediaPipe or a custom vision pipeline where needed, depending on which gives the best accuracy on your dataset. For Phase 2, I can package the solution into a simple usable interface where you can upload one or multiple images and instantly get angle values plus a visual landmark overlay for verification. I will keep CPU inference efficiency in mind from the start so the system remains practical on high-resolution images. I understand that variation in skin tone, pose, lighting, background, and anatomical differences is central to the challenge here, so I would focus on robustness and measurable performance rather than a generic hand-pose solution. I can deliver clean code, setup instructions, and a prototype that is easy to test and extend. Best regards Zahid Hassan

@DngPhgNam

⭐⭐⭐⭐⭐ ✅Hi there, hope you are doing well! I have previously developed a hand gesture recognition system that accurately detected finger positions and angles using a combination of deep learning and computer vision techniques, streamlining data extraction from varied image datasets effortlessly. From my experience, the most crucial part of this project is robust preprocessing and data augmentation to ensure the model’s accuracy across highly diverse images. Approach: ⭕ I will start by building a pipeline that combines classical computer-vision preprocessing with deep learning using PyTorch or TensorFlow. ⭕ I will leverage pose-estimation libraries like MediaPipe or OpenCV for landmark detection. ⭕ I will implement domain-adaptation and data augmentation techniques to enhance model robustness. ⭕ I will develop a model that outputs JSON/CSV with joint landmarks, angles, and missing-phalanges flags. ⭕ I will create a user-friendly web-based interface for easy photo uploads and real-time results with visual overlays. ❓ Could you please clarify the preferred format and platform for the interface (web or desktop)? I am confident I can deliver a practical, accurate, and user-friendly hand joint angle detection tool tailored to your requirements efficiently. Looking forward to collaborating on this exciting project! Best regards, Nam

@KhudaBuxMagsi

Hello, I will start by building a robust hand-analysis pipeline using a hybrid approach: a pre-trained hand landmark detector (such as MediaPipe Hands or an equivalent keypoint model) combined with a custom post-processing layer to compute precise joint angles and detect missing finger segments reliably. I will first standardize and preprocess your dataset (normalization, augmentation for lighting/pose variability), then validate landmark extraction quality across diverse hand conditions. Once stable landmarks are available, I will compute flexion/extension angles using geometric joint relationships and implement logic to flag missing phalanges or digits based on landmark continuity and structural inconsistency. The output will be structured in clean JSON/CSV format per image. For Phase 2, I will wrap the model into a simple interface—either a lightweight web dashboard or desktop app—where you can upload single or batch images and instantly view results with visual overlays of detected landmarks and angles. I will optimize inference for CPU to meet the 3-second constraint for high-resolution images. I will prioritize accuracy through careful calibration, validation on your held-out set, and iterative tuning to meet the ≤5° MAE target while minimizing false negatives for missing digits.

@Hubready

Hello! This is James from Hollywood. I’ve carefully read your project description regarding the Hand Joint Angle Detection Tool and I’m excited about the opportunity to assist you. With over 15 years of experience in AI, computer vision, and deep learning, I believe I can deliver a robust solution tailored to your needs. To ensure I fully understand your requirements, could you please clarify the following questions? 1. Are there specific algorithms or frameworks you prefer for the angle detection? 2. What is the expected output format for the detected angles? My approach would be to first analyze the dataset of photographs you have collected, followed by developing a machine learning model using OpenCV and possibly leveraging deep learning techniques for improved accuracy. I can also provide ongoing support for data augmentation to enhance model performance. I have previously worked on projects that involved image processing and machine learning, such as a hand gesture recognition app and an image classification tool for a small e-commerce platform. Additionally, I've been an active participant in the Shopify partners program since 2016, developing themes and apps that have driven user engagement. I’m looking forward to the possibility of collaborating on this project and am ready to dive into the details. Let’s chat!

@jeffleads

Hello. You need to build a hand pose analysis tool that calculates flexion/extension angles at every finger joint from hundreds of non-uniform photographs, while automatically flagging missing phalanges. I've built similar anatomical landmark detection systems before. I'm Jeff Chong, an AI Computer Vision expert. Based on search results, MediaPipe Hands provides 21 3D landmarks per hand at 40 FPS on CPU, ideal for your varied lighting and background conditions . Joint angles can be calculated using vector dot products from landmark triplets (e.g., DIP-PIP-MCP for each finger) . Here's my technical roadmap: Phase 1: Train a PyTorch model with heavy augmentation (rotation, lighting, skin tone) using MediaPipe as backbone Phase 2: Package in FastAPI web dashboard with OpenCV visualization overlay Quick question: Do your photos contain single hands or multiple hands per image? This affects detection logic. Let's chat. Best regards, Jeff Chong

@nemanjam61

Hello, I have experience with PyTorch, MediaPipe, and OpenCV for pose estimation and medical imaging analysis, including building landmark detection systems that calculate joint angles from skeletal keypoints and anomaly detection pipelines for identifying missing anatomical structures. For your hand analysis tool, I can implement a pipeline that extracts the flexion/extension angle at each interphalangeal and metacarpophalangeal joint using MediaPipe hand landmarks with custom geometric calculations, combined with a CNN classifier to flag absent phalanges. Let's discuss!

@Vsion2

Welcome to professional Python development services! Hi there, I'm Alema, a Python expert programmer who strives for clear code in atmospheric, numerical weather prediction, physics, and all other seminal fields. I'm ready to provide you with high-quality services. I have completed 350+ projects with a 100% Positive Rating. If you are looking for Quality work, look no further. Also, we are a team of professional workers, and we are always available 24/7 to help employers without limitations, and delivery is guaranteed on time. Your faithfully. Eng. Alema Akter

@johnedwardl

Hi, I will develop an analysis tool that processes your hand photographs and calculates joint angles while identifying any missing phalanges. My approach will leverage a combination of classical computer vision and deep learning, utilizing PyTorch or TensorFlow alongside pose-estimation libraries like MediaPipe or OpenCV for accurate landmark detection. With extensive experience in similar projects, I understand the importance of robust data augmentation and domain adaptation to handle the variability in your dataset. I will ensure that Phase 1 delivers a model that meets your acceptance criteria, achieving a mean absolute error of joint-angle measurement within the specified limits. Phase 2 will focus on creating a user-friendly interface, ideally a lightweight web dashboard, enabling quick analysis of multiple images simultaneously. To optimize this process, could you share the preferred format for the input images and any specific metrics for user interaction on the dashboard? I’m ready to start immediately and look forward to your response. Thank you.

@MIndlabsAi6

Hello, I am Vishal Maharaj, with 20 years of experience in Python, C++ Programming, Computer Vision, Data Augmentation, OpenCV, and Image Analysis. I have carefully reviewed your project requirements for the Hand Joint Angle Detection Tool. To accomplish this project, I propose using a combination of classical computer-vision preprocessing techniques and a deep-learning model, possibly leveraging PyTorch or TensorFlow for training. I will implement robust data-augmentation and domain-adaptation strategies to ensure accuracy. In Phase 1, I will train and validate a model to calculate joint angles and detect missing phalanges in hand images. In Phase 2, I will create an intuitive interface for easy input and output of angle values and visual overlays. I am ready to discuss the project further. Please initiate the chat. Cheers, Vishal Maharaj