Pi-Ready Fall Detection Model

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@amirrmusavifr

Hi, I’m an AI expert with professional experience in computer vision, with a proven track record of working on complex image processing and AI/ML model development. With skill sets: • Algorithm Development: Strong understanding of computer vision algorithms and techniques, including convolutional neural networks (CNNs), object detection, image segmentation and feature extraction. • Model Training & fine-tuning: Develop and train machine learning models tailored for image analysis and visual data interpretation. I have worked on some well-known models like YOLO, RCNN, U-Net, Deeplab, ViT etc. • AI Integration: Implement and integrate AI models into existing software and hardware systems, ensuring high performance and scalability. • Data Analysis: Analyze and process large datasets of images and video feeds to identify patterns, trends, and insights. • Data Handling: Experience in handling and processing large datasets, including image and video data. Familiarity with data augmentation techniques and synthetic data generation. • Performance Optimization: Optimize algorithms and models for real-time processing and ensure they can handle large-scale data efficiently. • Programming Skills: Proficient in programming languages such as Python. Experience with deep learning frameworks like TensorFlow, PyTorch, or Keras. • Tools & Libraries: Proficiency with OpenCV, scikit-image, and other relevant libraries. Experience with version control systems like Git.

@malaikamb2001

As a data scientist with extensive experience in deep learning and machine learning, I am well-equipped to tackle your project on developing a robust fall detection model. My proficiency in Python - along with libraries such as PyTorch, TensorFlow, and OpenCV - makes me comfortable exploring different approaches to achieve the desired accuracy. Be it CNN, SVM, RNN or a hybrid model, I believe in leveraging the right tools for efficient edge deployment. My collaborative style aligns perfectly with your need for an "efficient annotation pipeline" for labeling the raw footage, ensuring a high-quality training dataset for your model. Furthermore, I understand the significance of running smoothly and prevent overheating on Raspberry Pi, Therefore I have extensive experience optimizing models for low-resource devices like Edge of Cloud Nodes.

@Ravan011patel

Hello! I can deliver a complete fall-detection system from raw RGB Wi-Fi camera footage to an optimized Raspberry Pi build with 99%+ accuracy. ✅ Portfolio Freelancer: https://www.freelancer.com/u/Ravan011patel?sb=t ✅ Why Choose Me • Strong experience in Computer Vision, Deep Learning & Edge AI • Deployed real-time detection on Raspberry Pi, Jetson, mobile edge devices • Skilled with PyTorch, TensorFlow, ONNX, TFLite, TensorRT, OpenCV ✅ What You’ll Get • Semi-automated annotation pipeline for fast labeling • Clean, organized train/val/test dataset • High-accuracy model (CNN / CNN-LSTM / Hybrid) tuned to >99% • Optimized Raspberry Pi build (ONNX/TFLite) with smooth real-time FPS • Python inference script: – Reads the Wi-Fi stream – Detects falls instantly – Sends alerts via REST or MQTT • Full source code, setup guide, and Pi demo video ✅ Timeline Week 1: Annotation tool + dataset Week 2–3: Model training Week 4: Pi optimization + real-time demo

@zbynekmasler

**Hi,** My background is in building reliable Python systems with tight performance and hardware constraints. For example, I developed a Raspberry-Pi–based controller for an industrial VTL machine (BUM s.r.o.), where stability, timing, and low-level correctness were critical. This kind of work translates well to your requirement: taking a model and making it run efficiently and reliably on the Pi with real-time behavior. ### What I can handle confidently - Building a clean, efficient **annotation pipeline** for your RGB footage - Preparing a well-structured dataset (train/val/test) - Training a **simple but practical fall-detection model** based on your data - Optimizing the **Raspberry Pi inference** (Python + ONNX where appropriate) - Delivering a small service that streams the camera feed and raises **REST/MQTT alerts** - A short demo video + setup instructions ### About the accuracy target I will aim for the **>99% accuracy** you specified, but I want to be transparent: I’m not a deep-learning specialist, and real-world fall detection depends heavily on the **quality and diversity** of the labeled footage. What I *can* guarantee is: - a solid pipeline, - clean evaluation, - and the best accuracy achievable with the data provided. ### Quick questions - Raspberry Pi model + cooling? - Camera resolution / FPS? - Approx. duration of raw footage? Available immediately at **₹1,100 INR/hour**. — **Zbynek**

@shashank5683

I can deliver a full fall-detection system with >99% accuracy, from dataset annotation to Raspberry Pi deployment. I’ll build a clean labeling pipeline, train a lightweight yet high-performance model (TensorFlow/PyTorch), optimize it for the Pi (ONNX/TensorRT), and integrate real-time streaming + REST/MQTT fall alerts. I have experience with computer vision, edge AI, and IoT pipelines, so I can ensure smooth, fast performance without overheating or throttling. I’ll provide all source code, documentation, and a demo video running on the Pi. Ready to begin immediately.