Machine Learning for medical rehabilitation
Walking is one of the most common human activity. But the human gait varies by gender, age, culture etc. How can we use pre-trained models to identify human gait across different images.
In this session, we take a look at a real world case study where we are using deep learning models and Vision algorithms like DeeperCut and ArtTrack to objectively measure the human pose and gait and use this as a measure to predict their rehabilitation helping them to get back onto their feet in weeks instead of months.
We will look at how we went about building and training the model to understand the human gait. We will also look at the challenges that we faced when we wanted to use a generic model to understand Indian patients. We will also touch about the importance of trust and accuracy when working with machine learning algorithms in the healthcare space.
Outline/Structure of the Case Study
- Using ArtTrack and DeeperCut algorithms to detect human body structures.
- Re-training the model for medical rehabilitation patients.
- Challenges in the ML model for Indian patients
- Running the machine learning algorithm on a video
- Predicting gait trends based on machine learning
In this session the participant will understand how we train a machine learning model to infer specific information like the human pose and identifying different parts of the body. They will also learn about the challenges of personalizing ML algorithms by re-training it with local datasets.
Anyone who wants to understand the practical applications of machine learning in real life scenarios
Understanding basics of machine learning like training models and ML algorithms.
schedule Submitted 6 months ago
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Cognitive services enable us to recognize patterns and identify characteristics from images, audio or a video. While most of the examples show how we can work with photos and identify pets, scenes or friends, cognitive services also has deep application in key vital areas.
One of the areas that AI and cognitive services can make a big impact is in the area of medical rehabilitation. Understanding the human reaction to a rehabilitation process is vital to assess how their body is reacting to the treatment being provided to them. In this session, we will look at the complete life-cycle of a medical rehabilitation platform and how it uses IoT data, VisionAPI and sentiment analysis to provide a comprehensive feedback on patient rehabilitation to a doctor. It also touches up the sensitive topic of data ownership and how - in this age of data protection - we can use and work with data responsibly.