How to create a smart fitness tool with computer vision and succeed?
The fitness industry has seen dramatic changes due to the COVID-19 pandemic. Government policies forced many gyms and exercise facilities to close. As a result, many chose to exercise in their own homes, and sales of home workout gear and equipment rose exponentially.
This radical change in the fitness industry has created room for innovation and new products powered by computer vision AI models. By understanding the movements of the human body AI can transform home workouts, making them more impactful and reducing the chance of injury.
The success of fitness AI products in development depends on high quality training data. This blog will show how effective video or image annotation tool, like Keylabs, can transform image and video labeling and can help smart fitness tool developers.
Firstly, we will look at some of the smart fitness tools that are changing how we exercise. Secondly, we will look at the essential annotation techniques for AI training data in this industry. And finally, we will identify the important ways in which the right annotation tool can streamline AI development.
Exciting smart fitness applications
The pandemic has led more people to look for at home fitness solutions that work around their schedules. As a result, there has been a rapid growth in sales for products like Mirror, an all in one home gym and Peleton, the popular interactive cuddling system. Personal fitness is becoming more accessible, with better instruction and programs available at home. The addition of AI to home exercise products can ensure improved performance for users and safeguard health:
- Smart instructions: One significant drawback of working out at home is the lack of training instruction. Poor form can reduce the effectiveness of a workout and can also lead to injury. AI developers can use computer vision AI models to protect home exercisers.
Smart fitness tools are capable of understanding the body position of users. These systems can then instruct users on how best to improve their form and avoid workout injuries. - Creating bespoke training regimes: Computer vision AI powered home fitness platforms are able to learn the specific body type and movement capabilities of users. This data can then be processed by AI models to produce bespoke training programs that fit the exercise goals and realistic abilities of every smart tool user.
Annotation techniques for smart fitness tools
Image and video annotation are essential for AI home fitness applications. Accurate labeling of training data allows AI models to analyse body movement in real time.
Video annotation is essential for Smart fitness tools. Annotated video data allows AI models to understand body movements and support at home exercise with knowledgeable advice. However, video annotation can be time consuming for AI companies. Annotation tools optimized for video labeling can help to produce high quality video labels faster.
Developers use skeletal annotation to locate the position of the human body in digital images and video frames. To do this technique, annotators draw lines onto limbs and other body parts to create a simplified model of the human body that AI models can learn from.
Annotation tool advantages
Keylabs is a tool designed by labeling experts to maximize annotation speed without compromising on quality. Keylabs uses interpolation to track objects through frames, ensuring that video annotation projects are finished on time. Multiple annotators can also work on video data at the same time.
Keylabs can then seamlessly integrate their work, again ensuring a streamlined video annotation process. Finally, unique workforce analytics capabilities mean the Keylabs empowers managers to find the right workers for their annotation projects.