DEFINE A PROBLEM to solve with AI

AI is not a solution to every problem.

IDENTIFY IF A PROBLEM STATEMENT IS BEST SUITED FOR MACHINE LEARNING OR DEEP LEARNING

Step 1: Creating a HYPOTHESIS

Create a specific and actionable hypothesis for a problem you believe can be solved using AI/ML.

CRAFTING A STRONG HYPOTHESIS

· Define a problem

· Establish a goal

· Guide decision-making

· Measure success

One of the examples of an idea which AI can solve

Problem statement:

In today’s busy lifestyle, time is precious. And in the age of AI, we need to save time. Also, during the Pandemic, we learned most of the things online. Gyms and outdoor activities were closed, so most people had health issues. We need an online solution where we can exercise from home at our own time and cost. This will save time, money, and a lot of hassle.

Solution:

Virtual fitness Trainer website or app that will assist people in their day-to-day exercises. It will also teach various sports, such as boxing, dance or yoga, and help patients recover at home from their injuries after surgery. This will give people flexibility in time management and monitoring their health progress, which may save a lot of money.

Hypothesis:

The Virtual Fitness Trainer platform can enable users to stay fit and active from home while reducing the overall cost of in-person training and improving user fitness outcomes within a reduced time frame. By leveraging AI/ML technologies, the app can offer a highly personalised and engaging experience, catering to the diverse needs of its users and enhancing retention, motivation, and improved fitness outcomes.

1. Target Group Audience

• What age group of people do you like to target for your web app? ex:18-50 aged adults.
• People who are more interested in fitness activities from home as lack of time, instruments or access to nearby gyms/studios.
• You could be a Fitness enthusiast, a beginner, a busy professional or a post-injury.

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2. Problem Identification

• You may find a similar behaviour in humans, who always struggle with staying motivated, consistent, and accountable for their workout plans at home or the gym.
• Some workout plans may not be suitable for all individuals of different shapes and sizes.
• Limited access to real-time feedback collection, even progress tracking, especially for activities like yoga or strength training, is challenging to maintain.
• This web app will allow the user to choose several exercises and activities suitable to their body type and liking.

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3. Proposed AI/ML Solutions

a. Personalized Workout Plans

AI Solution:

For input data, we could use the following factors

: age, gender, and fitness goals.

Weight loss, muscle gain, available time, prior workout history and health limitations.

For recommendation engines using collaborative filtering and content-based filtering with ML models, such as supervised learning algorithms, to recommend personalised workout routines.

Natural Language Processing (NLP) model for interactive coaching, combined with reward-based learning algorithms to gamify the user experience.

b. Real-Time Form Detection & Feedback

• AI Solution: Implement computer vision using users’ webcams to track their body movements during exercises (e.g., squats, yoga poses, or dance routines). This system provides real-time feedback on posture, form, and corrections to prevent injury and ensure effectiveness.

1. The input data we could consider is a video feed from the webcam and body pose data.
2. The best possible ML model we will need is a Pose estimation model (like Open Pose and Move Net) to track and analyse movement accuracy.

• Cost and time have the maximum impact. It eliminates the cost of the in-person trainer, resulting in savings of up to 40%. It reduces the time to achieve fitness and improves workout effectiveness within 2–3 months. The Virtual Fitness Trainer can offer flexible and affordable subscription plan pricing while still providing a personalised experience in fitness.

c. Adaptive Activity Scheduling

• With the help of predictive analytics, we could adjust workout schedules to individuals’ availability, energy levels, and preferences. The MI model will try to ensure that users can fit workouts into their busy lives, maintain consistency, and minimise drop-off due to time constraints.

1. The input data the user gives, such as User activity history, calendar integration, energy levels, and preferences.

2. We could use the ML, a reinforcement learning model that adapts workout recommendations based on user feedback and performance.

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4. Metrics for Success

• In every product, the metrics show an analysis of how effective it is in terms of measuring the following thresholds.
• Cost — monthly or annual subscription users can see their money averagely saved by a target of 50–60% reduced.
• Time — How fast the user achieved his fitness goals, approximately 20–30% faster due to digital dashboard analysis.
• User Retention: Tracking monthly and notifying the active user regularly helps target a 20% retention improvement within six months.
• Customer Satisfaction: Conduct regular customer feedback and satisfaction surveys to gauge the success of personalised plans and virtual coaching (target 4.5/5 satisfaction rating within three months).