Just Keep Going Inc. is developing an AI-powered weightlifting training protocol to help Ghanaian athletes, especially women, safely reach elite competition. This system will reduce unsafe weight-cutting by 50%, help at least three lifters qualify for international events, and create 10+ AI and sports science jobs in Ghana within 24 months. By integrating AI-driven coaching, wearables, and nutrition tracking, we ensure safer, data-driven training for under-resourced athletes.
How Our Project Will Contribute To The Growth Of The Decentralized AI Platform
The Just Keep Going AI Weightlifting Training Protocol advances BGI’s mission by demonstrating AI’s real-world impact in sports, improving athlete health and performance, and fostering AI adoption in underserved regions. It reduces unsafe weight-cutting by 50%, creates 10+ AI and sports science jobs in Ghana, and provides elite AI-driven training to under-resourced athletes. This project expands AI’s role in human optimization, aligning with BGI’s vision for ethical AI innovation.
Our Team
The Just Keep Going AI Weightlifting Training Protocol is led by Kevin Frey, CEO of Just Keep Going Inc., SingularityNET Ambassador, and IWF Delegate for Ghana, with firsthand coaching experience at the IWF World Championships. Coaching consultants include Dave Sawyer, British Olympic Weightlifting Coach with 40+ years of experience, and Mattie Sasser, a two-time Olympian. Marie Agbah-Hughes, Ghana’s national record holder, serves as Test Subject & Athlete Coordinator.
To put the collected data into words for the coaches and athletes
Company Name (if applicable)
Just Keep Going Inc
The core problem we are aiming to solve
The core problem is the lack of AI-driven training resources for under-resourced athletes, leading to unsafe weight-cutting, inefficient training, and missed elite competition opportunities. In Ghana, many weightlifters, especially women, struggle with proper nutrition, injury prevention, and performance tracking, limiting their potential. This project integrates AI-powered biomechanics, wearable sensors, and nutrition planning to provide safe, data-driven training, reducing injuries and unsafe practices while helping athletes reach the Olympic level.
Additionally this project was started to finding a pathway for athletes to monetize their performance data through ethical data ownership.
Our specific solution to this problem
The Just Keep Going AI Weightlifting Training Protocol provides a data-driven, AI-powered training system to optimize performance, prevent injuries, and ensure safe weight management for Ghanaian athletes.
Our solution integrates:
1. Wearable Sensors & AI Biomechanics – Tracking bar speed, force, heart rate, and movement patterns to provide real-time feedback for safer, more effective training.
2. AI-Driven Load & Recovery Management – Ensuring optimal strength progression while preventing overtraining and injury.
3. AI Nutrition & Weight Management – Monitoring metabolic output and hydration needs, generating personalized nutrition plans to reduce unsafe weight-cutting by 50%.
4. AR Coaching & AI-Powered Analytics – Delivering real-time technique adjustments to improve biomechanics and maximize lifts.
This $50,000 grant will launch Phase 1, with an additional $75,000 planned to finalize development. The system will help at least three additional Ghanaian lifters qualify for international competitions, create 10+ AI and sports science jobs in Ghana, and provide elite-level training tools to under-resourced athletes, making Olympic success more accessible.
Project details
Just Keep Going: AI-Driven Weightlifting Training Protocol for Olympic Success (Phase 1)
Project Summary
Just Keep Going Inc. is developing an AI-powered weightlifting training protocol to help Marie Agbah-Hughes, Ghana’s female weightlifting record holder, qualify for the 2028 Olympics. This initiative integrates AI-based performance analytics, wearable technology, and AR coaching tools to:
Optimize weightlifting techniques through real-time biomechanical analysis
Prevent injuries by analyzing movement patterns and training loads
Support safe weight management and nutrition through AI-driven planning
The need for this AI-driven approach became clear during the IWF World Championships in Bahrain, where Kevin Frey, CEO of Just Keep Going Inc., served as assistant coach to British Olympic Weightlifting Coach Dave Sawyer while working with Marie Agbah-Hughes and Junior Periclex (Cameroon). Kevin also coached Amo Boateng separately during the competition.
Ghanaian Support for AI in Sports
Kevin Frey has been actively promoting AI in athletics across Ghana, building strong local support for this initiative. He has met with Ghana’s Sports Minister, Kofi Adams, to discuss AI integration into athletics and has engaged in media outreach:
GTV (twice) – One appearance specifically discussing AI in athletics
Asempa Radio – Speaking extensively about AI in sports
Osagyefo TV & Radio – Discussing AI’s role in athletics
This first phase of development will be funded by $50,000 from BGI Nexus DeepFunding, with a plan to apply for an additional $75,000 in a future round to expand and finalize development.
Prior Investment & Strategic Engagement
Kevin Frey has already personally invested $26,500 into exploring the feasibility of this AI-driven training protocol, which includes:
Engaging with athletes through training and sponsorship
Traveling to Ghana to collaborate with the national weightlifting federation and the incoming president
Meeting with Ghana’s Sports Minister to discuss AI integration
Attending the IWF World Championships in Bahrain to coach and assess AI applications in training
How We Will Build the System
Step 1: Integrating Wearable Sensors into Training
VBT Devices: Measure bar speed, force, and velocity
Wearables/Smart Shirts: Monitor heart rate, breathing rate, and movement patterns
Biometric Sensors: Provide real-time biometric data to assess recovery needs
Step 2: Developing the AI Training Platform
Sensor Data Collection: Capturing movement, heart rate, and muscle engagement data
AI Processing: Analyzing movement inefficiencies and biometric signals
Feedback & Recommendations:
Coaches receive AI-generated reports for technique improvements
Athletes get real-time feedback through a mobile interface
50% reduction in unsafe weight-cutting practices among female athletes
At least three additional Ghanaian lifters qualifying for international competitions
Creation of at least 10 AI and sports science jobs in Ghana within 24 months
Providing elite-level training tools to under-resourced athletes
Athletic Performance Predictions
Why This Project Will Get Marie Agbah-Hughes to the Olympic Qualification Threshold Marie’s current total is 159 kg, and the Olympic qualification standard is 210 kg. AI-driven optimization, biomechanical analysis, and structured programming make this a realistic multi-phase goal.
Projected Increase with AI Optimization: +25-32 kg in 12-18 months
Long-Term Potential: 210 kg qualification threshold within 24-30 months
Budget & Milestones
Phase 1 Request: $50,000 USD (in AGIX)
Milestone
Description
Timeline
Budget
1
Purchase wearable devices
1 month
$11,500
2
Develop AI model for biomechanics & nutrition tracking
3 months
$15,000
3
Sensor & data integration testing
4 months
$10,000
4
Software Integration & Testing
5 months
$5,000
5
Athlete Testing & Data Collection
6 months
$4,500
Team Members & Achievements
Kevin Frey – CEO, Just Keep Going Inc.; SingularityNet Ambassador; International Weightlifting Federation Delegate for Ghana; IWF World Championship Coach
Marie Agbah-Hughes – Ghana Weightlifting National Record Holder; Test Subject & Athlete Coordinator
Dave Sawyer – British Olympic Weightlifting Coach; UK Level 4 Master Coach; 40+ years of coaching experience
TBD AI Lead – Responsible for AI model development and system integration
Just Keep Going Inc. Athletes
Marie Agbah-Hughes
Emmanuel Allotey
Gabriel Owusu
Abraham Adjei
Paul Agrama
Sylvanus Kwame Kugblenu
Conclusion
This $50,000 grant will kickstart development, with a future $75,000 request planned to complete the system. By leveraging AI, this initiative will showcase SingularityNet and AI’s power to enhance human performance ethically and effectively.
Let’s build the future of Olympic training—one AI-driven lift at a time.
Existing resources
Commercial wearables and hardware.
Was there any event, initiative or publication that motivated you to register/submit this proposal?
Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses.
Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively).
Feasibility (secondary)
This represents the user's assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding.
Viability (secondary)
This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?”
Examples:
A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.
Desirability (secondary)
Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference?
Example:
Creating a translation service from, say Spanish to English might be possible, but it's questionable if such a service would be able to get a significant share of the market
Usefulness (secondary)
This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?”
For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?).
For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things?
Examples:
A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.
A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.
About Expert Reviews
Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses.
Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively).
Feasibility (secondary)
This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding.
Viability (secondary)
This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?”
Examples:
A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.
Desirability (secondary)
Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference?
Example:
Creating a translation service from, say Spanish to English might be possible, but it\'s questionable if such a service would be able to get a significant share of the market
Usefulness (secondary)
This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?”
For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?).
For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things?
Examples:
A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.
A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.
AI Engineer | https://www.linkedin.com/in/musah-ibrahim-ali-820600ab/
Bio
Experienced Software Engineer and a strong team player focused on achieving project objectives. Holds a degree in Electrical Engineering. and is passionate about AI.
Experience
Working with RAIL-KNUST, I have worked on modern AI solutions along with other researchers to diagnose rare conditions in Ghana and aid the diagnostic process of medical imaging experts.
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