Slingshot
Wearable tech for optimised running.
A 2019 research and product concept exploring a low-cost, foot-worn wearable for runners, designed to capture gait data more accurately than wrist-based trackers and turn it into actionable feedback for performance, fatigue, and technique improvement.
2
Runners evaluated on the pre-final prototype
₹1800
Prototype benchmark cost target
v7
Ongoing evolution referenced in Playground
My Role
Led user research and analysis, problem framing, ideation, concept and UX design, prototyping, and user testing.
The Team
1 designer, 1 professor
Timeline
Jan 2019 — Jun 2019
Focus
Foot-based gait analysis for runners, fatigue awareness, technique guidance, and low-cost wearable feasibility.
Problem
The problem: runners still lack actionable gait data.
Existing consumer fitness wearables made activity tracking mainstream, but they still failed to provide the kind of precise foot-based analysis runners need for technique improvement, fatigue awareness, and injury prevention.
Running carries a high risk of injury, often tied to poor technique or accumulated fatigue. Traditional gait analysis can reveal those issues, but it usually depends on lab-based setups that are expensive and inaccessible to everyday runners.
Wrist-worn trackers generate inaccurate gait data because they do not directly measure foot movement.
Most wearables optimize for distance, pace, and step count rather than detailed running technique metrics such as foot strike, pronation, or contact time.
Existing devices rarely provide fatigue alerts based on gait change, even though that is critical for avoiding overuse injuries.
Smart shoes exist, but they are often expensive, fragile, and harder to adopt than a standalone module.
Phone-based solutions add weight, dependence on carrying the phone, and possible online requirements.
The opportunity was a low-cost, convenient, foot-mounted device that could measure key gait parameters accurately and give runners useful feedback on performance and fatigue.
Research
Research and discovery.
The early phase combined literature review, market analysis, and a first working prototype to validate whether low-cost IMU-based gait tracking could actually become meaningful for runners.
1. Literature review
I explored running biomechanics, the role of gait analysis in performance and injury prevention, and the capture methods already used in research. IMUs emerged as the most promising low-cost, mobile option, especially when mounted closer to the foot or shank.
2. Secondary research
Existing fitness bands, smart shoes, and running apps were benchmarked to understand feature coverage and market gaps. The analysis confirmed recurring weaknesses in measurement accuracy, detailed gait visibility, fatigue feedback, and usability.
3. Primary research and feedback
An early low-cost prototype using IMUs and piezoelectric sensors on the foot and shank was built to capture gait data during walking. Doctors and users responded positively to the idea of a low-cost gait-analysis tool, while feedback pushed the concept toward runner-specific technique analysis and fatigue alerts.
Opportunity
Defining the opportunity and product goals.
The research confirmed that serious runners need more than general activity tracking. They need accurate, foot-based analysis that helps them understand movement quality, training load, and fatigue before injuries happen.
The project direction became clear: build a lower-cost alternative to lab analysis and higher-end wearables while focusing on parameters that are actually meaningful for running performance.
Develop a low-cost, foot-mounted wearable using IMUs to capture key running gait parameters more accurately.
Analyze gait data to surface feedback on running performance and fatigue levels.
Support running rhythm and Fartlek-oriented training patterns.
Incorporate real-time feedback such as vibration to help guide technique.
Visualize data clearly enough that runners can interpret and act on it.
Target joggers and athletes training for performance improvement or marathons.
The proposed feature set included contact time, step rate, stride length, foot strike type, heel lift, pronation metrics, stance excursion, and fatigue alerts.
Ideation
Ideation and concept development.
Multiple form factors and sensing directions were explored before the project converged on a lace-mounted module that balanced stability, component fit, and future feature potential.
Mind mapping
Early ideation explored foot actions, gestures, placement possibilities, and different ways the product could support running comfort and training.
1. E-textile smart socks
One direction proposed washable socks with integrated pressure sensors, flexible electronics, NFC, and local storage.
2. Kinematic modules
Another direction explored multiple BLE-connected modules that could be placed across the body, stream data live, charge over USB, and fit within a polycarbonate shell.
3. Lace slip-in sensor module
The chosen direction became a lace-attached module with BLE, GPS, gesture control, SD card storage, haptic feedback, and assistance features for technique and rhythm.
It was selected because it offered secure attachment, room for the required components, dual-module analysis potential, offline and live data streaming, and strong relevance for dedicated runners.
Narrative
Slingshot as a running-tech narrative.
Beyond the hardware, the concept needed a training logic and a story. The product was framed around Fartlek running, technique guidance, and a system that made performance feedback feel motivating rather than clinical.
Need and Fartlek integration
Slingshot was positioned around Fartlek training, where runners alternate periods of faster and slower effort inside continuous movement. That method is effective, but it also makes detailed feedback and fatigue awareness difficult to manage without better instrumentation.
The concept aimed to surface contact time, foot strike events, and pre-fatigue indicators, then support runners with vibration haptics that could guide foot placement and rhythm as a lightly gamified performance aid.
Product story
Branding
The Slingshot logo was designed to express running performance through forward momentum, agility, and energy. The arrow-like movement, sharp angles, and fluid curves all support a brand identity built around speed and improvement.
Refinement
Design refinement and prototyping.
Once the direction was chosen, the work shifted toward physical feasibility, placement stability, form exploration, and the companion interfaces that would make the captured data useful.
Placement and attachment
Different placements and locking mechanisms were explored, including through-lace, under-lace, ankle, and over-eyelet approaches. The lace slip-in direction won because it offered the best stability for the product intent.
Form exploration
Multiple physical forms were sketched to test how the module could sit on a shoe while balancing attachment logic, footprint, and overall visual character.
Component integration
The concept was refined around a compact assembly including an LDPA cover, GPS, BLE, Micro SD, microcontroller, switches, PCB, and battery.
App wireframes
High-fidelity wireframes were created for the companion mobile experience. They also guided the associated website focused on running metrics and data visualization.
Prototype
Prototype development and feasibility.
The working prototype made the concept tangible, but it also exposed the tension between compactness, cost, component fit, and feedback effectiveness.
Prototype development
The first working build used Styrene casing, BLE 2.0, GPS, an SD card, and a LiPo battery. It proved the sensing direction was viable, even though the available parts pushed the form factor larger than the long-term goal.
Prototyping challenges
A major challenge was fitting GPS, Bluetooth, IMU, battery, and storage into a compact shoe-mountable unit while staying cost-effective. To pressure-test that ambition, the pre-final prototype was benchmarked against Mi Band v1 and Samsung Gear Sport.
At roughly ₹1800, the prototype managed to include GPS and significant storage, validating that affordable performance tracking was achievable even if the early version remained bulkier than the intended final form.
Mi Band 1
Samsung Gear Sport watch 2018
A distant view of Slingshot's initial prototype worn on foot
Slingshot's initial prototype worn on foot
Evaluation
Evaluation and user feedback.
The pre-final prototype was tested with two users running on campus for around twenty minutes, with the captured gait metrics displayed on the prototype website through visualizations and basic running metrics.
Users found the foot strike and fatigue data novel and valuable, and the device weight felt comfortable when worn on the ankle.
The strap mechanism was uncomfortable and should be redesigned for easier wear.
Ankle-based vibration worked during walking but failed during running, where body movement masked the feedback and made it ineffective for technique guidance.
A regular athlete could understand the app, but the more general athlete found the graphs unclear, especially the axis representation.
Users requested a clearer live-feedback indicator and key results surfaced directly on the device itself.
The biggest takeaway was that the core concept had clear value, but the feedback channel, strap design, and data communication all needed refinement before the experience could work for a broader set of runners.
Conclusion
Conclusion and future scope.
Slingshot demonstrated that foot-worn wearables could unlock useful running insights still missing from wrist-based trackers, but it also made clear where the product needed to mature.
The concept showed real potential to help runners understand movement quality, manage fatigue, and improve performance with more kinematic awareness than mainstream devices were offering.
Refine the physical experience, especially strap comfort and the possibility of on-device display feedback.
Improve visualization clarity so the output is understandable even for less technical users.
Explore alternative feedback methods such as audio cues or different vibration placements and intensities.
Expand and validate the Fartlek guidance and fatigue-mapping logic more rigorously.
With those refinements, Slingshot could become a much stronger tool for runners trying to train more intelligently and safely.
Still evolving
Slingshot continues to evolve.
Development later progressed to v7 with a smaller form factor, improved strap direction, and an evolving demo website. The current continuation lives in Playground, where the project is being revisited through a more contemporary product and ML/AI learning lens.
Recognition
Featured in D'source.
This project was featured in D'source, the open design initiative by IDC at IIT Bombay in collaboration with India's Ministry of Human Resource Development as part of the National Mission in Education through ICT. The feature positioned Slingshot within a broader design education and open-learning context.
Personal context
Designed as a runner, revisited as one.
Born from my interest in both design and running, Slingshot began as an attempt to build a wearable that could unlock a runner's potential through gait analysis. After later completing multiple 10k races and half marathons, the project took on new meaning as a bridge between athlete experience, product design, and wearable technology.
References
Selected references.
• Francisco Kiss, Konrad Kucharski, Sven Mayer, Lars Lischke, Pascal Knierim, Andrzej Romanowski, Paweł W. Wo ́zniak. RunMerge: Towards Enhanced Proprioception for Advanced Amateur Runners. 2017. DIS 2017, Edinburgh, UK. http://dx.doi.org/10.1145/3064857.3079144
• Christina Strohrmann, Holger Harms, Gerhard Troster, Stefanie Hensler, Roland Muller. Out of the Lab and Into the Woods: Kinematic Analysis in Running Using Wearable Sensors. 2011. UbiComp'11, Beijing, China.
• Tim Op De Beéck, Wannes Meert, Kurt Schütte, Benedicte Vanwanseele, and Jesse Davis. 2018. Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion. KDD '18, London, United Kingdom. https://doi.org/10.1145/3219819.3219864
• Maheshya Weerasinghe, G.K.A Dias, Anuja Dharmaratne, Damitha Sandaruwan, Aruni Nisansala, Chamath Keppitiyagama, Nihal Kodikara. Computer Aid Assessment of Muscular Imbalance for Preventing Overuse Injuries in Athletes. 2016. ICCIP '16, Singapore.
• Jari Parkkari, Urho M. Kujala and Pekka Kannus. Is it Possible to Prevent Sports Injuries?. 2001. Sports Med 2001; 31 (14): 985-995.
And 30+ additional research papers and articles referenced during the study.
