Cycling science explores the bicycle as both a mechanically elegant system and a dynamic platform for applied learning in systems information science. Its widespread familiarity and structural simplicity make it an ideal foundation for investigating how physical and digital systems interact. This interdisciplinary field spans engineering, data systems, cyber-physical integration, and human-centered computing.

 

While the bicycle may appear simple, its operation is governed by fundamental principles such as gear ratios, mechanical efficiency, and aerodynamic forces. Modern cycling technologies integrate advanced digital systems—including GPS-enabled computers, electronic gear shifting, and wireless sensors that monitor physiological and mechanical performance—creating a real-time data interface between human and machine.

 

For systems information science students, cycling offers a hands-on context for exploring Internet of Things (IoT) architectures, embedded systems, sensor networks, mobile computing, and digital infrastructure. It also supports research into human-machine interaction, smart mobility solutions, and the design of data-driven applications. Below are some example areas we are actively exploring through research initiatives.

Cycling science represents a case study in the evolution from mechanical systems to intelligent, connected technologies—making it highly relevant to students of systems information science. The transition from traditional mechanical gearing to integrated wireless communication systems exemplifies how data, automation, and human-centered design converge to drive innovation in real-world environments.

 

While traditional gear systems are reliable, they are limited by mechanical friction, manual calibration, and environmental vulnerability. In contrast, modern electronic shifting systems leverage advancements in embedded electronics, wireless communication, and sensor networks to enable faster, more accurate, and low-maintenance gear changes.

 

These systems are integrated with GPS units, power meters, and physiological sensors, transforming the bicycle into a real-time data platform. This creates a continuous feedback loop between the rider and the machine—ideal for exploring key systems information science themes such as IoT architectures, cyber-physical systems, user interface design, mobile data analysis, and intelligent system optimization. Cycling becomes a dynamic ecosystem for studying the interaction of humans, data, and digital infrastructure.

Cycling is an example of human-machine movement dynamics, where the rider and the bicycle function as an integrated system. A key component of this interaction is saddle height, which affects joint mechanics—particularly at the knee, hip, and ankle—throughout the pedal stroke. When improperly adjusted, saddle height can cause asymmetrical motion, inefficient force application, and increased strain on muscles and joints. These biomechanical inefficiencies often result in a higher risk of overuse injuries.

 

To address these issues, modern bike fitting relies on data-intensive techniques such as motion capture, wearable sensors, and real-time force analysis. These tools allow for precise measurement of limb angles, pedal stroke efficiency, and joint loading patterns.

The resulting data are used to calibrate a rider’s position, promoting smooth, symmetrical movement and optimal power transfer. 

 

For systems information science students, this presents an ideal case study in cyber-physical systems, sensor integration, and human-centered data analysis. The cyclist-bike relationship illustrates how real-time biomechanical data can be collected, modeled, and used to optimize human interaction with machines. 

In cycling science, crank rotation and pedal lag are critical factors in understanding human-machine movement dynamics. Crank rotation should generate smooth and continuous torque throughout the entire pedal stroke. However, inefficiencies arise at the transition points—top dead center (0°) and bottom dead center (180°)—where neither leg contributes effectively, creating biomechanical "dead zones" in power output. 

 

Pedal lag can also occur due to mechanical delays from the freehub mechanism. When a cyclist resumes pedaling after coasting, a slow-engaging hub can result in a measurable delay before force transmission reaches the rear wheel. This mechanical lag negatively impacts responsiveness and power transfer. Factors such as crank arm length further influence performance—shorter cranks allow quicker cadence and may reduce dead zones, while longer cranks can increase torque but exacerbate lag.

 

Students in systems information science can quantify torque output across the crank cycle, identify dead zones, and evaluate drive train lag. These data can then be modeled to enhance human-machine interaction, optimize pedaling efficiency, and inform the design of smarter cycling systems.

Hooked and hookless wheel rim designs represent a compelling case study in data-driven decision-making, cyber-physical systems design, and safety-critical system analysis. These competing rim technologies offer different trade-offs in terms of aerodynamics, mechanical performance, safety, and system compatibility.

 

Hooked rims feature a traditional bead hook that securely retains the tire under higher pressures. This design accommodates a wide range of tire types and riding conditions, making it a robust and proven solution. However, the additional material adds weight and limits aerodynamics. In contrast, hookless rims enable cleaner, more aerodynamic profiles, advantageous in tubeless setups. They reduce manufacturing complexity and weight but require strict tire compatibility and lower maximum inflation pressures—introducing safety risks such as blowouts if misused.

 

Systems information science students can analyze force distributions, tire fit tolerances, and pressure thresholds. Students can explore how technical specifications, environmental factors, and digital standards intersect to optimize performance and reduce failure risks in human-machine systems.