PhD Defence Mario Boot | Evaluating Experiences with Smart Cycling Technologies | Sensor-based Evaluations of Outdoor Cycling Experiences with Smart Cycling Technologies

Evaluating Experiences with Smart Cycling Technologies | Sensor-based Evaluations of Outdoor Cycling Experiences with Smart Cycling Technologies

Mario Boot is a PhD student in the department Transport Engineering and Management. (Co)Promotors are prof.dr.ing. K.T Geurs; prof.dr. P.J.M. Havinga† and dr.ir. M.B. Ulak from the Faculty of Engineering Technology (ET), University of Twente.

E-bikes are overwhelmingly popular worldwide. Compared to regular bicycles, e-bike riders often ride faster, further, and at older ages. This growth brings clear benefits for mobility and wellbeing, but also new safety challenges. To further enlarge the benefits of cycling and to mitigate increased risk exposure, many interventions are being implemented including digital support systems for cyclists. Such systems are called Smart Cycling Technologies (SCTs) in this thesis, and examples include in-ride warning systems and intelligent motor control systems.

Next to behavioral effects like reaching safer speeds, SCTs shape subjective cycling experiences. These experiences matter: they relate to cycling uptake, technology adoption, and wellbeing. Yet it remains unclear how SCTs impact subjective experiences in dynamic and real-world environments. Also, given the potential benefits of using mobile sensors, it remains unclear how contemporary sensor-based methods can be used to evaluate the causal impacts of SCTs on subjective cycling experiences.

This thesis therefore aimed:

• To evaluate how Smart Cycling Technologies impact subjective cycling experiences, and
• To propose, develop, and test an evaluation framework that guides the use of mobile and wearable sensors in such evaluations.

Four interlinked research questions structured the work:

1. What conceptual framework can guide evaluations of SCT impacts on subjective cycling experiences?
2. How can simultaneous flow in duos of cyclists be measured and supported through personalized e-bike motor support?
3. How can pleasantness be processed and analyzed using a mixed-method, sensor-based approach, and how does a speed warning system relate to pleasantness?
4. How does an Intelligent E-bike Motor Control System impact riding dynamics and subjective experiences?

The thesis combines a systematic literature review with three naturalistic field studies conducted within the interdisciplinary Smart Connected Bicycle project. In these field studies, custom-made prototypes of a speed warning system and an intelligent e-bike motor control system were tested. Multimodal data were collected: physiology (e.g., electrodermal activity and heart rhythm dynamics), riding dynamics (speed, cadence, position), warning and motor logs, contextual factors, questionnaires, and in-ride experience sampling via handlebar devices. Data were processed in time windows and analyzed using mixed-effects regression, machine learning (including Random Forest and SHAP values), and Granger causality tests.

The literature and fieldwork of the SCT prototypes led to the following key conclusions:

• SCTs can have positive and negative impacts that are complex, diverse, and both direct and indirect as they are mediated by riding dynamics and context.
• As SCTs communicate multimodally, different signal combinations can lead to different experiential outcomes. Both in-ride multimodal communication and framing of SCTs are crucial factors in impact evaluations.
• Experiences with SCTs are context dependent in complex ways. Traffic hindrance and scenic beauty can buffer or amplify impacts of SCTs.
• The evaluation framework is applicable across multiple technologies, experiential constructs, and methods for data collection and analysis. The framework needs broader validation.
• Mobile sensor data is valuable input for evaluations as it can be linked to subjective cycling experiences with SCTs. Data about riding dynamics and physiology seems valuable for evaluations, but such data is complex to analyze.

The thesis makes several contributions:

• An interdisciplinary framework that guides the use of mobile sensor data in evaluations of the impact of SCTs on subjective cycling experiences.
• New approaches for in-ride sampling of cycling experiences.
• Handlebar devices with simple push buttons are shown to deliver valuable labels for sensor data, facilitating statistical modelling and classification of sensor data.
• Knowledge about links between wearable sensor data and pleasantness and flow as positive aspects of cycling experiences.
• Insights about impact of personalization of motor support level; warnings with audio, visual, and vibrotactile signals; and interventions by an intelligent e-bike motor control system that impact cycling experiences.
• A dataset and scripts for analysis and processing were published, facilitating transparency and replicability. Moreover, processed data for all the studies can be requested from the researcher.

Limitations—small samples, early-stage prototypes, naturalistic variability, and linear modelling constraints—mean the findings are indicative rather than exhaustive.

The implications of the thesis span research, industry, and society. Evaluations of SCTs should explicitly address trade-offs and interaction mechanisms rather than isolated features. Mobile sensor data are valuable but methodologically demanding; more robust causal and non-linear modelling approaches are needed. Designers and policymakers should recognize that SCT impacts are context-dependent and embodied, and that large-scale deployment requires careful, participatory evaluation to ensure societal wellbeing.