Increasing the Intensity over Time of an Electric-Assist Bike Based on the User and Route: The Bike Becomes the Gym

Enhancing Urban Mobility with Self-Tuning Fuzzy Logic Controllers for Power-Assisted Bicycles in Smart Cities

In smart cities, bicycle-sharing systems have become an essential component of the transportation services available in major urban centers around the globe. Due to environmental sustainability, research on the power-assisted control of electric bikes has attracted much attention. Recently, fuzzy logic controllers (FLCs) have been successfully applied to such systems. However, most existing FLC approaches have a fixed fuzzy rule base and cannot adapt to environmental changes, such as different riders and roads. In this paper, a modified FLC, named self-tuning FLC (STFLC), is proposed for power-assisted bicycles. In addition to a typical FLC, the presented scheme adds a rule-tuning module to dynamically adjust the rule base during fuzzy inference processes. Simulation and experimental results indicate that the presented self-tuning module leads to comfortable and safe riding as compared with other approaches. The technique established in this paper is thought to have the potential for broader application in public bicycle-sharing systems utilized by a diverse range of riders.

Torque Measurement and Control for Electric-Assisted Bike Considering Different External Load Conditions

This paper proposes a novel torque measurement and control technique for cycling-assisted electric bikes (E-bikes) considering various external load conditions. For assisted E-bikes, the electromagnetic torque from the permanent magnet (PM) motor can be controlled to reduce the pedaling torque generated by the human rider. However, the overall cycling torque is affected by external loads, including the cyclist's weight, wind resistance, rolling resistance, and the road slope. With knowledge of these external loads, the motor torque can be adaptively controlled for these riding conditions. In this paper, key E-bike riding parameters are analyzed to find a suitable assisted motor torque. Four different motor torque control methods are proposed to improve the E-bike's dynamic response with minimal variation in acceleration. It is concluded that the wheel acceleration is important to determine the E-bike's synergetic torque performance. A comprehensive E-bike simulation environment is developed with MATLAB/Simulink to evaluate these adaptive torque control methods. In this paper, an integrated E-bike sensor hardware system is built to verify the proposed adaptive torque control.

Health and well-being benefits of e-bike commuting for inactive, overweight people living in regional Australia

INTRODUCTION

Pedal-assisted electric-bikes (e-bikes) are bicycles fitted with electric motors. Motorised functions on e-bikes only operate when the user pedals, allowing riders a moderate amount of physical activity. This study aimed to explore the mental and physical health and well-being impacts related to ebike usage for inactive overweight or obese individuals living in regional Australia.

METHODS

Twenty inactive, overweight/obese people who seldom cycled were provided with an ebike over a 12-week period. Individual semi-structured interviews conducted at the end of the trial generated data about participants' experiences of using ebikes. Inductive thematic analysis of interview data using Thomas (2006) data analysis framework and NVivo 12 software was undertaken.

RESULTS

Data analysis revealed that e-cycling improved participants' mental and physical well-being and that they felt happier when riding an e-bike.

CONCLUSIONS

Riding an e-bike can improve mental and physical health, happiness and overall sense of well-being. Greater uptake of e-bikes would have positive health implications for the wider community. Results from this study can be used to inform active transport policy. SO WHAT?: Our study demonstrated that encouraging active transport in the form of e-cycling can improve the overall health and well-being of overweight and obese Australians. More specifically, e-cycling demonstrated a positive impact on mental health well-being.

The Factors Influencing Resident’s Intentions on E-Bike Sharing Usage in China

E-bike sharing is considered a new mode of transport that is rapidly developing in China. In order to better understand the factors affecting the intention to use e-bike sharing, this study is based on the theory of planned behavior (TPB) and the technology acceptance model (TAM) and added the variable of policy support. A sample of 441 respondents in a small city in China was collected to analyze residents’ intention on e-bike sharing usage. The results show that the research model can explain well residents’ intention to use shared e-bikes. Perceived ease of use, perceived usefulness, attitude, subjective norms, and perceived behavioral control have direct positive effects on the intention to use shared e-bikes. Among them, the perceived ease of use has the greatest impact on the intention to use shared e-bikes. Moreover, policy support has an indirect positive influence on the intention to use shared e-bikes through partial mediation of attitude and subjective norms. Finally, some strategies to promote e-bike sharing are proposed. This study can provide a better understanding of the acceptance of e-bike sharing and the strategy for promoting e-bike sharing in urban transportation.

Smart Electrically Assisted Bicycles as Health Monitoring Systems: A Review

This paper aims to provide a review of the electrically assisted bicycles (also known as e-bikes) used for recovery of the rider's physical and physiological information, monitoring of their health state, and adjusting the "medical" assistance accordingly. E-bikes have proven to be an excellent way to do physical activity while commuting, thus improving the user's health and reducing air pollutant emissions. Such devices can also be seen as the first step to help unhealthy sedentary people to start exercising with reduced strain. Based on this analysis, the need to have e-bikes with artificial intelligence (AI) systems that recover and processe a large amount of data is discussed in depth. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used to complete the relevant papers' search and selection in this systematic review.

Smart Sensing Technologies for Personalised e-Coaching

People living in both developed and developing countries face serious health challenges related to sedentary lifestyles. It is therefore essential to find new ways to improve health so that people can live longer and age well. With an ever-growing number of smart sensing systems developed and deployed across the globe, experts are primed to help coach people to have healthier behaviors. The increasing accountability associated with app- and device-based behavior tracking not only provides timely and personalized information and support, but also gives us an incentive to set goals and do more. This paper outlines some of the recent efforts made towards automatic and autonomous identification and coaching of troublesome behaviors to procure lasting, beneficial behavioral changes.