Design and Validity of a Smart Healthcare and Control System for Electric Bikes.

This paper presents the development of an electronic system that converts an electrically assisted bicycle into an intelligent health monitoring system, allowing people who are not athletic or who have a history of health issues to progressively start the physical activity by following a medical protocol (e.g., max heart rate and power output, training time). The developed system aims to monitor the health state of the rider, analyze data in real-time, and provide electric assistance, thus diminishing muscular exertion. Furthermore, such a system can recover the same physiological data used in medical centers and program it into the e-bike to track the patient's health. System validation is conducted by replicating a standard medical protocol used in physiotherapy centers and hospitals, typically conducted in indoor conditions. However, the presented work differentiates itself by implementing this protocol in outdoor environments, which is impossible with the equipment used in medical centers. The experimental results show that the developed electronic prototypes and the algorithm effectively monitored the subject's physiological condition. Moreover, when necessary, the system can change the training load and help the subject remain in their prescribed cardiac zone. This system allows whoever needs to follow a rehabilitation program to do so not only in their physician's office, but whenever they want, including while commuting.

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.

Toward Smart Soil Sensing in v4.0 Agriculture: A New Single-Shape Sensor for Capacitive Moisture and Salinity Measurements.

Modern agriculture imposes the need for better knowledge of the soil moisture content to rationalize the amount of water needed to irrigate farmlands. In this context, since current technological solutions do not correspond to the cost or use criteria, this paper presents a design for a new original capacitive bi-functional sensor to measure soil moisture and salinity. In this paper, we outline the design stages from simulation to finished elements of the optimal design to deployment in the fields, considering the mechanical integration constraints necessary for industrialization. The measurement electronics were developed based on the sensor's electric model to obtain a double measurement. An on-site (field lot) measurement program was then carried out to validate the system's good performance in real-time. Finally, this performance was matched with that of leading commercially available sensors on the market. This work demonstrates that, after deployment of the sensors, the overall system makes it possible to obtain a precise image of cultivated soil's hydric condition, with the best response time.

A Study about Kalman Filters Applied to Embedded Sensors.

Over the last decade, smart sensors have grown in complexity and can now handle multiple measurement sources. This work establishes a methodology to achieve better estimates of physical values by processing raw measurements within a sensor using multi-physical models and Kalman filters for data fusion. A driving constraint being production cost and power consumption, this methodology focuses on algorithmic complexity while meeting real-time constraints and improving both precision and reliability despite low power processors limitations. Consequently, processing time available for other tasks is maximized. The known problem of estimating a 2D orientation using an inertial measurement unit with automatic gyroscope bias compensation will be used to illustrate the proposed methodology applied to a low power STM32L053 microcontroller. This application shows promising results with a processing time of 1.18 ms at 32 MHz with a 3.8% CPU usage due to the computation at a 26 Hz measurement and estimation rate.