Ionut-Marian Dobra

This study presents a low-cost wearable system for monitoring and classifying paddle strokes in open-water environments. Building upon our previous work in controlled aquatic and dryland settings, the proposed system consists of ESP32-based embedded nodes equipped with MPU6050 accelerometer–gyroscope sensors. These nodes communicate via the ESP-NOW protocol in a master–slave architecture. With minimal hardware modifications, the system implements gesture classification using Dynamic Time Warping (DTW) to distinguish between left and right paddle strokes. The collected data, including stroke type, count, and motion similarity, are transmitted in real time to a local interface for visualization. Field experiments were conducted on a calm lake using a paddleboard, where users performed a series of alternating strokes. In addition to gesture recognition, the study includes empirical testing of ESP-NOW communication range in the open lake environment. The results demonstrate reliable wireless communication over distances exceeding 100 m with minimal packet loss, confirming the suitability of ESP-NOW for low-latency data transfer in open-water conditions. The system achieved over 80% accuracy in stroke classification and sustained more than 3 h of operational battery life. This approach demonstrates the feasibility of real-time, wearable-based motion tracking for water sports in natural environments, with potential applications in kayaking, rowing, and aquatic training systems.

Wearable devices are gaining prominence in a variety of applications, including athletic and physical activity monitoring. Their effectiveness often relies on the ability to deliver instantaneous feedback, enabling timely responses and supporting their integration into fast-paced, interactive contexts. Based on these aspects, this paper proposes a wearable system that collects accelerometer and gyroscope data using the MPU6050 sensor from three points on a human arm. The collected data are compared to a predefined set of values using Dynamic Time Warping. Each wearable device provides live feedback via a vibration motor if a stroke deviates from the desired motion. The entire system enables swimming coaches to monitor individual strokes in real time using Wi-Fi connectivity while also allowing athletes to receive immediate feedback when their strokes are executed incorrectly. This system aids in identifying improper swimming techniques and enables faster correction, ultimately improving performance. Experiments will be performed in water environment supervised by a specialized swimming coach.

Swimming is a well-rounded, highly efficient physical activity that provides significant contributions to a healthy lifestyle. Therefore, it is frequently chosen as a form of exercise, even later in life, by many individuals with no prior swimming experience. However, mastering swimming as an adult can be challenging, due to the required time needed to be invested at a swimming facility under the guidance of an instructor. This paper proposes a method of dryland training suitable for swimmers of all levels, with the aid of embedded solutions. The solution is composed of pairs of MPU6050 accelerometer sensors and ESP32 development boards within a multi-device system. These pairs are affixed onto strategic points on the human body to analyze swimming movements performed by the user. The system records the data and generates accuracy assessments based on a reference dataset.

In recent times the escalating pollution within densely populated metropolitan areas has emerged as a significant and pressing concern. Authorities are actively grappling with the challenge of devising solutions to promote a cleaner and more environmentally friendly urban landscapes. This paper outlines the potential of establishing a LoRa node network within a densely populated urban environment. Each LoRa node in this network is equipped with an air quality measurement sensor. This interconnected system efficiently transmits all the analyzed data to a gateway, which subsequently sends it to a server or database in real time. These data are then harnessed to create a pollution map for the corresponding area, providing users with the opportunity to assess local pollution levels and their recent variations. Furthermore, this information proves valuable when determining the optimal route between two points in the city, enabling users to select the path with the lowest pollution levels, thus enhancing the overall quality of the urban environment. This advantage contributes to alleviating congestion and reducing excessive pollution often concentrated behind buildings or on adjacent streets.