This paper presents work related to tennis stroke detection and classification. For arm movement acquisition a miniature wearable IMU device, positioned on the player's forearm (right above the wrist) is proposed and presented. The device uses a MEMS-based accelerometer and gyroscope with 6-DOF. For reliable and accurate tennis stroke detection the information obtained from the accelerometer data is used, and for tennis stroke classification, information from gyroscope data is extracted and processed. The proposed system is able to detect and classify three most common tennis strokes: forehand, backhand, and serve. Because of limited memory and lack of processing power, the proposed algorithms for stroke detection and classification are quite simple, but are nonetheless capable of achieving high classification rate. Overall 98.1% tennis stroke classification accuracy was achieved.

This paper presents the work related to the identification of PRV (pulse rate variability) and SpO2 (blood oxidation content) using miniature wearable wrist device. The extension of currently widely used PPT (photo plethysmography) measuring technique is proposed. Most PPT devices only measure PR (pulse rate), but with minimum adaptations to the sensor, a wider range of parameters can be measured. Two of such parameters are PRV and SpO2. This set of parameters gives a better insight into ones physical and mental state than only PR parameter. In order to develop a device that does not obstruct the user in any way, we propose a miniature non-invasive wearable device, and algorithms that do not need any significant processing power in order to identify the biometric parameters. The device is a lightweight battery powered embedded system, that measures and analyses biometric data for later analysis in correlation with one's activity.

The purpose of this article is to present the use of a previously validated wearable sensor device, Armbeep, in a real-life application, to enhance a tennis player’s training by monitoring and analysis of the time, physiological, movement, and tennis-specific workload and recovery indicators, based on fused sensor data acquired by the wearable sensor—a miniature wearable sensor device, designed to be worn on a wrist, that can detect and record movement and biometric information, where the basic signal processing is performed directly on the device, while the more complex signal analysis is performed in the cloud. The inertial measurements and pulse-rate detection of the wearable device were validated previously, showing acceptability for monitoring workload and recovery during tennis practice and matches. This study is one of the first attempts to monitor the daily workload and recovery of tennis players under real conditions. Based on these data, we can instruct the coach and the player to adjust the daily workload. This optimizes the level of an athlete’s training load, increases the effectiveness of training, enables an individual approach, and reduces the possibility of overuse or injuries. This study is a practical example of the use of modern technology in the return of injured athletes to normal training and competition. This information will help tennis coaches and players to objectify their workloads during training and competitions, as this is usually only an intuitive assessment.

Based on the fact that there are many challenging cases of infirm persons, who are able to control only their eye muscles, a low-cost mobile device for electronic eye gesture recognition has been designed as a human-machine interface, which enables the control of different applications and home appliances by user's eye gestures by the IR and Bluetooth wireless technology. In this paper the embedded system design of the device is presented in detail, including hardware and software design, modes of operation, and used methods for the eye gesture recognition. Beside these, measurements of the used differential amplifier and the achieved eye gesture recognition efficiency are presented within the test results. Furthermore, a newly designed adjustable head mounted EOG acquisition device with the permanent surface electrodes is proposed.

This paper describes the implementation of a reliable maximum power point tracking design as an analog circuit for use on a satellite relying on solar power generation within low Earth orbit. The environment in which such a spacecraft system would function is evaluated. A specific maximum power point tracking algorithm is selected with regards to environmental constraints. A further increase in reliability was achieved by only using carefully selected analog components that are suitable for use within a space environment. The system was prototyped, and its power conversion performance was characterized using a custom-built measurement setup. The system successfully tracked the maximum power point through all the performed measurements. The results from the measurements suggest that the prototyped system has a nominal regulator efficiency of 88% and a nominal tracking accuracy of 96%.

This paper presents research made on the topic of how consistent a tennis player is when hitting a stroke. The analysis is made preliminarily only for the most common tennis stroke, which is forehand. The analysis results can be very useful for the tennis coaches and other experts on the evaluation of a tennis player's current shape and performance stability. For swing movement acquisition a miniature wearable device is used. It is positioned on a player's forearm above the wrist and uses accelerometer and gyroscope to record acceleration and angular velocity in all three axes. A wearable device is a part of the system-in-a-cloud service where complex and more detailed game/performance analyses can be performed.