Anton Umek

Suitability of Smartphone Inertial Sensors for Real-Time Biofeedback Applications

This article studies the suitability of smartphones with built-in inertial sensors for biofeedback applications. Biofeedback systems use various sensors to measure body functions and parameters. These sensor data are analyzed, and the results are communicated back to the user, who then tries to act on the feedback signals. Smartphone inertial sensors can be used to capture body movements in biomechanical biofeedback systems. These sensors exhibit various inaccuracies that induce significant angular and positional errors. We studied deterministic and random errors of smartphone accelerometers and gyroscopes, primarily focusing on their biases. Based on extensive measurements, we determined accelerometer and gyroscope noise models and bias variation ranges. Then, we compiled a table of predicted positional and angular errors under various biofeedback system operation conditions. We suggest several bias compensation options that are suitable for various examples of use in real-time biofeedback applications. Measurements within the developed experimental biofeedback application show that under certain conditions, even uncompensated sensors can be used for real-time biofeedback. For general use, especially for more demanding biofeedback applications, sensor biases should be compensated. We are convinced that real-time biofeedback systems based on smartphone inertial sensors are applicable to many similar examples in sports, healthcare, and other areas.

Evaluation of Smartphone Inertial Sensor Performance for Cross-Platform Mobile Applications.

Smartphone sensors are being increasingly used in mobile applications. The performance of sensors varies considerably among different smartphone models and the development of a cross-platform mobile application might be a very complex and demanding task. A publicly accessible resource containing real-life-situation smartphone sensor parameters could be of great help for cross-platform developers. To address this issue we have designed and implemented a pilot participatory sensing application for measuring, gathering, and analyzing smartphone sensor parameters. We start with smartphone accelerometer and gyroscope bias and noise parameters. The application database presently includes sensor parameters of more than 60 different smartphone models of different platforms. It is a modest, but important start, offering information on several statistical parameters of the measured smartphone sensors and insights into their performance. The next step, a large-scale cloud-based version of the application, is already planned. The large database of smartphone sensor parameters may prove particularly useful for cross-platform developers. It may also be interesting for individual participants who would be able to check-up and compare their smartphone sensors against a large number of similar or identical models.

Wearable training system with real-time biofeedback and gesture user interface

Wearable computing and sensors are becoming increasingly prevalent in our daily lives. This paper presents a wearable training system designed to facilitate the learning process of proper movement patterns in sports training. The system implements a gesture user interface and real-time biofeedback. The feedback loop consists of one or more body-attached motion sensors, a processing device and a biofeedback device that are interconnected through low-latency communication channels. Due to the diverse number of possible applications, a flexible system architecture, which includes several different system versions, is proposed. Operation of the system is driven by user gestures. To demonstrate the concept of the proposed real-time biofeedback training system, an application for golf swing training is developed. The application implements the system using smartphone motion sensors and audio biofeedback and aids golfers in correcting unwanted head movements during a golf swing. The application is driven by a gesture user interface. During the golf swing, the application provides users with real-time audio feedback that signals head movement errors. The field test results show that the developed application can be used as an efficient tool in golf swing training.

Spatial sound generation using HRTF created by the use of recursive filters

Generating spatial sound and playing it through headphones is a demanding task, since two important factors, ILD - inter-aural level difference and ITD - inter-aural time difference, need to be taken into consideration. The problem can be solved by the use of head related transfer functions (HRTF) which represent a set of empirically measured functions, one for each spatial direction. The complete reconstruction of HRTF is possible through the use of finite impulse response (FIR) filters with 512 coefficients each. Since the spectrum of HRTF consists of distinctive maximums and minimums, the spectrum could be approximated by the use of resonators and notch filters. The approximation of the complete spectrum (20 Hz - 20 kHz) could be done by the use of six resonators and one notch filter. Our approach to spatial sound generation using HRTF created by the use of recursive (IIR) filters presents a practical and computationally effective solution. It also indicates a way to uniformly model all factors connected to spatial sound perception.

Validation of smartphone gyroscopes for mobile biofeedback applications

Smartphones are currently the most pervasive wearable devices. One particular use of smartphone inertial sensors is motion tracking in various mobile systems and applications. The objective of this study is to validate smartphone gyroscopes for angular tracking in mobile biofeedback applications. The validation method includes measurements of angular motion performed concurrently by a smartphone gyroscope and a professional optical tracking system serving as the reference. The comparison of the measurement results shows that the inaccuracies of a calibrated smartphone gyroscope for various movements are between 0.42° and 1.15°. Based on the measurement results and the general requirements of biofeedback applications, smartphone gyroscopes are sufficiently accurate for angular motion tracking in mobile biofeedback applications.

The Role of High Performance Computing and Communication for Real-Time Biofeedback in Sport

This paper studies the main technological challenges of real-time biofeedback in sport. We identified communication and processing as two main possible obstacles for high performance real-time biofeedback systems. We give special attention to the role of high performance computing with some details on possible usage of DataFlow computing paradigm. Motion tracking systems, in connection with the biomechanical biofeedback, help in accelerating motor learning. Requirements about various parameters important in real-time biofeedback applications are discussed. Inertial sensor tracking system accuracy is tested in comparison with a high performance optical tracking system. Special focus is given on feedback loop delays. Real-time sensor signal acquisitions and real-time processing challenges, in connection with biomechanical biofeedback, are presented. Despite the fact that local processing requires less energy consumption than remote processing, many other limitations, most often the insufficient local processing power, can lead to distributed system as the only possible option. A multiuser signal processing in football match is recognised as an example for high performance application that needs high-speed communication and high performance remote computing. DataFlow computing is found as a good choice for real-time biofeedback systems with large data streams.

Multi-sensor Golf Swing Classification Using Deep CNN

Abstract In recent years smart sport equipments have achieved great success in professional and amateur sports, as well as body sensory systems; now discovering interesting knowledge in the surge of data from those embedded sensors used in sports is necessary and the focus of our research. In this paper, we investigate golf swing data classification method based on deep convolutional neural network (deep CNN) fed with multi-sensor golf swing signals. Our smart golf club integrates two orthogonally affixed strain gage sensors, 3-axis accelerometer and 3-axis gyroscope, and collects real-world golf swing data from professional and amateur golf players. Furthermore we explore the performance of our well-trained CNN-based classifier and evaluate it on the real-world test set in terms of common indicators including accuracy, precision-recall, and F1-score. Experiments and corresponding results show that our CNN-based model can satisfy the requirement of accuracy of golf swing classification, and outperforms support vector machine (SVM) method.

Suitability of Strain Gage Sensors for Integration into Smart Sport Equipment: A Golf Club Example

Wearable devices and smart sport equipment are being increasingly used in amateur and professional sports. Smart sport equipment employs various sensors for detecting its state and actions. The correct choice of the most appropriate sensor(s) is of paramount importance for efficient and successful operation of sport equipment. When integrated into the sport equipment, ideal sensors are unobstructive, and do not change the functionality of the equipment. The article focuses on experiments for identification and selection of sensors that are suitable for the integration into a golf club with the final goal of their use in real time biofeedback applications. We tested two orthogonally affixed strain gage (SG) sensors, a 3-axis accelerometer, and a 3-axis gyroscope. The strain gage sensors are calibrated and validated in the laboratory environment by a highly accurate Qualisys Track Manager (QTM) optical tracking system. Field test results show that different types of golf swing and improper movement in early phases of golf swing can be detected with strain gage sensors attached to the shaft of the golf club. Thus they are suitable for biofeedback applications to help golfers to learn repetitive golf swings. It is suggested that the use of strain gage sensors can improve the golf swing technical error detection accuracy and that strain gage sensors alone are enough for basic golf swing analysis. Our final goal is to be able to acquire and analyze as many parameters of a smart golf club in real time during the entire duration of the swing. This would give us the ability to design mobile and cloud biofeedback applications with terminal or concurrent feedback that will enable us to speed-up motor skill learning in golf.

Challenges in wireless communication for connected sensors and wearable devices used in sport biofeedback applications

Abstract Sensors, wearables, wireless networks, and other Internet of Things technologies are ever more present in our daily life. We study their applicability and use in biofeedback systems and applications in sport. Biofeedback systems are important in motor learning where a person in the loop uses the feedback information to influence the execution performance. Sensors, actuators, and wireless technologies come in great varieties regarding their properties. We describe the most common groups of sensors and actuators that are used in sport and list the most widespread and easily available wireless technologies. We present the most important constraints of a biofeedback system operation and define a number of fundamental architectures of biofeedback systems. Taking into account all of the above, we present a number of different biofeedback application scenarios in sports. We match the scenarios to the most appropriate existing wireless technology that is expected to sustain scalability in the number of nodes or increased data rates for the expected application lifetime. We find out that currently none of the existing wireless technologies can satisfy the variety of demands of different biofeedback application scenarios.

Autonomous Wearable Personal Training System with Real-Time Biofeedback and Gesture User Interface

In this paper we present an autonomous wearable personal training system, which includes a gesture user interface and real-time biofeedback. The system consists of a wearable processing device and one or more body-attached inertial sensors with 3-axis accelerometer and 3-axis gyroscope. In our experiments we employ inertial sensors of a smartphone attached to the body. The system processes sensor data and provides users with real-time audio feedback. The operation of the system is driven by user gestures. Gestures are defined users body movements that are detected through their characteristic inertial sensor responses. As an example of such a system, we have designed the application for golf swing error detection and correction. The application helps golfers correct the unwanted head movements during the golf swing. The gesture-driven user interface is able to detect the beginning and the end of the swing, swing phases, and the data recording interval. The application guides users to assume the correct setup position. During the golf swing, it provides them with the real-time audio feedback, signalling head movement errors. After the swing, the application indicates head movement errors and draws 3D head movement diagrams. Field test results show that our system is an efficient tool for the detection and correction of head movement errors during the golf swing.