Numerous studies have established that using various forms of augmented feedback improves human motor learning. In this paper, we present a system that enables real-time analysis of motion patterns and provides users with objective information on their performance of an executed set of motions. This information can be used to identify individual segments of improper motion early in the learning process, thus preventing improperly learned motion patterns that can be difficult to correct once fully learned. The primary purpose of the proposed system is to serve as a general tool in the research on the impact of different feedback modalities on the process of motor learning, for example, in sports or rehabilitation. The key advantages of the system are high-speed and high-accuracy tracking, as well as its flexibility, as it supports various types of feedback (auditory and visual, concurrent or terminal). The practical application of the proposed system is demonstrated through the example of learning a golf swing.
COBISS.SI-ID: 11471188
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. 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.
COBISS.SI-ID: 11111764
This paper presents an analysis of a golf swing to detect improper motion in the early phase of the swing. Led by the desire to achieve a consistent shot outcome, a particular golfer would (in multiple trials) prefer to perform completely identical golf swings. In reality, some deviations from the desired motion are always present. Swing motion deviations that are not detrimental to performance are acceptable. This analysis is conducted using a golfer’s leading arm kinematic data, which are obtained from a golfer wearing a motion sensor that is comprised of gyroscopes and accelerometers. Applying the principal component analysis (PCA) to the reference observations of properly performed swings, the PCA components of acceptable swing motion deviations are established. Using these components, the motion deviations in the observations of other swings are detected in the early phase of a golf swing, i.e., in the backswing. An early detection method for improper swing motions that is conducted on an individual basis provides assistance for performance improvement.
COBISS.SI-ID: 9904212
This paper presents the results of the evaluation of the performance of the Leap Motion controller with the aid of the professional, high-precision, and fast-speed motion tracking system. A set of static, as well as dynamic, measurements with a different number of tracking objects and configurations, was performed. The linear correlation revealed a significant increase of standard deviation when moving away from the controller. The results of the dynamic scenario revealed the inconsistent performance of the controller, with a significant drop of accuracy for samples taken higher than 250 mm above the controller's surface. Due to a rather limited sensory space and inconsistent sampling frequency, it currently cannot be used as a professional tracking system.
COBISS.SI-ID: 10430036
Skeletal muscle is the largest tissue structure in our body and plays an essential role for producing motion through integrated action with bones, tendons, ligaments and joints, for stabilizing body position, for generation of heat through cell respiration and for blood glucose disposal. A key function of skeletal muscle is force generation. Non-invasive and selective measurement of muscle contraction force in the field and in clinical settings has always been challenging. The aim of our work has been to develop a sensor that can overcome these difficulties and therefore enable measurement of muscle force during different contraction conditions. In this study, we tested the mechanical properties of a "Muscle Contraction" (MC) sensor during isometric muscle contraction in different length/tension conditions. The MC sensor is attached so that it indents the skin overlying a muscle group and detects varying degrees of tension during muscular contraction. We compared MC sensor readings over the biceps brachii (BB) muscle to dynamometric measurements of the force of elbow flexion, together with recordings of surface EMG signal of BB during isometric contractions at 15° and 90° of elbow flexion. Statistical correlation between MC signal and force was very high at 15° (r = 0.976) and 90° (r = 0.966) across the complete time domain. Normalized SD was used as a measure of linearity of MC signal and elbow flexion force in dynamic conditions. The average was 8.24% for an elbow angle of 90° and 10.01% for an elbow of angle 15°, which indicates high linearity and good dynamic properties of MC sensor signal when compared to elbow flexion force. The next step of testing MC sensor potential will be to measure the tension of muscle-tendon complex in conditions when length and tension change simultaneously during human motion.
COBISS.SI-ID: 10778452